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GigaOm Sonar for Vector Databasesv1.01

An Exploration of Cutting-Edge Solutions and Technologies

Table of Contents

  1. Executive Summary
  2. Overview
  3. Considerations for Adoption
  4. GigaOm Sonar
  5. Solution Insights
  6. Near-Term Roadmap
  7. Analyst’s Outlook
  8. Report Methodology
  9. About Andrew Brust

1. Executive Summary

In the world of data analytics, vectors refer to data constructs that contain its most meaningful features and indicate spatial directions, dimensions, and relationships. Vector databases store, index, and query vector embeddings, which are numerical representations of various types of content, including unstructured data types such as text, images, video, and audio files. These databases also serve as vector retrieval engines, embedding queries to facilitate rapid natural language querying of enterprise data at enormous scale.

Vector embeddings are fundamental to the most progressive manifestations of artificial intelligence, including generative AI. (We use the terms vectors and embeddings interchangeably in this report.) Vector databases support this movement by providing an enterprise-certified repository of data that generative AI models can access for their content creation. These AI-powered retrieval systems are just as applicable for rapidly searching through images, video, and audio data via semantic search, similarity search, and other cognitive search capabilities. They’re also primed for making recommendations, detecting anomalies, and answering questions.

There are numerous reasons vector similarity search engines are so important to the overall landscape for both AI and data management. First, they are purpose-built to handle the deluge of unstructured data that organizations have been struggling to cope with for years. They effectively make what was previously termed “dark data” (unstructured text, audio conversations, videos, graphics, and more) immediately available to enterprise users and advanced AI models—particularly those involving generative capabilities. Their search capabilities for this–and all–organizational data are equally valuable. Unlike previous paradigms in which users relied on traditional BI tools with strict limitations about what questions could be asked and how long it took for responses, the information in these databases is almost instantly available for any relevant question.

Vector databases, with their simplified search and the expedient results they provide for it across all enterprise data, can be exceedingly useful to any user persona, from the C-suite to non-technical business users. Their capabilities are effective for customer service, BI, question answering, long-term strategic projections, and many other applications. The core benefits these supporting AI systems provide is information retrieval of unstructured data and the capability to tailor generative AI capabilities to an organization’s proprietary content. Leveraging these capabilities for customer interactions and employee productivity makes this technology compelling.

The vector database market is still developing and is not fully mature. Depending on how a vector search engine is implemented, considerable effort may be needed to deploy it for applications, such as fine-tuning language models and maximizing its value via prompt engineering. Once these implementation tasks are completed, however, vector databases can profoundly increase the productivity of users.

Since the explosion of ChatGPT, the demand for generative AI solutions and supporting AI retrieval systems has increased exponentially. Every day, vendors across specialization areas are looking to incorporate vector database and vector search capabilities into their offerings. Consequently, the movement to integrate vector storage and search capabilities into traditional solutions (like relational databases) is already afoot. Vendors in this report include those that deliver dedicated vector search engine offerings (both commercial and open source) and those that leverage the pgvector extension for the open source PostgreSQL relational database.

This is the first year that GigaOm has reported on the vector database space in the context of our Sonar reports. This GigaOm Sonar report provides an overview of the market’s vendors and their available offerings, outlines the key characteristics that prospective buyers should consider when evaluating solutions, and equips IT decision-makers with the information they need to select the best solution for their business and use case requirements.


This GigaOm report focuses on emerging technologies and market segments. It helps organizations of all sizes to understand a new technology, its strengths and its weaknesses, and how it can fit into the overall IT strategy. The report is organized into five sections:

  • Overview: An overview of the technology, its major benefits, and possible use cases, as well as an exploration of product implementations already available in the market.
  • Considerations for Adoption: An analysis of the potential risks and benefits of introducing products based on this technology in an enterprise IT scenario. We look at table stakes and key differentiating features, as well as considerations for how to integrate the new product into the existing environment.
  • GigaOm Sonar Chart: A graphical representation of the market and its most important players, focused on their value proposition and their roadmap for the future.
  • Vendor Insights: A breakdown of each vendor’s offering in the sector, scored across key characteristics for enterprise adoption.
  • Near-Term Roadmap: 12- to 18-month forecast of the future development of the technology, its ecosystem, and major players in this market segment.