GigaOm Radar for Service Meshv4.0

1. Executive Summary

Service mesh technology has emerged as an essential infrastructure layer for managing communication between microservices, which are the building blocks of modern, scalable applications deployed in cloud environments. As organizations transition from monolithic to microservices architectures, the complexity of managing service-to-service interactions increases significantly. A service mesh addresses this complexity by providing a transparent and consistent way to secure, connect, and observe microservices without requiring changes to the application code.

Purpose of a Service Mesh
The primary purpose of a service mesh is to facilitate and manage the communication among dozens or even hundreds of microservices that make up a distributed application architecture. Without a service mesh, managing these interactions can become overwhelmingly complex. Key functionalities of a service mesh include:

• Traffic management: It controls the way requests are routed and managed between services, supporting capabilities like load balancing, canary releases, and A/B testing. This allows for fine-grained control over traffic, which is crucial for optimizing application performance and rolling out updates safely.
• Security: It provides robust security features, such as mutual transport layer security (mTLS) for encrypted communications, fine-grained access control policies, and authentication and authorization mechanisms. These features help ensure that communications between services are secure from unauthorized access and potential threats.
• Observability: It offers rich observability features, including logging, monitoring, and tracing of requests that flow through the mesh. This visibility is critical for diagnosing and troubleshooting issues, understanding system performance, and ensuring that the application runs smoothly and efficiently.

Why Adopting a Service Mesh Is Important
The importance of a service mesh stems from its ability to abstract the complexity of inter-service communications into a dedicated infrastructure layer. This abstraction allows developers to focus on building business logic rather than dealing with the intricacies of network and security policies. As applications grow and the number of services increases, a service mesh becomes crucial for maintaining control and visibility over these complex interactions. Implementing a service mesh ensures that applications remain robust, secure, and easy to manage, regardless of scale.

Open source projects and commercial vendors target a wide range of application environments and deployment options. Figure 1 lists the open source and commercial service meshes that are included in this report, along with their acquisition options.

Figure 1. Service Mesh Projects and Vendors

Note: The Cloud Native Computing Foundation (CNCF) provides governance for open source, vendor-neutral cloud-native projects. It hosts several community-driven open source projects with varying maturity levels: sandbox (early stage), incubating (stable), or graduated (widely deployed in production environments).

How the Service Mesh Landscape is Evolving
The concept of a service mesh has evolved significantly since its inception. Early iterations were primarily focused on providing basic service discovery and load balancing. However, as cloud-native technologies like containers and orchestration tools like Kubernetes became mainstream, the role of service meshes has expanded to include more sophisticated traffic management, enhanced security, and comprehensive observability.

• Improved usability: Initially, service meshes were complex to configure and manage. Recent developments have focused on enhancing the user experience with more intuitive interfaces, automated setup processes, integration with existing cloud-native ecosystems, and better documentation. These features lower the barrier to entry, making service meshes accessible to a broader range of users.

• Performance optimization: Modern service meshes are being designed to minimize latency and resource overhead. Innovations such as sidecarless deployments and the integration of cutting-edge technologies like ambient mesh and eBPF have the potential to reduce latency and resource consumption, minimizing the performance impact traditionally associated with service meshes.

• Standardization and interoperability: As the service mesh landscape grows, there has been a push towards standardization to ensure compatibility and interoperability between different service mesh technologies. Initiatives like the Kubernetes Gateway API aim to create a standard set of APIs, making it easier for users to switch between service meshes or use multiple meshes within the same environment.

• Expansion into new areas: Service meshes are now beginning to extend beyond traditional cloud and microservices environments into areas like serverless computing and edge computing. This expansion opens up new use cases and opportunities for innovation.

In conclusion, a service mesh simplifies the management of complex service interactions and enhances application security and observability. As the technology continues to evolve, it will play an increasingly critical role in enabling organizations to scale, secure, and optimize their applications efficiently.

This is our fourth year evaluating the service mesh space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 16 of the top service mesh solutions and compares each solution against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading service mesh offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

2. Market Categories and Deployment Types

To help prospective customers find the best fit for their use case and business requirements, we assess how well an open source or vendor service mesh is designed to serve specific target markets and deployment models (Table 1).

For this report, we recognize the following market segments:

• Cloud service provider (CSP): Providers that deliver on-demand, pay-per-use services to customers over the internet, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
• Network service provider (NSP): Service providers selling network services—such as network access and bandwidth—provide entry points to backbone infrastructure or network access points (NAPs). In this report, NSPs include data carriers, ISPs, telcos, and wireless providers.
• Managed service provider (MSP): Service providers delivering managed application, communication, IT infrastructure, network, and security services and support for businesses at either the customer premises or via MSP (hosting) or third-party data centers (colocation).
• Large enterprise: Enterprises of 1,000 or more employees with dedicated IT teams responsible for planning, building, deploying, and managing their applications, IT infrastructure, networks, and security in either an on-premises data center or a colocation facility.
• Small-to-medium business (SMB): Small businesses (fewer than 100 employees) and medium-size businesses (100-1,000 employees) with limited budgets and constrained in-house resources for planning, building, deploying, and managing their applications, IT infrastructure, networks, and security in either an on-premises data center or a colocation facility.

In addition, we recognize the following deployment models:

• Single or multiple cluster: Service meshes can be configured as either a single cluster or a single mesh, including multiple clusters. A single cluster deployment may offer simplicity, but it lacks features such as fault isolation, failover, and project isolation that are available in a multicluster deployment.
• Single or multiple network: Workload instances directly connected without using a gateway reside in a single network, enabling the uniform configuration of service consumers across the mesh. A multinetwork approach allows a service mesh to span various network topologies or subnets, providing compliance, isolation, high availability, and scalability.
• Single or multiple control plane: The control plane configures all communication between workload instances within the mesh. Deploying multiple control planes across clusters, regions, or zones provides configuration isolation, fine-grained control over configuration rollouts, and service-level isolation. If one control plane becomes unavailable, the impact of the outage is limited to the workloads managed by that control plane.
• Single or multiple mesh: While a single mesh can span one or more clusters or networks, service names are unique within the mesh. Since namespaces are used for tenancy, a federated mesh is required to discover services and communicate across mesh boundaries. Each mesh reveals services that can be consumed by other services, providing line-of-business boundaries and isolation between test and production workloads.

Table 1. Vendor Positioning: Target Market and Deployment Model

Table 1 components are evaluated in a binary yes/no manner and do not factor into a vendor’s designation as a Leader, Challenger, or Entrant on the Radar chart (Figure 1).

“Target market” reflects which use cases each solution is recommended for, not simply whether that group can use it. For example, if an SMB could use a solution but doing so would be cost-prohibitive, that solution would be rated “no” for SMBs.

3. Decision Criteria Comparison

All solutions included in this Radar report meet the following table stakes—capabilities widely adopted and well implemented in the sector:

• Dedicated infrastructure layer
• Service-to-service authentication
• Centralized control plane
• Control plane telemetry
• Built-in resilience

Tables 2, 3, and 4 summarize how each vendor in this research performs in the areas we consider differentiating and critical in this sector. The objective is to give the reader a snapshot of the technical capabilities of available solutions, define the perimeter of the relevant market space, and gauge the potential impact on the business.

• Key features differentiate solutions, highlighting the primary criteria to be considered when evaluating a service mesh solution.

• Emerging features show how well each vendor implements capabilities that are not yet mainstream but are expected to become more widespread and compelling within the next 12 to 18 months.

• Business criteria provide insight into the nonfunctional requirements that factor into a purchase decision and determine a solution’s impact on an organization.

These decision criteria are summarized below. The corresponding report, “GigaOm Key Criteria for Evaluating Service Mesh Solutions,” provides more detailed descriptions.

Key Features

• Hybrid platform support: A service mesh with hybrid platform support enables seamless operation across diverse infrastructures, including containers, Kubernetes, virtual machines, and traditional service registries. This flexibility is crucial because organizations often have heterogeneous environments and need a unified way to manage service communication, security, and observability.
• Sidecar implementation: A sidecar implementation involves attaching a service proxy to a workload during deployment to manage inter-service communications within a service mesh. Promoting modularity and separation, the sidecar model allows developers to focus solely on the business logic of their services because common functionality is decoupled and managed by the sidecar.
• Sidecarless implementation: A sidecarless service mesh architecture eliminates the need for individual sidecar proxies attached to each microservice. Leveraging a feature of Linux that allows applications to do certain types of work within the kernel itself, a sidecarless architecture moves the proxy function from the sidecar to the node level, adding Layers 3 and 4 acceleration and observability at the kernel level.
• Automated service discovery: Automated service discovery dynamically detects and registers new service instances as they are added or removed, keeping the view of the system’s topology up to date and allowing for accurate routing and load distribution among services. This automation eliminates the need for manual configuration, enhancing efficiency and reducing potential errors.
• Load balancing: Rich Layer 7 (application layer) load balancing is implemented with support for blue-green, canary, and rolling deployments. After applying dynamic routing rules to determine the intended destination, the service mesh determines the best possible communications path based on the route’s recent latency history. If an instance consistently returns errors, the mesh removes it from the load balancing pool, adding it back only after positive retries.
• Traffic management: Traffic management in a service mesh dynamically controls and optimizes the flow of traffic among services using advanced features like routing rules and traffic splitting. This functionality is crucial for enhancing application performance, gradually rolling out changes in a controlled manner, and ensuring robust, scalable service delivery.
• Policy and configuration enforcement: Policy and configuration enforcement in a service mesh involves consistently applying predefined rules and settings across all services, ensuring adherence to organizational standards and security protocols. This enforcement is essential for maintaining compliance and safeguarding the integrity of service communications.
• Encryption and security: The service mesh encrypts and decrypts requests and responses, prioritizing the reuse of existing, persistent connections to optimize performance. While most meshes provide mutual TLS (mTLS) encryption for all service-to-service communications, some do not yet have encryption capabilities.

Table 2. Key Features Comparison

Emerging Features

• eBPF support: eBPF (extended Berkeley Packet Filter) enables the safe execution of user-defined programs within the Linux kernel, enhancing system observability, security, and networking capabilities. Its importance lies in its ability to provide granular visibility and control over system operations without modifying kernel code, making it a critical tool for modern cloud-native applications.
• Ambient mesh support: Ambient mesh is a new sidecar-less architecture for Istio that simplifies service mesh operations by using node-level ztunnels for Layer 4 traffic and waypoint proxies for Layer 7 traffic. This approach reduces complexity, improves performance, and lowers infrastructure costs while maintaining security and observability.
• Type-safe language support: Type-safe language support ensures that operations on data are restricted to those permitted by the data type, preventing type errors and undefined behavior. This is crucial for enhancing code reliability, security, and maintainability by catching errors early–often at compile time–and reducing runtime bugs.
• Transparent tunnels: In a service mesh, transparent tunnels provide secure, seamless, and protocol-agnostic communication channels between services. They enable encrypted and authenticated data transfer without requiring modifications to the applications, simplifying the implementation of secure communication in complex microservices environments.
• 5G/edge integration: Integrating 5G network technology and edge computing principles into service mesh architecture enables low-latency, high-bandwidth, and decentralized computing for microservices-based applications at the network edge. This integration is crucial for supporting the growing demand for real-time, data-intensive applications and services at the edge across various industries.
• Service mesh as a service (SMaaS): SMaaS offers a subscription-based model for managing complex, distributed service meshes. By outsourcing the configuration, integration, and management of service meshes to vendors, organizations can focus on their core applications and services while benefiting from the advanced features and expertise provided by SMaaS providers.

Table 3. Emerging Features Comparison

Business Criteria

• Configurability: Configurability in a service mesh enables organizations to customize and fine-tune various aspects of the mesh to meet specific application requirements and optimize performance. This flexibility is crucial for adapting the service mesh to the unique needs of each organization, ensuring that it aligns with its business goals and technical constraints.
• Interoperability: Interoperability enables seamless communication between microservices running on different platforms and environments. Interoperability is a critical factor for organizations with heterogeneous infrastructures, allowing them to leverage the benefits of the technology without being locked into a single service mesh implementation.
• Manageability: Manageability in a service mesh refers to the ease and efficiency of deploying, configuring, monitoring, and maintaining the mesh and its associated services. A manageable service mesh, typically equipped with a centralized control plane and user-friendly interfaces, is essential for streamlining operations and reducing the complexity of managing microservices-based applications.
• Observability: Observability in a service mesh provides deep visibility into the behavior, performance, and health of services within the mesh. By collecting and analyzing comprehensive telemetry data, such as metrics, distributed traces, and access logs, a service mesh with strong observability features can lead to increased customer satisfaction, higher retention rates, and better business outcomes.
• Performance: Performance in a service mesh refers to its ability to efficiently handle service-to-service communication while minimizing latency and optimizing resource consumption. Ensuring high-throughput, responsive, and scalable application performance is crucial for delivering a seamless user experience and meeting the demands of modern microservices-based applications.
• Resiliency: Resiliency in a service mesh refers to its ability to maintain stable and reliable service-to-service communication in the face of failures, such as service outages, network issues, or unexpected traffic spikes. By automatically adapting to changing conditions and mitigating the effects of failures, a resilient service mesh enables organizations to deliver on their service level agreements (SLAs) and meet customer expectations.
• Support: Support for a service mesh refers to the vendor or project maintainers’ ability to help organizations minimize downtime, reduce operational risks, and ensure the long-term success of their microservices-based applications. Given the critical role of a service mesh in an application’s infrastructure, reliable and responsive support is essential for ensuring the smooth operation and long-term viability of the deployment.
• Cost: The cost of a service mesh includes expenses related to software licenses, hardware resources, cloud services, training, and personnel. Carefully evaluating these financial requirements against the potential benefits and ROI is crucial for making an informed purchase decision and ensuring the long-term viability of the service mesh deployment.

Table 4. Business Criteria Comparison

4. GigaOm Radar

The GigaOm Radar plots vendor solutions across a series of concentric rings with those set closer to the center judged to be of higher overall value. The chart characterizes each vendor on two axes—balancing Maturity versus Innovation and Feature Play versus Platform Play—while providing an arrowhead that projects each solution’s evolution over the coming 12 to 18 months.

Figure 2. GigaOm Radar for Service Mesh

As you can see in Figure 2, Buoyant.io, greymatter.io, HashiCorp, Kong, Solo.io, and CNCF’s open source Istio and Linkerd projects are Leaders based on their high aggregate scores in our decision criteria. In addition, Buoyant.io, greymatter.io, Isovalent, Kong, Solo.io, and CNCF’s open source Linkerd are recognized as Outperformers based on their rate of change and solution enhancements compared to the industry in general.

It should be noted that Maturity does not exclude Innovation. Instead, it identifies the solution as being proven in a production setting compared to a newer solution undergoing innovation to achieve initial customer acceptance and adoption. Furthermore, positioning in the Platform Play quadrants indicates that the service mesh includes the functionality generally expected from a service mesh and can be deployed on a wide range of platforms, even if the project or vendor is focused on a limited set of use cases. In contrast, service meshes are positioned in the Feature Play quadrants for the following reasons:

• The service mesh supports a limited range of platforms (AWS App Mesh, Anthos Service Mesh, OpenShift Service Mesh, and Traefik Service Mesh).
• The service mesh has a limited set of features (Network Service Mesh).
• The service mesh includes the functionality generally expected from a service mesh but with a new and evolving architecture (Cilium Service Mesh and Isovalent).

The length of the arrow (Forward Mover, Fast Mover, or Outperformer) is based on the rate of change and execution against roadmap and vision (based on project or vendor input and in comparison to improvements made across the industry in general).

Buoyant.io and Isovalent are new additions to the list of vendors this year, with each offering enterprise-ready distributions of their respective open source projects, Linkerd and Cilium Service Mesh. Since last year, several acquisitions have occurred, which may affect investment and innovation. Cisco acquired Isovalent in April 2024, and IBM acquired HashiCorp, with that deal expected to close by the end of 2024. Additionally, following Broadcom’s acquisition of VMware, Tanzu Service Mesh is no longer available as a standalone solution and has been removed. Furthermore, F5 appears to be transitioning from a proprietary implementation to open source Istio’s dual-stack networking implementation, and Traefik Labs is shifting its focus away from service mesh to API management technology.

Since the 2023 GigaOm Radar for Service Mesh, the Istio project has reestablished itself as a Leader under the direction of Solo.io, while VMware’s development of Tanzu Service Mesh has slowed down from being an Outperformer to a Forward Mover due to the uncertainty surrounding the future of the Tanzu portfolio following its acquisition by Broadcom.

When reviewing solutions, it’s important to keep in mind that there are no universal “best” or “worst” offerings; every solution has aspects that might make it a better or worse fit for specific customer requirements. Prospective customers should consider their current and future needs when comparing solutions and vendor roadmaps.

5. Solution Insights

Amazon Web Services (AWS): AWS App Mesh

Solution Overview
Founded in 1994, Amazon is the world’s largest online retailer and a major player in artificial intelligence, cloud computing, and digital streaming. Launched at AWS re:Invent 2018 and generally available since March 2019, AWS App Mesh is a fully managed service that provides application-level networking for microservices.

Configured via the AWS Management Console or the AWS CLI, AWS App Mesh uses a customized version of the open source Envoy proxy as a sidecar container deployed alongside each microservice, forming a mesh network handling all inbound and outbound traffic based on defined policies, automatic service discovery, load balancing, and traffic routing. The centralized control plane provides easy policy management, enabling quick updates and consistent application behavior across compute platforms.

Supporting a wide range of application architectures–from traditional monoliths to complex microservices–across both containerized and non-containerized environments, the mesh seamlessly integrates with various AWS compute services, including Amazon EC2 instances, Amazon ECS tasks, Amazon EKS pods, and AWS Fargate. It also integrates with AWS Outposts for applications running on-premises and AWS CloudMap for service discovery, enabling it to dynamically route traffic to the appropriate services based on their current status and configuration.

App Mesh offers advanced traffic management capabilities, such as fine-grained traffic routing based on weighted distributions, path-based routing, and header-based routing for HTTP and gRPC requests, supporting automatic retries and circuit breaking to enhance application resilience. App Mesh provides built-in security features, including mTLS authentication for secure service-to-service communication and integration with AWS Certificate Manager for certificate management. It seamlessly integrates with AWS observability tools like Amazon CloudWatch and AWS X-Ray, enabling detailed monitoring, logging, and distributed tracing of microservices.

Strengths
AWS App Mesh offers tight integration with the AWS ecosystem, enabling seamless deployment across Amazon EC2, ECS, EKS, and Fargate. As a fully managed service, it eliminates the operational overhead of running a service mesh control plane. App Mesh’s use of the Envoy proxy ensures broad compatibility with AWS and third-party monitoring tools like CloudWatch and X-Ray. Fine-grained traffic controls, including weighted routing for canary deployments, enhance its traffic management capabilities. With support for mTLS authentication and granular security policies, App Mesh provides robust security features. Its ability to extend the mesh to on-premises applications via AWS Outposts further sets it apart. While AWS-centric, App Mesh’s powerful feature set and operational simplicity make it a compelling choice for AWS-based microservices architectures.

Challenges
As an AWS-centric service mesh, AWS App Mesh may not be ideal for organizations with multicloud or hybrid cloud strategies. App Mesh’s deep integration with AWS services can be a double-edged sword, potentially leading to vendor lock-in and making it challenging to migrate to other platforms. While App Mesh supports non-containerized workloads and key protocols such as HTTP, HTTP/2, and gRPC, its primary focus is on containerized environments, and its limited support for other protocols may affect App Mesh’s applicability for certain legacy systems or specific use cases. Some users report a steeper learning curve than for other service mesh solutions, particularly when configuring advanced traffic management scenarios. App Mesh also lacks some of the more advanced observability and tracing features found in other service meshes. Managing and debugging the Envoy sidecars can also introduce complexity, particularly in large-scale deployments involving numerous microservices.

Purchase Considerations
Amazon offers a simple pay-as-you-go model, by which customers incur costs only for the compute and storage resources actually consumed by their application and the Envoy proxy sidecars deployed alongside the application containers. There are no additional fees for using App Mesh capabilities.

AWS App Mesh is primarily suited for organizations looking to implement a service mesh within the AWS ecosystem, leveraging its seamless integration with other AWS services.

Radar Chart Overview
Amazon is a Challenger in the Maturity-Feature Play quadrant. AWS App Mesh is a fully managed service mesh that simplifies the networking and management of microservices. Its ease of use, integration with AWS services, and focus on security and observability position it as a compelling option for AWS-centric environments adopting microservices architectures.

Buoyant.io: Buoyant Enterprise for Linkerd

Solution Overview
Founded in 2015, Buoyant.io created and maintains Linkerd, an open source service mesh donated to the Cloud Native Computing Foundation (CNCF) in 2017. In February 2024, Buoyant.io released Buoyant Enterprise for Linkerd (BEL), an enterprise-ready distribution of Linkerd tailored for large-scale use. It includes additional proprietary tools and features not available in the open source version.

BEL incorporates lifecycle automation tools to streamline the deployment, updating, and management of Linkerd instances across an organization’s infrastructure. Extending to every proxy across all clusters, this automation ensures that service mesh instances are consistently configured and maintained, reducing the potential for human error and the operational overhead associated with manual management.

A key feature of BEL is high-availability zonal load balancing (HAZL), which optimizes traffic management across multiple availability zones to minimize latency and network costs. HAZL dynamically balances HTTP and gRPC traffic, reducing expensive cross-zone traffic and enhancing application availability and resilience. This feature is particularly beneficial for Kubernetes clusters deployed across multiple zones, as it ensures efficient resource usage while maintaining high availability.

BEL adheres to stringent security standards, including FIPS 140-2 compliance, ensuring that cryptographic operations within the service mesh meet federal regulations for data protection. Automated security policy generation based on observed traffic simplifies the enforcement of security policies based on live traffic patterns, enhancing the security posture by automatically adapting to environmental changes. BEL also integrates with Buoyant Cloud for SaaS-based monitoring and alerting, providing enterprises with real-time insights into the health and performance of their service mesh deployments.

Strengths
Buoyant Enterprise for Linkerd provides lifecycle automation, simplifying deployment and updates, and high-availability zonal load balancing (HAZL), enhancing resilience by distributing traffic across zones. It ensures robust security with FIPS-140-2 compliance and automated security policy generation from live traffic. BEL’s integration with Buoyant Cloud offers SaaS-based monitoring and alerting, providing real-time insights and proactive issue resolution. Linkerd’s use of Rust for its micro-proxy ensures superior performance and security, avoiding vulnerabilities common in C/C++ projects. The recent 2.15 release extends support to non-Kubernetes environments, including virtual machines, to enable hybrid deployments. These features collectively enhance security, reliability, and operational efficiency, making BEL a compelling choice for enterprise-scale service mesh deployments.

Challenges
While Buoyant Enterprise for Linkerd 2.15 introduced mesh expansion to support non-Kubernetes workloads, such as those running on VMs or bare metal servers, it still lacks a universal mode for VMs compared to some competitors. Linkerd’s Rust-based sidecar proxy provides performance benefits but doesn’t use the popular Envoy proxy, missing out on some Envoy features like rate limiting. BEL’s advanced features, such as lifecycle automation and high-availability zonal load balancing, require a subscription-based pricing model, which may not suit all organizations accustomed to the free, open source Linkerd. However, Linkerd 2.15 includes support for native sidecar containers, which is a new way to inject the Linkerd proxy alongside application containers, and SPIFFE (secure production identity framework for everyone) workload identities for machine-to-machine authentication within the mesh, addressing previous limitations.

Purchase Considerations
BEL is free for companies with fewer than 50 employees. However, with the 2.15 release, Buoyant has moved to a tiered (standard, premium, and enterprise) pod-based pricing model for BEL, with different tiers based on the organization’s size and Linkerd usage.

Enhancing Linkerd’s simplicity, performance, and security to meet enterprise production needs, Buoyant Enterprise for Linkerd’s use cases include managing service-to-service communications in Kubernetes and non-Kubernetes workloads, improving reliability through circuit breaking and high-availability zonal load balancing, ensuring compliance with FIPS-140-2 standards, and automating lifecycle management.

Radar Chart Overview
Buoyant is a Leader in the Maturity/Platform Play quadrant. Built on Linkerd’s lightweight Rust-based “micro-proxy” architecture, BEL offers enterprise-grade features without the complexity and overhead of other service meshes. With a focus on operational simplicity, resource efficiency, and a powerful feature set, BEL is well-positioned as a compelling choice for organizations seeking a secure, performant service mesh solution.

CNCF: Cilium Service Mesh

Solution Overview
Contributed by Isovalent in December 2021, Cilium Service Mesh is a graduated CNCF project, achieving this status on October 11, 2023. As a graduated project, Cilium Service Mesh has met the CNCF’s criteria by reaching a high level of technical maturity, widespread production usage and adoption by multiple organizations and users, and sustainability within the cloud-native ecosystem.

Cilium Service Mesh uses eBPF (extended Berkeley Packet Filter) technology to provide scalable networking, security, and observability for cloud-native environments. Unlike traditional service meshes that rely on sidecar proxies, Cilium integrates the service mesh layer directly into the Linux kernel, eliminating the need for sidecars and reducing complexity and resource overhead. Managing connectivity at both the networking and application protocol layers, Cilium integrates seamlessly with Kubernetes to offer a native experience for managing service-to-service communication and security policies.

The service mesh offers a choice of control plane options, ranging from simpler solutions like Gateway API and Ingress to more feature-rich options like Istio or the Envoy CRD. This flexibility allows users to choose between running a service mesh with or without sidecars based on their specific requirements. Cilium introduces the CiliumEnvoyConfig (CEC) CRD, which provides a low-level abstraction for programming Envoy proxies directly, unlocking the full feature set of Envoy for advanced Layer 7 use cases.

Cilium’s architecture supports multicluster connectivity through its Cluster Mesh feature, enabling consistent policy enforcement and service discovery across distributed environments. By leveraging eBPF, Cilium Service Mesh bypasses the performance overhead associated with traditional sidecar proxies, enabling direct and efficient communication between services. It provides resilient connectivity, Layer 7 traffic management, identity-based security, observability, and tracing capabilities, all while maintaining transparency to applications without requiring code changes.

Strengths
Cilium Service Mesh leverages eBPF, which integrates service mesh functionality directly into the Linux kernel, eliminating the need for sidecar proxies. This approach significantly reduces complexity and resource overhead while enhancing performance and scalability. Cilium offers flexible control plane options, supporting both sidecar and sidecar-free models, and integrates seamlessly with existing tools like Istio and Envoy. It provides robust L7 traffic management, security features such as mTLS, and extensive observability through integrations with Prometheus and Grafana. Cilium’s ability to handle a wide range of protocols (IP, TCP, UDP, HTTP, Kafka, gRPC, and DNS) makes it a versatile and high-performance solution for a cloud-native environment.

Challenges
While Cilium Service Mesh’s eBPF-based approach offers performance benefits by eliminating sidecars, it faces limitations in handling complex application-layer protocols like HTTP/2 and TLS termination within the kernel space. The constraints imposed by the eBPF verifier restrict the complexity of eBPF programs, potentially limiting advanced service mesh features. With Kubernetes 1.28 introducing native support for service mesh sidecars, Cilium may face competition from more tightly integrated solutions that leverage the Kubernetes API for seamless sidecar management and lifecycle operations. These native sidecars can offer robust security and operational isolation, potentially making them more appealing for enterprises seeking mature, well-supported solutions. However, Cilium’s flexibility in supporting both sidecar and sidecar-less models could mitigate this challenge by allowing users to choose the approach that best suits their requirements.

Purchase Considerations
Open source Cilium Service Mesh is free to use. For enterprise-grade features and support, Isovalent offers a commercial distribution called Isovalent Enterprise for Cilium.

Cilium Service Mesh’s use cases include sidecar-less service mesh, Layer 7 load balancing, mTLS encryption, canary deployments, multicluster connectivity via ClusterMesh, and identity-based network policies.

Radar Chart Overview
CNCF (Cilium) is a Challenger in the Innovation/Feature Play quadrant. Cilium Service Mesh leverages eBPF to integrate service mesh functionality directly into the Linux kernel, eliminating the need for sidecar proxies. By tightly coupling networking and security, Cilium offers a high-performance, sidecarless service mesh solution tailored for cloud-native environments, setting it apart from traditional sidecar-based architectures like Istio and Linkerd. This kernel-level approach enhances performance, scalability, and efficiency but lacks the robust isolation and security of sidecars.

CNCF: Istio

Solution Overview
Created by Google, IBM, and Lyft in 2017, Istio is a graduated CNCF project that achieved this status on July 12, 2023. As a graduated project, Istio has met the CNCF’s criteria by reaching a high level of technical maturity, widespread production usage and adoption by multiple organizations and users, and sustainability within the cloud-native ecosystem.

Istio is an open source service mesh that provides a uniform way to secure, connect, and observe microservices. It operates by deploying Envoy proxies as sidecars alongside each service instance in a Kubernetes pod, which handles all inbound and outbound traffic. Designed for extensibility, Istio offers a robust, unified K8s-based control plane for managing K8s (in either public or on-premises clouds), VM, and bare metal data planes, supporting a diverse range of deployment needs.

Istio’s architecture is divided into a data plane and a control plane. The data plane consists of Envoy proxies that manage traffic and collect telemetry, while the control plane, Istiod, configures these proxies to enforce policies, manage traffic, and collect telemetry data, ensuring secure and efficient communication between services. It is designed to be platform-independent and can be deployed on various environments, including Kubernetes, virtual machines, and multicloud setups.

Recent advancements in Istio include the introduction of ambient mesh, a sidecar-less architecture that simplifies operations and reduces resource overhead. Ambient mesh uses lightweight shared per-node Layer 4 proxies and optional Layer 7 per-workload proxies, providing the same security and observability benefits without the need for sidecars. This new mode significantly reduces memory and CPU usage, making Istio more efficient and easier to manage in large-scale deployments.

Strengths
One of the more mature service meshes available, Istio stands out for its comprehensive feature set and robust security capabilities. It offers advanced traffic management, including request routing, fault injection, and circuit breaking, which are essential for maintaining resilient microservices architectures. Istio’s security features include mTLS for secure service-to-service communication, JWT-based end-user authentication, and fine-grained access control policies.

Istio supports both Kubernetes and VM environments, making it versatile for hybrid cloud deployments. The introduction of ambient mesh further enhances its efficiency by reducing resource overhead and simplifying operations. Istio’s extensive community support, integration with various authentication providers, and adherence to security standards like FIPS make it a preferred choice for large, security-focused enterprises.

Challenges
Istio’s robust and powerful capabilities come with a steep learning curve and can be resource-intensive, particularly in large deployments. Some users may find it overly complex for simpler use cases, whereas lightweight alternatives may be more suitable. While Istio’s use of Envoy sidecar proxies offers robust functionality, it adds overhead in terms of both compute and memory resources. The solution’s ambient mode lacks some of the features of the sidecar mode, including seamless interoperability with sidecar mode, controlled egress traffic, multinetwork support, VM support, and enhanced troubleshooting. The introduction of Kubernetes-native sidecars in version 1.28 aims to simplify the deployment and management of sidecars, potentially reducing the need for complex service meshes like Istio by offering a more integrated and streamlined approach to managing sidecar lifecycles and behaviors.

Purchase Considerations
While the open source Istio project is free to use, many vendors offer commercial products and services around Istio to simplify deployment and management.

Istio’s primary use cases include enabling traffic management strategies like canary deployments and A/B testing, enforcing policies for access control and rate limiting, enhancing observability through telemetry data collection, and simplifying multicloud and hybrid deployments by providing a consistent service mesh layer across environments.

Radar Chart Overview
CNCF (Istio) is a Leader in the Maturity/Platform Play quadrant. Istio is a leading open source service mesh that provides a comprehensive set of capabilities for securing, connecting, and observing microservices. Despite competition from alternative solutions and the threat of emerging technologies, Istio is well-positioned for enterprise adoption due to its maturity, backing by major industry players, and alignment with trends like zero-trust security.

CNCF: Kuma

Solution Overview
Contributed by Kong in June 2020, Kuma is a CNCF sandbox project, meaning it represents an early-stage initiative that aims to encourage public visibility of experiments or other early work that can potentially build the ingredients of a successful incubation-level project.

Kuma is an open source control plane for service mesh built on top of the Envoy proxy. It supports a wide range of environments, including Kubernetes, virtual machines, and bare metal, making it a versatile solution for both cloud-native and traditional applications. Unlike other service mesh solutions, Kuma provides native support for Kubernetes and VMs on both control and data planes. Additionally, it facilitates multimesh deployments, allowing for the creation of multiple isolated service meshes within the same cluster, which can significantly lower operational costs and complexity.

One of Kuma’s key features is its native multizone support, which enables seamless connectivity across different clouds, clusters, and regions. This feature is particularly beneficial for organizations with distributed architectures because it allows them to deploy a single service mesh across multiple Kubernetes clusters, clouds, and regions, with automatic policy synchronization and cross-zone connectivity. Kuma’s architecture is designed to be scalable and enterprise-ready, offering advanced security features such as mTLS for encrypted communication between services, automatic proxy injection, and fine-grained traffic control policies.

Kuma offers a powerful yet simple policy architecture for implementing zero-trust security, observability, service discovery, routing, and traffic reliability. It leverages attribute-based policies and tagging selectors, allowing fine-grained control over service traffic based on arbitrary attributes like region, compliance requirements, or service types. Moreover, Kuma provides an intuitive user interface and command-line tools (kumactl) for easy installation, configuration, and management of the service mesh.

Strengths
Kuma is a modern, Envoy-based service mesh that stands out with its broad platform support, spanning Kubernetes, VMs, and hybrid environments. It excels in multicluster and multizone deployments, providing automated cross-zone connectivity and policy synchronization across diverse infrastructures. Kuma’s powerful policy architecture enables fine-grained control over service traffic using attribute-based selectors, making it well-suited for enterprise environments with stringent regulatory requirements. Kuma also offers multitenancy support, allowing multiple isolated meshes to be managed from a unified control plane and reducing operational costs. Its focus on simplicity, an intuitive learning curve, and seamless integration with platforms like Envoy and Kubernetes contribute to Kuma’s ease of adoption and management.

Challenges
While recognized for its ease of use and modular structure, Kuma faces specific technical limitations and challenges compared to other service meshes. Its universal approach, designed to accommodate both Kubernetes and traditional infrastructure, may introduce complexity in environments heavily optimized for Kubernetes, where alternative solutions offer deeper integrations.

Kuma’s focus on sidecar proxies can introduce performance overhead and added complexity in environments already dealing with high operational demands, with its performance in multicluster scenarios observed to lag behind other service meshes, particularly in environments demanding high throughput and low latency. Although Kuma excels in multizone and multicluster deployments, it may not yet match the maturity and extensive feature set of more established meshes, particularly when handling complex enterprise use cases and large-scale enterprise deployments. It also lacks the advanced customization, granular control, and extensibility required for highly specific enterprise use cases.

Purchase Considerations
Kuma is free open source software available under the Apache 2.0 license. Kong Inc. provides an enterprise distribution called Kong Mesh, which bundles Kuma with additional enterprise features, support, and managed services via Kong Konnect.

Kuma’s key use cases include secure service-to-service communication with mTLS, multicluster, and multicloud deployments with automated cross-zone connectivity, and multitenancy with isolated meshes managed from a single control plane.

Radar Chart Overview
CNCF (Kuma) is a Challenger in the Innovation/Platform Play quadrant. Kuma is a modern, easy-to-use, platform-agnostic service mesh solution enabling the implementation of a service mesh across an entire infrastructure, regardless of the underlying platforms. It facilitates service mesh adoption by making it more accessible and practical for organizations of various sizes and infrastructure setups.

CNCF: Linkerd

Solution Overview
Contributed by Buoyant.io in 2017, Linkerd is a graduated CNCF project that achieved this status on July 28, 2021. As a graduated project, Linkerd has met the CNCF’s criteria by reaching a high level of technical maturity, widespread production usage and adoption by multiple organizations and users, and sustainability within the cloud-native ecosystem.

An open source service mesh designed primarily for Kubernetes environments, Linkerd provides critical features out of the box–-including observability, reliability, and security— without requiring any changes to application code. Its architecture is based on ultralight Rust “micro-proxy” sidecars, which offer significantly better performance, resource usage, and operability compared to other service meshes. Linkerd’s control plane manages configuration and policy enforcement across the mesh, transforming isolated proxies into a cohesive distributed system to ensure the security, reliability, and observability of all service-to-service traffic.

Linkerd offers advanced load balancing, including request-level latency-aware load balancing as the default. This balancing dynamically distributes network traffic across service instances based on real-time metrics and health checks, ensuring optimal resource utilization, high availability, and low latency. Additionally, Linkerd supports dynamic request routing and traffic splitting, enabling sophisticated traffic management strategies such as canary deployments and blue-green deployments. These features allow users to seamlessly control and optimize traffic flow between services.

The service mesh includes advanced authentication and authorization protocols, automated certificate management, and automatic mTLS for secure service-to-service communication. Linkerd’s use of the Rust programming language at the data plane layer helps avoid common security vulnerabilities found in C and C++ projects, ensuring a robust security posture. Integration with existing Kubernetes security primitives enables a zero-trust security model by default without requiring additional configuration.

Strengths
Linkerd’s ultralight Rust “micro-proxy” sidecars offer superior performance, resource efficiency, and operational simplicity compared to Envoy-based service meshes. It provides automatic mTLS for secure service-to-service communication by default without requiring configuration. Linkerd’s advanced load balancing capabilities, including request-level latency-aware load balancing, ensure optimal traffic distribution and low latency. The service mesh also supports dynamic request routing, traffic splitting, and circuit breaking for sophisticated traffic management. Linkerd’s control plane collects detailed telemetry data for observability and integrates with existing Kubernetes primitives rather than inventing new ones, enabling a zero-trust security model.

Challenges
Linkerd, while offering significant performance and simplicity advantages, faces several technical limitations and challenges. The use of a custom proxy instead of Envoy restricts extensibility and the adoption of community-driven enhancements, while the absence of an officially exposed API for the control plane may hinder automation and integration efforts. It lacks support for sidecarless implementations, which some competitors offer to reduce resource overhead and improve performance at the node level. Linkerd does not integrate eBPF, limiting its ability to leverage kernel-level optimizations for network traffic. However, both these innovations are designed to improve the performance of the Envoy proxy, which does not apply to Linkerd. While the latest version of Linkerd supports hybrid platforms, its primary focus remains on Kubernetes environments.

Purchase Considerations
Linkerd can be downloaded free of charge from GitHub. Smaller companies with fewer than 50 employees can download a stable enterprise distribution of Linkerd for free from Buoyant.io. The open source project will continue to provide edge releases, which are production-ready but do not have semantic versioning guarantees for upgrades.

Linkerd’s use cases include monitoring microservices’ success rates and latency, encrypting service-to-service communication with automatic mTLS, and diagnosing failures with real-time traffic analysis.

Radar Chart Overview
CNCF (Linkerd) is a Leader in the Maturity/Platform Play quadrant. Linkerd’s simple yet feature-rich design allows users to easily manage service-to-service communication, enforce policies, and secure their applications without the complexity and overhead associated with other service mesh solutions. Its lightweight architecture and ease of use make it a good fit for companies looking to get started quickly with a service mesh, as minimal configuration is required.

CNCF: Network Service Mesh

Solution Overview
Contributed in April 2019, Network Service Mesh is a CNCF sandbox project. It represents an early-stage initiative that aims to encourage public visibility of experiments or other early work that can potentially build the ingredients of a successful incubation-level project.

Network Service Mesh (NSM) provides a hybrid, multicloud IP service mesh enabling individual Kubernetes pods, VMs, or physical servers to network with other workloads across multiple clusters, clouds, or on-premises environments using a simple set of APIs. NSM’s architecture includes network service clients (NSCs), network service endpoints (NSEs), and virtual wires (vWires). NSCs request connections to network services by name, while NSEs provide the requested network services. Acting as virtual connections between NSCs and NSEs, vWires ensures secure and reliable packet transmission.

NSM APIs enable each NSC to attach to zero or many network services, providing the required connectivity, security, and observability features independently of where the workloads are running. Network services can range from a simple distributed virtual Layer 3 (vL3) for IP communication to traditional service meshes like Consul, Istio, Kuma, or Linkerd. NSM allows a single workload to connect to multiple Layer 7 service meshes, enabling cross-company interactions and collaborative service mesh environments.

NSM configuration is managed through a Kubernetes Operator, which automates the creation and management of NSM infrastructure components like network service managers, forwarders, and registries. This operator allows users to declare forwarders and their respective images in a custom resource manifest, simplifying the deployment and management of NSM. The configuration process involves creating vWires through gRPC calls, which connect NSCs to network services, monitoring connections to ensure they remain active and functional.

Strengths
Network Service Mesh provides hybrid, multicloud IP service mesh capabilities, enabling seamless connectivity across diverse environments, including multiple Kubernetes clusters, clouds, and on-premises setups. Its architecture introduces the concept of vWires, which act as virtual wires connecting clients to endpoints, allowing for flexible and secure network service connectivity without requiring changes to Kubernetes or workloads. NSM’s configuration is managed through a Kubernetes Operator, simplifying deployment and management. It supports advanced traffic management and load balancing at lower network layers (Layer 2/Layer 3) and integrates robust security features, including mTLS for encryption and authentication, ensuring secure and observable communication across distributed systems.

Challenges
Network Service Mesh operates primarily at lower network layers (L2/L3), lacking advanced Layer 7 traffic management features found in other service meshes. Its architecture, centered around vWires connecting clients to endpoints, may introduce complexity when handling diverse use cases beyond simple IP communication, which can entail a steep learning curve for teams unfamiliar with NSM. Performance overhead can be a concern because the use of vWires and multiple proxies may introduce latency and resource consumption issues. Unlike more mature service meshes, NSM’s ecosystem and community support are still developing, potentially impacting ease of adoption, configuration, and observability in production environments. Integration with existing Kubernetes and cloud-native tools may require additional configuration and management effort.

Purchase Considerations
Network Service Mesh is an open source project with no direct pricing model. Key considerations include the deployment scale, performance requirements, and any professional services needed for implementation and support.

Network Service Mesh’s key use cases include providing a common IP reachability domain for database replication, connecting workloads to different Layer 7 service meshes concurrently, and implementing zero-trust networking with per-workload granular policies.

Radar Chart Overview
CNCF (Network Service Mesh) is an Entrant in the Innovation/Feature Play quadrant. Network Service Mesh occupies a unique niche by enabling multicluster, multicloud networking capabilities beyond traditional service meshes. However, as a relatively new CNCF sandbox project, its market positioning and adoption levels appear to be still developing compared to more mainstream service mesh solutions at this stage. Its differentiated capabilities could make it complementary to existing service meshes in complex hybrid/multicloud environments over the long run.

F5: F5 Aspen Mesh

Solution Overview
Founded in 1996, F5 specializes in application availability, performance, security, and multicloud management. F5 Aspen Mesh, which was an internal incubation project before being released in 2017, is a security-hardened Istio-based service mesh distribution built to manage complex, mature Kubernetes infrastructures.

Simplifying the deployment, lifecycle management, and monitoring of microservices in complex infrastructures spanning multiple clusters and clouds, Aspen Mesh extends Istio’s capabilities with advanced features such as granular traffic control, strict security policies out-of-the-box, native dual-stack networking, simplified certificate management, simplified installations and upgrades, and an intuitive user interface. Objective-driven, AI/ML-powered insight recognition policy frameworks allow users to specify, measure, and enforce business goals.

Aligning deployment with specific operational, performance, and security requirements, Aspen Mesh supports automated and manual sidecar injection. This enables fine-grained control over which Kubernetes namespaces or pods should include the Envoy proxy sidecars. Configuration is streamlined with Helm charts and override values files, including configuring authentication modes for dashboard access, setting up cluster names for reporting, and managing persistent volume claims for metrics collection.

Available as a SaaS platform, Aspen Mesh offers traffic control and policy enforcement, enabling features like load balancing, traffic routing, and canary deployments. It offers comprehensive security capabilities, automatic certificate management, attribute-based (ABAC) and role-based (RBAC) access control for fine-grained authorization, and built-in mTLS support for authenticated and encrypted service-to-service communication, ensuring trust within the mesh. These features collectively contribute to a zero-trust network architecture, reducing the attack surface and improving the overall security posture of microservices deployments.

Strengths
F5 Aspen Mesh, an Istio-based service mesh platform available as a SaaS, simplifies the deployment and management of microservices at scale, including multicluster and multicloud environments. It provides comprehensive traffic control, policy enforcement, and observability, enabling load balancing, traffic routing, and canary deployments, which extends Istio’s capabilities with advanced features like strict security configurations, native dual-stack IPv4/IPv6 networking, granular certificate management, enhanced observability via service-mesh traffic capture, and simplified installations and upgrades. Aspen Mesh’s robust security features include mTLS encryption and service-to-service authorization. It supports seamless migrations from traditional monolithic architectures to cloud-native applications, ensuring high availability and disaster recovery capabilities backed by 24/7 expert support for reliable operation and maintenance.

Challenges
F5 Aspen Mesh is based on Istio, so it inherits complexities associated with managing and configuring a service mesh, which can be daunting for users without extensive expertise in Kubernetes and microservices architectures. The integration of advanced security features and configurations, although beneficial, adds layers of complexity that may impact the ease of deployment and operational agility. The dual-stack networking and granular certificate management, while providing enhanced capabilities, require meticulous setup and maintenance.

In January 2024, F5 released an experimental carrier-grade version of Aspen Mesh with open source Istio’s dual-stack networking implementation instead of the proprietary Aspen Mesh implementation. This raises the possibility of F5 adopting open source Istio as its primary service mesh, with the Aspen Mesh following NGINX Service Mesh (NSM) into end of support (EoS).

Purchase Considerations
F5 Aspen Mesh is a subscription-based SaaS platform available per node. It offers optional paid services and 24/7 support from F5’s team of service mesh experts.

F5 Aspen Mesh is designed for complex microservices architectures demanding high availability or disaster recovery capabilities across multiple clusters and clouds. It’s ideal for 5G deployments, supplying a service mesh building block for 5G SA and transitioning from 4G/5G NSA to standalone 5G.

Radar Chart Overview
F5 is a Challenger in the Maturity/Platform Play quadrant. It is an enterprise-grade, Istio-based service mesh platform catering to complex infrastructures spanning multiple clusters and clouds. It is suitable for highly regulated businesses and service providers prioritizing data ownership and security, especially those with legacy services in transition.

Google: Anthos Service Mesh

Solution Overview
Founded in 1998, Google is a global technology giant specializing in internet-related products and services, including its search engine, cloud computing, and software. Officially introduced in 2018, Anthos Service Mesh (ASM) is a suite of tools that helps monitor and manage a service mesh deployed on-premises or Google Cloud (GCP). Built on top of open source Istio, it is a limited distribution providing a managed control plane for added resiliency and reduced operational effort.

Enhancing the management, observability, and security of microservices across various environments, including on-premises, Google Kubernetes Engine (GKE) clusters, and hybrid setups, ASM consists of a data plane—composed of Envoy proxy sidecars that handle inter-service communication—and a control plane managed by Google, which configures the proxies and enforces policies. ASM provides sophisticated traffic routing controls–such as canary deployments, A/B testing, and blue-green deployments–and enhanced security by implementing mTLS and access control policies to regulate which services can communicate with each other.

ASM offers two deployment options for the control plane: a managed Anthos Service Mesh and an in-cluster control plane. The managed option comprises a managed control plane and a managed data plane. Google handles upgrades, scaling, and security, minimizing manual user maintenance. With the managed data plane enabled, Google installs an in-cluster controller that manages the sidecar proxies. For the in-cluster control plane option, the user is responsible for managing a Google-supported distribution of the Istio daemon service, including upgrades and security.

Offering deep integration with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and Cloud Trace, ASM’s service topology graph shows the relationships and traffic flows between services, simplifying troubleshooting and maintenance. This comprehensive visibility into the service mesh aids operations teams in proactive monitoring and rapid problem resolution, thereby improving overall system reliability.

Strengths
Anthos Service Mesh is a fully managed service mesh that offers a range of technical strengths and benefits for managing microservices across multiple environments. Its key features include automated sidecar proxy injection and upgrades, significantly reducing operational overhead. ASM supports sophisticated traffic routing controls such as canary and blue-green deployments, enhancing deployment safety. It ensures enhanced security through mTLS encryption and robust access control policies. The service mesh offers deep insights into service metrics, logs, and traces, facilitating effective monitoring and debugging. It also seamlessly integrates with Google Cloud services and supports multicluster management, making it ideal for enterprises adopting service mesh technologies due to its simplicity and integration capabilities.

Challenges
While leveraging Istio’s core traffic management, observability, and security capabilities to provide a streamlined service mesh experience on Google Cloud, Anthos Service Mesh does not expose all of Istio’s pluggable components and architecture to end users. It supports GKE clusters only in specific regions and versions, with migrations from older setups not supported. Scale is limited to 1,000 services and 5,000 workloads per cluster. Slow configuration propagation can occur due to insufficient allocated resources or large cluster sizes, requiring workarounds or reducing the configuration state. Some general service mesh challenges include increased complexity from additional runtime instances and proxies, and the need to integrate the mesh into workflows. Organizations should carefully evaluate their requirements in light of ASM’s limitations, especially considering the uncertainty surrounding its future, given its limited adoption.

Purchase Considerations
Google offers ASM free for GKE Enterprise customers, while standalone customers on GCP pay a per-cluster hourly fee plus a per-client hourly fee after a monthly allocation.

ASM’s use cases include securing service-to-service communication across hybrid and multicloud environments with mTLS and access policies, improving application resiliency through traffic management features like retries and circuit breakers, and gaining deep observability into service performance for troubleshooting.

Radar Chart Overview
Google is a Challenger in the Maturity/Feature Play quadrant. Anthos Service Mesh is a fully managed service mesh that simplifies managing microservices across Google Cloud, on-premises, and other clouds. ASM’s Google-managed control plane integrates with Amazon Elastic Kubernetes Service (EKS) on AWS, Azure Kubernetes Service (AKS) on Microsoft Azure, and on-premises clusters, making it an attractive option for enterprises looking to streamline their microservices operations in a hybrid environment.

Greymatter.io: Greymatter

Solution Overview
Founded in 2015, Greymatter.io specializes in managing, securing, and optimizing application and service performance across hybrid, multicloud, and on-premises environments. Released in 2019, Greymatter is a service connectivity platform that integrates service mesh capabilities, API management, and infrastructure intelligence.

Greymatter’s architecture provides a comprehensive service connectivity layer for managing distributed applications across hybrid, multicloud, and sovereign cloud environments. It includes a dedicated infrastructure layer supporting sidecar and sidecarless implementations. Greymatter employs a decentralized architecture with distributed control planes and distributed data planes. The control planes handle service discovery, configuration management, and policy enforcement for specific network segments, while the Envoy-based data plane handles traffic routing, load balancing, and security enforcement.

The platform provides comprehensive security features–including automated mTLS for encrypted communication, ephemeral certificate management, and integration with SPIFFE/SPIRE for secure service identity management–and supports advanced traffic management features such as intelligent routing, load balancing, and fault injection. The control plane acts as a decision point for security policies across applications, APIs, data sources, and microservices, ensuring compliance with NIST zero-trust guidelines, while each data plane acts as an enforcement or information point closest to the resource. Greymatter also offers comprehensive audit trails, dynamic policy enforcement, and integration with SIEM, SOAR, EDR, and other security systems for incident detection and response.

Greymatter simplifies configuration through its use of CUE-based projects managed through the use of GitOps and an automation Dev Kit, which reduces configuration code by 90% compared to JSON, YAML, or Helm charts. It also offers policy enforcement, configuration management playbooks, and lifecycle management automation to streamline operations and ensure consistent policy enforcement across diverse environments. The platform’s configuration management playbooks provide predefined templates and workflows that automate routine tasks, reducing operational overhead and minimizing configuration errors.

Strengths
Greymatter integrates networking gateways, service mesh, API management, and infrastructure intelligence into a unified solution. It provides robust security capabilities, including automated mTLS encryption, SPIFFE/SPIRE integration for service identity management, fine-grained access controls through ABAC, RBAC, and NGAC, and compliance with NIST zero-trust guidelines. Supporting sovereign, multicloud, and hybrid environments, Greymatter’s control plane architecture simplifies configuration management with powerful declarative playbooks, enabling automated lifecycle management across hybrid and multicloud environments. Advanced observability features, powered by deep telemetry collection and analysis, offer real-time insights into service behavior, anomaly detection, and business intelligence. Support for 5G core protocols and multiprotocol negotiations enhances its versatility and performance in complex, mission-critical environments.

Challenges
Greymatter, while robust and feature-rich, faces specific technical limitations and challenges compared to other service meshes. Greymatter’s automation and DevOps orchestration model isolates developer code from infrastructure code based on a least privilege access model rooted in the separation of concerns, which can create a learning curve for teams without specialized expertise in cloud-native infrastructure. Additionally, Greymatter’s reliance on a custom control plane and configuration management playbooks may require significant initial setup and ongoing maintenance efforts. The platform’s advanced security and observability features, while beneficial, can also introduce performance overhead, potentially impacting latency and resource consumption. Greymatter’s integration with existing tools and environments, although flexible, may necessitate additional customization and tuning to achieve optimal interoperability and performance in diverse enterprise settings.

Purchase Considerations
Greymatter is sold as an annual software subscription offering predictable, flat pricing per mesh and service cluster, rather than the number of service instances, throughput, or usage.

Greymatter’s use cases include secure hybrid, multicloud, and sovereign cloud service-to-service communication, zero-trust security enforcement, API management, and real-time observability.

Radar Chart Overview
Greymatter.io is a Leader in the Innovation/Platform Play quadrant. Greymatter is proven in complex, highly secure defense and intelligence environments worldwide, providing an enterprise-ready solution that addresses real-world operational needs. Greymatter.io is experiencing a growth phase as it shifts from a company predominantly focused on US government and Department of Defense clients to a venture capital corporation supporting global customers spanning a variety of industries.

HashiCorp: HashiCorp Consul

Solution Overview
Founded in 2012 and acquired by IBM in April 2024 (deal expected to close by EoY 2024), HashiCorp is a leading provider of multicloud infrastructure automation software. Initially released in 2014 as a service discovery platform, HashiCorp Consul has evolved into a full-featured service mesh solution, providing secure service segmentation and configuration across any cloud or runtime environment.

HashiCorp Consul is a distributed, highly available service networking solution using the Envoy proxy to provide service discovery, configuration, and segmentation functionality. Leveraging a highly scalable decentralized client-server model, servers maintain the state of the service registry while clients handle the runtime aspects. Consul uses a gossip protocol to manage membership and broadcast messages to the cluster and a consensus protocol to provide consistency and availability in the case of failures.

Offered as a self-managed or managed solution, Consul supports multiple platforms, including containers, Kubernetes, and virtual machines, making it versatile for hybrid and multicloud environments. Configuration is managed via HTTP API, CLI, or DNS interface, enabling dynamic service configuration, including health checks, load balancer integration, and service segmentation. Integration with tools like HashiCorp Nomad provides advanced orchestration for dynamic infrastructures.

Consul provides advanced Layer 4 and Layer 7 traffic management using a series of configuration entries to manage traffic at the application layer. This enables detailed routing, splitting, and resolution strategies supporting various load-balancing algorithms, such as round-robin, least-request, and random, for sophisticated deployment patterns such as canary releases, A/B testing, and blue-green deployments. Consul’s intentions feature allows administrators to define which services may communicate with each other, implementing zero-trust networking principles at the application layer.

Strengths
HashiCorp Consul offers robust service discovery, enabling automatic detection of and connectivity between services across diverse environments. It enhances network configuration automation, reducing manual intervention and accelerating deployment processes. Unlike many other service meshes, Consul can run in a VM-only environment without requiring Kubernetes. Consul’s service mesh architecture ensures secure service-to-service communication through mTLS, supporting comprehensive security measures. The tool facilitates advanced traffic management capabilities, such as canary deployments and A/B testing, by controlling traffic flow at Layer 7.

Consul can also manage thousands of services across multiple data centers, maintaining high availability and resilience. It is tightly integrated with other HashiCorp products like Terraform and Vault, and HashiCorp’s managed Consul offering, HCP Consul Dedicated, is an attractive option for customers looking for push-button, self-service deployments.

Challenges
HashiCorp Consul, while offering robust service discovery and configuration, can be resource intensive, particularly on larger deployments, due to its gossip protocol and high network chattiness, which can lead to high resource consumption during peak operational times, potentially affecting performance. Upgrading Consul clusters requires careful planning, as version incompatibilities between servers and clients can cause issues. However, in November 2022, HashiCorp addressed this situation by introducing support for an alternative service mesh deployment architecture that does not require the use of the gossip protocol or clients. Configuration can be complex, especially when managing intentions and service mesh policies across environments, including cross-datacenter communication. Consul’s ecosystem is limited compared to competitors, lacking support for Kubernetes integrations such as Flagger. With only a small open source community supporting non-HashiCorp users, Consul’s primary value is for existing HashiCorp users wishing to incorporate Kubernetes into their HashiCorp stack. HashiCorp’s acquisition by IBM will undoubtedly cause some uncertainty about the future of Consul.

Purchase Considerations
HashiCorp offers a free, self-managed, open source version of Consul, a self-managed enterprise edition with tiered node and SLA-based subscription pricing, and a time and instance-based managed cloud service offering, HCP Consul Dedicated.

HashiCorp Consul’s use cases include service discovery and health monitoring, service networking and traffic management, middleware automation, and implementing zero-trust networking principles through its service mesh capabilities.

Radar Chart Overview
HashiCorp is a Leader in the Maturity/Platform Play quadrant. Consul’s platform versatility allows seamless integration with various runtimes, including containers, Kubernetes, and VMs, making it suitable for hybrid and multicloud infrastructures. Consul’s Layer 7 traffic management capabilities facilitate advanced deployment patterns, while mTLS encryption and identity-based authorization policies enforce zero-trust networking.

Isovalent: Isovalent Enterprise for Cilium

Solution Overview
Founded in 2017 and acquired by Cisco in April 2024, Isovalent created and maintains Cilium, an open source service mesh donated to the CNCF in 2021. In November 2023, Isovalent released Isovalent Enterprise for Cilium, a hardened, enterprise-grade distribution of the eBPF-based cloud networking platform Cilium. It includes advanced networking, security, and observability features not available in the open source version.

Isovalent Enterprise for Cilium extends the capabilities of the open source Cilium project by providing advanced networking, security, and observability features tailored for enterprise environments. It includes enhanced network policy capabilities such as DNS-aware policies, Layer 7 policies, and deny policies, enabling fine-grained control over network traffic for improved security and micro-segmentation. It offers multicluster connectivity via Cluster Mesh, allowing seamless networking and security across multiple clouds and on-premises environments.

The enterprise version also integrates advanced security features through Tetragon, which provides protocol enforcement, IP and port whitelisting, and automatic application-aware policy generation to protect against sophisticated threats. Tetragon’s capabilities are built on eBPF, ensuring scalability and performance in demanding cloud-native environments. Isovalent Enterprise for Cilium also includes features like mutual authentication, transparent encryption using WireGuard and IPsec, and support for the latest Gateway API version, enhancing security and operational capabilities.

Isovalent Enterprise for Cilium offers enterprise-grade support, including 24/7 support with service level agreements (SLAs), hardened and extended end-of-life (EOL) versions, and professional services to help organizations deploy and manage Cilium in production environments. The solution provides real-time network traffic flow observability, policy visualization, and a powerful user interface for easy troubleshooting and network management with its Hubble platform. Isovalent Enterprise for Cilium also integrates with popular observability tools like Prometheus, Grafana, and distributed tracing systems, enabling end-to-end visibility and correlation of metrics, logs, and traces.

Strengths
Isovalent Enterprise for Cilium stands out with its advanced eBPF-based architecture, offering sidecar-free service mesh capabilities that reduce complexity and resource overhead. It provides robust networking features, including DNS-aware, Layer 7, and deny policies for fine-grained traffic control, and multicluster connectivity via Cluster Mesh for seamless multicloud and on-premises integration. Robust security features include transparent encryption, mutual TLS authentication with SPIFFE integration, and Tetragon for protocol enforcement. Hubble provides comprehensive observability with real-time traffic visualization and policy management. Additionally, Isovalent Enterprise for Cilium includes enterprise-grade support, hardened releases, and professional services, making it a compelling solution for secure, scalable, and high-performance cloud-native environments.

Challenges
Isovalent Enterprise for Cilium, while offering advanced eBPF-based networking and security features, faces technical limitations compared to traditional sidecar-based service meshes. Its sidecar-less architecture, though reducing overhead, may lack the extensive feature set and mature ecosystem of solutions like Istio and Linkerd. The complexity of eBPF programming and kernel-level operations requires a deeper understanding of kernel internals, presenting both opportunities and challenges for developers in this space. Integrating existing infrastructure and legacy systems might also be less seamless than with established service meshes, potentially requiring significant effort for full compatibility. Kubernetes introduces native sidecar capabilities, so Cilium may face competition from these built-in solutions that offer robust security and operational isolation.

Purchase Considerations
Isovalent offers tiered (base, advanced, and advanced+) node-based subscriptions, with each tier offering advanced capabilities like enterprise support, multicluster connectivity, and security observability.

Isovalent Enterprise for Cilium’s use cases includes multicluster connectivity, advanced network policies, service mesh capabilities, real-time traffic visualization, protocol enforcement and automated policy generation, and transparent encryption.

Radar Chart Overview
Isovalent is a Challenger in the Innovation/Feature Play quadrant. Isovalent Enterprise for Cilium leverages an eBPF-based architecture to deliver a high-performance, sidecar-less service mesh solution that reduces resource overhead. Its integration with major cloud providers like AWS, Azure, and Google Cloud and its ability to operate in hybrid and multicloud environments enhance its appeal for large-scale, distributed deployments.

Kong: Kong Mesh

Solution Overview
Founded in 2017, Kong Inc. provides open source platforms and cloud services for managing, monitoring, and scaling APIs and microservices. Released in August 2020, Kong Mesh is an enterprise-grade service mesh built on top of open source Kuma and the Envoy proxy. It provides additional features, commercial support, and integration with Kong products.

While inheriting Kuma’s core service mesh capabilities, Kong Mesh extends them with several enterprise-focused features. These include multizone security with JWT-based authentication and third-party CA integration options such as HashiCorp Vault, AWS Certificate Manager, and Kubernetes cert-manager, which allow organizations to secure multizone deployments without storing sensitive data in Kong Mesh itself.

Kong Mesh offers enhanced enterprise capabilities like native support for Red Hat Universal Base Image (UBI) and FIPS 140-2 compliant encryption, catering to the needs of enterprise and federal environments with stringent security and compliance requirements. It also includes a native ECS controller for automating deployments on AWS ECS with Fargate and EC2, provisioning data plane proxy tokens securely via AWS Secrets Manager. These features ensure that Kong Mesh can be deployed in diverse environments, providing robust security and compliance.

Architecturally, Kong Mesh supports both Kubernetes and VM environments, offering multizone and multicluster deployments managed through a global control plane that propagates policies to zone control planes. It provides native integration with Kong Gateway Enterprise, enabling full-stack connectivity across API gateways, Kubernetes ingress, and service mesh from a unified control plane and SaaS management solution (Kong Konnect). This integration simplifies end-to-end connectivity, governance, and observability across diverse architectures and environments, making it a comprehensive solution for managing microservices with enhanced security, observability, and resilience.

Strengths
Kong Mesh offers multizone security with JWT-based authentication and third-party CA integrations like HashiCorp Vault and AWS Certificate Manager, ensuring secure multizone deployments. The Mesh Manager, integrated into Kong Konnect, provides a unified control plane for seamless management across hybrid, multicloud, and on-premises environments. Kong Mesh also supports advanced load balancing, traffic management, and observability with native integrations for Prometheus, Grafana, and OpenTelemetry. Its robust security features include mTLS, FIPS-140 encryption, and embedded Open Policy Agent (OPA) for policy enforcement, making it a comprehensive solution for managing microservices with enhanced security, observability, and resilience.

Challenges
Kong Mesh, while robust and feature-rich, faces specific technical limitations and challenges compared to other service meshes. One notable limitation is its reliance on the Envoy proxy, which can introduce additional memory and CPU overhead, impacting resource consumption and performance. Additionally, Kong Mesh does not currently support sidecar-less implementations, which are gaining traction for their reduced resource footprint and simplified architecture. Another challenge is the complexity of managing multizone deployments, which, although supported, can be intricate and require careful configuration. While Kong Mesh integrates well with Kong Gateway, it may not offer the same level of seamless integration with other third-party tools and platforms as some competitors do, potentially limiting interoperability in diverse environments.

Purchase Considerations
Kong offers a per-zone (cluster) pricing model based on the number of zones/clusters where Kong Mesh is deployed. Kong is also trialing a consumption-based pricing model through Mesh Manager in Kong Konnect.

Kong Mesh’s key use cases include enabling zero-trust security with mTLS encryption, advanced load balancing, circuit breaking, canary deployments, and a centralized control plane for managing distributed microservices.

Radar Chart Overview
Kong is a Leader in the Innovation/Platform Play quadrant. Kong Mesh is a simple yet powerful service mesh that supports hybrid environments, provides enterprise-grade security and multimesh capabilities, integrates natively with Kong’s API gateway, and offers flexibility through self-managed or SaaS deployment models. This positions Kong Mesh as a compelling choice for large enterprises embarking on microservices adoption across diverse infrastructures.

Red Hat: OpenShift Service Mesh

Solution Overview
Founded in 1993 and acquired by IBM in 2019, Red Hat operates as an independent subsidiary within IBM’s Hybrid Cloud division, focusing on its core competencies in open source software and enterprise solutions. Announced in August 2019, Red Hat OpenShift Service Mesh (OSSM) connects, manages, and observes microservices within the OpenShift Container Platform, a private PaaS developed by Red Hat for enterprises running OpenShift on on-premises or public cloud infrastructure.

OSSM is based on the Maistra project, an opinionated distribution of Istio—using Envoy proxies—tailored specifically for the OpenShift environment. Maistra modifies the upstream Istio project to optimize integration with OpenShift’s security and operational models. It also provides deeper integration with OpenShift’s monitoring and logging capabilities and support for OpenShift’s security and network policies. OSSM integrates with open source Grafana, Jaeger, Kiali, and Prometheus to enhance its functionality.

The mesh provides advanced traffic management capabilities that allow users to control the flow of traffic and API calls between services. This includes routing traffic based on weights, HTTP headers, and other parameters. It supports deployment strategies such as A/B testing, canary releases, and blue-green deployments, which are crucial for testing new versions of services with minimal risk to the production environment. Additionally, the service mesh supports multitenant and multimesh deployments, enabling complex organizational structures and large-scale systems to be managed effectively.

OSSM implements end-to-end encryption, service-to-service authentication, and robust access control policies to enhance security across the microservices architecture. It supports mTLS for secure service communication and includes features for managing certificate revocation lists (CRLs) and Online Certificate Status Protocol (OCSP) stapling for external traffic, ensuring that credentials can be effectively managed and revoked.

Strengths
OpenShift Service Mesh provides deep integration with the OpenShift Container Platform, enhancing ease of use and operational efficiency in Kubernetes environments. It builds on Istio, adding enhanced features, such as automated sidecar injection and simplified management through the OpenShift console. OSSM integrates with Red Hat’s ecosystem, providing robust support for enterprise deployments, including simplifying the deployment and management of service meshes with the OpenShift Service Mesh Operator, streamlining installation and updates. Additionally, OSSM offers superior observability through integrated tools like Kiali for service topology visualization and Jaeger for tracing, helping developers optimize microservices performance and troubleshoot issues effectively. It supports complex enterprise environments with features tailored for multitenancy and advanced traffic management, making it particularly suitable for large-scale, hybrid cloud deployments.

Challenges
OpenShift Service Mesh tends to lag behind its upstream counterpart, Istio, in feature updates, which can delay access to the latest enhancements for users. Its deep integration with the OpenShift platform, although beneficial for OpenShift users, might limit its flexibility and increase complexity for those not fully committed to the Red Hat ecosystem. OSSM introduces an additional layer of operational complexity to the Kubernetes environment, requiring a deeper understanding and management of its components like Istio and Envoy. Managing multiple control planes in a multicluster setup can be challenging, requiring careful configuration and maintenance to ensure stability and performance. The overhead introduced by sidecar proxies, which handle all service-to-service communications, can impact performance, potentially increasing latency, which could be a concern for organizations with limited DevOps resources or those requiring highly optimized performance.

Purchase Considerations
Red Hat offers OSSM as part of all self-managed or fully managed subscription levels of Red Hat OpenShift, catering to different deployment environments such as on-premises, cloud, or hybrid setups.

OSSM’s use cases include multitenant and multimesh deployments, making it suitable for hybrid cloud environments where different teams or organizations manage various applications.

Radar Chart Overview
Red Hat is a Challenger in the Maturity/Feature Play quadrant. OpenShift Service Mesh, based on Istio, enhances microservices management within the OpenShift Container Platform, positioning it as a pivotal tool for operational insight, control, and security. With deep integration into OpenShift, it simplifies operations while leveraging open source projects like Kiali for observability, Jaeger for distributed tracing, and Prometheus for monitoring, delivering a robust solution for operating cloud-native applications at scale.

Solo.io: Gloo Mesh

Solution Overview
Founded in 2017, Solo.io provides cloud-native application networking solutions, including API gateways and service mesh technologies. Originally launched in early 2019 under the name Service Mesh Hub before being rebranded, Gloo Mesh is a comprehensive service mesh management solution that simplifies the adoption and management of service meshes across multiple clusters and cloud environments.

A critical component of the Solo Gloo Platform, Gloo Mesh enhances the capabilities of open source Istio by providing a centralized control plane for managing multiple Istio service meshes across diverse Kubernetes environments, including multicluster and multicloud deployments. Gloo Mesh offers two main editions: Gloo Mesh Core, which simplifies Istio adoption with SLA-backed support, intuitive lifecycle management, and enhanced observability features, and Gloo Mesh Enterprise, which extends these capabilities with advanced features for large-scale, complex deployments.

Gloo Mesh Enterprise enhances open source Istio with several key capabilities. It introduces multitenant workspaces, enabling organizations to define fine-grained access control and editing permissions based on roles, which facilitates collaboration across teams. Additionally, it provides a unified API for managing both east-west and north-south traffic, simplifying the configuration of rules and policies. Gloo Mesh Enterprise also offers advanced observability features, including service topology graphs, TCP metrics, and full-screen visibility into network traffic, latency, and speeds across clusters.

Gloo Mesh Enterprise streamlines the integration of VMs into Istio environments, reducing the time and code required. It supports advanced policy controls for traffic management, security, and resiliency, enabling features like content-based routing, mTLS, and fault injection. Gloo Mesh Enterprise also provides long-term support for Istio versions, FIPS compliance for secure production environments, and integration with developer tools for an improved developer experience.

Strengths
Gloo Mesh is an enterprise-grade service mesh management plane built on top of Istio and Envoy. It provides a centralized control plane for managing multiple Istio service meshes across diverse Kubernetes environments, including multicluster and multicloud deployments. Gloo Mesh Enterprise enhances open source Istio with key capabilities like multitenant workspaces for fine-grained access control, a unified API for managing east-west and north-south traffic, advanced observability features like service topology graphs and TCP metrics, and streamlined integration of virtual machines into Istio environments. It also offers advanced policy controls for traffic management, security, resiliency, long-term Istio version support, FIPS compliance, and integration with developer tools, making it an option for large-scale, complex service mesh deployments.

Challenges
While robust and feature-rich, Gloo Mesh faces specific technical limitations and challenges compared to other service meshes. One notable challenge is its complexity, which can lead to a steep learning curve for new users, particularly when managing multicluster and multicloud environments. Gloo Mesh’s reliance on Istio means it inherits some of Istio’s limitations, such as resource overhead from sidecar proxies and potential performance impacts, although it offers sidecarless options with significant reductions in resource overhead. Integration with existing non-Kubernetes infrastructure and workflows can also be less seamless compared to platform-agnostic service meshes, necessitating careful planning and execution to avoid disruptions. Furthermore, while Gloo Mesh provides advanced features like multitenancy and zero-trust security, these capabilities may be overkill for simpler use cases. As an alternative, Gloo Mesh Core provides an initial adoption path for customers that don’t initially require advanced features but also allows them to expand as their use cases and requirements evolve.

Purchase Considerations
Solo.io offers per-cluster licensing and CPU-capacity-based pricing, with enterprise-grade support included for customers purchasing Gloo Mesh Enterprise.

Gloo Mesh’s key use cases include advanced traffic management, multicluster service mesh management, secure service-to-service communication with mutual TLS, and support for large, complex deployments across cloud and on-premises environments.

Radar Chart Overview
Solo.io is a Leader in the Maturity/Platform Play quadrant. Gloo Mesh is a leading enterprise-grade service mesh management solution that simplifies the adoption and management of service meshes across multicluster and multicloud environments. The solution addresses the complexities associated with open source service meshes, providing intuitive lifecycle management, enhanced observability, and SLA-backed support, making it an option for enterprises looking to secure, scale, and simplify their application networking environments.

Traefik Labs: Traefik Mesh

Solution Overview
Founded in 2016, Traefik Labs simplifies the deployment and management of APIs and microservices, enhancing enterprises’ cloud-native experience. Released in September 2019, Traefik Mesh is a lightweight and non-invasive service mesh solution built on top of the popular Traefik Proxy, allowing users to use the same routing and load balancing capabilities for external and internal traffic management.

Designed to provide visibility and management of traffic flows within Kubernetes clusters, Traefik Proxy supports a host/node architecture instead of a sidecar proxy for simplicity and resource conservation. Instead of using sidecar proxies, Traefik Mesh deploys a DaemonSet with the proxy on each node of the Kubernetes cluster. The proxies on each node act as intermediaries to handle routing and management of traffic flows between services within the cluster.

Traefik Mesh is opt-in by default, which means existing services are unaffected until explicitly added to the service mesh rather than being automatically injected into the application. A dedicated mesh controller pod handles the configuration parsing and deployment to the proxy nodes. This non-invasive approach means Traefik Mesh does not modify existing Kubernetes resources or require any changes to application code. The control plane runs natively in a clustered mode using the Raft algorithm, while the data plane can scale horizontally by adding more proxy nodes to handle increased traffic loads.

Traefik Mesh offers features like circuit breaking and access control for inter-service communication. Capabilities like rate limiting, in-flight request limiting, and Let’s Encrypt certificate management are distributed across the cluster. Additionally, encrypted communication between nodes, separation of control and data planes, and support for authentication mechanisms, such as LDAP, JWT, and OAuth, provide security. A cluster-wide dashboard is available for visualizing nodes, configurations, traffic metrics, and error reporting.

Strengths
Traefik Mesh provides a non-invasive host/node proxy architecture that avoids sidecar proxies, reducing operational overhead while delivering essential service mesh benefits like visibility, reliability, and security. This lightweight approach simplifies the service mesh without modifying applications or resources. Traefik Mesh offers robust traffic management features like circuit breaking, rate limiting, and automated updates without restarts, integrating seamlessly with Traefik Proxy to leverage its advanced routing and load-balancing capabilities. Its observability is enhanced through integrations with Prometheus and Grafana, providing comprehensive metrics and tracing.

Challenges
Traefik Mesh’s host/node proxy model, which avoids sidecar proxies, can lead to less granular control over individual service instances and potentially higher latency due to centralized routing on each node. It lacks support for virtual machines and advanced features found in more mature meshes, such as comprehensive service-to-service authentication (mTLS), multicluster capabilities, and a centralized control plane for easier management and configuration. The absence of these features can limit its suitability for highly complex microservices environments requiring robust security and fine-grained traffic management capabilities. Users requiring a unified control plane spanning clusters, clouds, and meshes should look elsewhere. Furthermore, while still supporting Traefik Mesh, Traefik Labs is shifting its focus to API management, which may impact innovation.

Purchase Considerations
Traefik Mesh is free as an open source service mesh. Traefik Labs offers premium, subscription-based support for enterprise-grade Traefik Proxy deployments. The company also incorporates Traefik Mesh within Traefik Enterprise, a commercial, enterprise-grade API gateway and ingress controller solution.

Traefik Mesh’s use cases include implementing traffic management patterns, enhancing observability, and securing inter-service communication through access controls, all without sidecar proxies or modifying application code.

Radar Chart Overview
Traefik Labs is an Entrant in the Maturity/Feature Play quadrant. Traefik Mesh is a lightweight, non-invasive service mesh solution designed for Kubernetes environments. It follows a host/node proxy architecture, integrating with Traefik Proxy to leverage its advanced routing and load-balancing capabilities without the need for sidecar proxies. While lacking comprehensive service-to-service authentication and a centralized control plane, it offers a simplified approach to enhancing observability, implementing traffic patterns, and securing inter-service communication within Kubernetes clusters. However, Traefik Labs is shifting its focus away from service mesh to API management technology.

6. Analyst’s Outlook

The service mesh market is experiencing rapid growth, driven by the increasing adoption of microservices architectures and cloud-native technologies and the need for enhanced security, observability, and traffic management in complex distributed systems. Key players in the market are focusing on product innovation and strategic mergers and acquisitions to expand their market presence, as evidenced by Hashicorp’s acquisition by IBM and Isovalent’s acquisition by Cisco. Additionally, integrating service mesh with other technologies like Grafana, Kubernetes, and Prometheus is expected to provide a more comprehensive set of features, further driving adoption.

Organizations can prepare for the future of service mesh by first assessing their current microservices architecture and identifying specific pain points that a service mesh can address, such as service discovery, security, and observability. Staying informed about emerging trends, such as the advent of Kubernetes-native sidecars and the integration of eBPF for improved performance and reduced complexity and the potential shift towards sidecarless models, can help organizations make strategic decisions about their service mesh deployments. By focusing on these areas, organizations can ensure they are well-positioned to leverage the full potential of service mesh technology as it continues to evolve.

Here are five steps and relevant considerations that can help you make an informed decision:

1. Assess your Microservices Architecture

• Complexity and scale: If your organization is managing a complex microservices architecture with a significant number of services that communicate with each other, a service mesh can provide essential capabilities such as service discovery, load balancing, and secure service-to-service communication.
• Cross-platform and multicloud deployments: For organizations deploying services across multiple platforms or cloud providers, a service mesh can offer a unified and consistent way to manage service communications across these environments.

2. Identify Pain Points

• Service discovery and communication: Are your services struggling to discover and communicate with each other efficiently? A service mesh can automate and simplify these processes.
• Security concerns: Are you looking to enhance the security of your microservices communications with features like mutual TLS and fine-grained access control? A service mesh can provide these capabilities out of the box.
• Observability and monitoring: Is gaining visibility into your microservices’ behavior and performance challenging? A service mesh offers built-in observability features, including metrics, logs, and tracing.

3. Evaluate Operational Readiness

• Complexity and overhead: Implementing a service mesh introduces additional complexity and operational overhead. Organizations should evaluate their readiness to manage these aspects, including the potential performance impact of sidecar proxies.
• Skill set and learning curve: Assess whether your team has the necessary skills or the willingness to learn how to deploy, configure, and maintain a service mesh. Consider the availability of training resources and community support.

4. Review Commercial and Open Source Options

• Feature set: Compare the features offered by different service mesh solutions–including traffic management, security, and observability–to ensure they meet your specific requirements.
• Performance and scalability: Consider the performance impact and scalability of the service mesh solutions you are considering. Evaluate them based on real-world use cases similar to your own.
• Community and vendor support: Assess the strength and activity of the community for open source service mesh solutions. Consider the level of support and services offered for vendor solutions.

5. Conduct a Proof of Concept

• Test in a controlled environment: Before committing to a service mesh, conduct a proof of concept test in a controlled environment. This allows you to assess the benefits and challenges firsthand, ensuring that the service mesh meets your expectations and integrates well with your existing infrastructure.

By carefully evaluating these aspects, organizations can determine whether a service mesh aligns with their architectural needs, operational capabilities, and specific challenges, ensuring a successful implementation that delivers the intended benefits.

To learn about related topics in this space, check out the following GigaOm Radar and Sonar reports:

• GigaOm Radar for API Management
• GigaOm Radar for Application and API Security (AAS)
• GigaOm Sonar for Container Networking

7. Methodology

*Vendors marked with an asterisk did not participate in our research process for the Radar report, and their capsules and scoring were compiled via desk research.

For more information about our research process for Key Criteria and Radar reports, please visit our Methodology.

8. About Ivan McPhee

Formerly an enterprise architect and management consultant focused on accelerating time-to-value by implementing emerging technologies and cost optimization strategies, Ivan has over 20 years’ experience working with some of the world’s leading Fortune 500 high-tech companies crafting strategy, positioning, messaging, and premium content. His client list includes 3D Systems, Accenture, Aruba, AWS, Bespin Global, Capgemini, CSC, Citrix, DXC Technology, Fujitsu, HP, HPE, Infosys, Innso, Intel, Intelligent Waves, Kalray, Microsoft, Oracle, Palette Software, Red Hat, Region Authority Corp, SafetyCulture, SAP, SentinelOne, SUSE, TE Connectivity, and VMware.

An avid researcher with a wide breadth of international expertise and experience, Ivan works closely with technology startups and enterprises across the world to help transform and position great ideas to drive engagement and increase revenue.

9. About GigaOm

GigaOm provides technical, operational, and business advice for IT’s strategic digital enterprise and business initiatives. Enterprise business leaders, CIOs, and technology organizations partner with GigaOm for practical, actionable, strategic, and visionary advice for modernizing and transforming their business. GigaOm’s advice empowers enterprises to successfully compete in an increasingly complicated business atmosphere that requires a solid understanding of constantly changing customer demands.

GigaOm works directly with enterprises both inside and outside of the IT organization to apply proven research and methodologies designed to avoid pitfalls and roadblocks while balancing risk and innovation. Research methodologies include but are not limited to adoption and benchmarking surveys, use cases, interviews, ROI/TCO, market landscapes, strategic trends, and technical benchmarks. Our analysts possess 20+ years of experience advising a spectrum of clients from early adopters to mainstream enterprises.

GigaOm’s perspective is that of the unbiased enterprise practitioner. Through this perspective, GigaOm connects with engaged and loyal subscribers on a deep and meaningful level.

10. Copyright

© Knowingly, Inc. 2024 "GigaOm Radar for Service Mesh" is a trademark of Knowingly, Inc. For permission to reproduce this report, please contact sales@gigaom.com.