There are a lot of questions a startup has to answer when making decisions about its cloud infrastructure. Private cloud or public cloud? Amazon Web Services, Google Compute Engine or Rackspace? DynamoDB or open source Cassandra?And after all those questions are finally answered, prudent CTOs might find themselves asking them again as new products hit the market and hourly rates continue to fall.
This is why one investor and adviser told Parse.ly Co-founder and CTO Andrew Montalenti that, when it comes to the cloud, “life is a series of ever-changing alternatives.”
Montalenti, whose startup provides analytics to some of the biggest web publishers around, shared that insight and more about the economics of cloud computing on the Structure Show podcast this week. Here are some highlights of that interview, but you’ll want to listen to the whole thing for more details on what Parse.ly does and which open source technologies it uses to do it. (And, for a detailed take on the company’s cloud computing history and plans, you can read this recent story, too.)
Saying ‘No, thank you’ to colocation
“Basically, there’s no good reason anymore, at least not from a price-performance standpoint that I can find, to run your own data center,” Montalenti said. Even though, he acknowledged later, “When you walk through the equations, there might be some ways that you can find running your own colo will save some money on some metrics.”
In fact, he recently advised another startup CTO concerned that his company’s cloud costs had hit $20,000 a month that squeezing more savings out of the cloud provider (via Amazon Web Services reserved instances, for example) is a better option than putting servers in a colocation space. Economies of scale are there if you’re running a “robo data center,” he said, but probably not when you’re running a few dozen servers.
“You’re not going to actually save money today if you bring it in-house, you just think you are,” Montalenti told his peer. (Not everyone agrees with this sentiment, however.)
Keeping tabs on cloud prices
“[H]onestly, this ecosystem has gotten so complicated that any startup that’s running any system at scale really needs to have someone who’s monitoring the ecosystem to really understand where we can get the best bang for our buck over time,” Montalenti said.
To solve this problem at Parse.ly, he hired a “devops guy” who is tasked with keeping tabs on what’s available where, and for how much. Parse.ly, for example, is most concerned with the price per gigabyte of RAM per month, something that used to cost about $40 with Rackspace and now costs around $33, he said. It’s about $10 with AWS and $13 with Google Compute Engine.
“[T]he prices are dropping so fast, and the question is, ‘Have we already hit a price-drop plateau, or are they going to continue to drop?’” he said. “And the truth is that prices aren’t really dropping for running your own data center.”
Cloud computing is great, lock-in isn’t
“I basically hate lock-in with a passion. So as a result, all the Amazon services that are highly Amazon-specific, I tend to shy away from,” Montalenti explained. “If it’s not built on open source technology, I’ll tend to say that it’s sort of off limits for us to use, at least in production.”
The one exception he noted is Elastic MapReduce, one of AWS’s big data services. Building and maintaining Hadoop clusters is hard, he said, but “using EMR is just so damn easy.”
Parse.ly used an open source service called Libcloud to move workloads between Rackspace data centers earlier this year, he noted, and is going to rely on it again when the company ports a large portion of its Rackspace workloads to AWS. Although services like Amazon DynamoDB might work great, Montalenti said, “You don’t want to couple your tools to any one provider.”