MLOps: When to Build vs. Buy Your Infrastructure
Deciding to build or buy MLOps tools hinges on whether it creates a competitive advantage. For commodity tasks like experiment tracking, buying a managed service avoids locking up engineers.
Why it exists
The build vs. buy decision in MLOps is more consequential than in other software domains. A wrong 'build' decision can waste months of senior engineering time on infrastructure a service could provide in a week. A wrong 'buy' decision can create vendor lock-in that constrains your architecture for years, leading to a patchwork system that's difficult to maintain.
The mental model
Frame the decision around competitive differentiation. If a piece of infrastructure is not a core reason why customers choose your product, you should not be in the business of building and maintaining it. Your engineering resources are best spent on the models, data, and product features that make you unique.
How it works
The decision for any component comes down to four questions. First, is this a source of competitive differentiation? If not, buy. Second, what is the total cost of ownership (TCO) of building? This includes initial development, testing, documentation, ongoing maintenance, and production support—costs that are systematically underestimated. Third, does a managed service meet your actual, current requirements, not hypothetical future ones? Fourth, what is the migration cost if you choose wrong? Some components, like feature stores, are much harder to replace than others.
WHEN TO USE IT (BUILD): Building is justified only in narrow circumstances. Build when the infrastructure itself is your competitive advantage. Or, build when you have specific, non-negotiable requirements—such as unusual latency targets, extreme scale with unique cost constraints, or data residency rules—that no managed service can satisfy.
WHEN NOT TO USE IT (BUILD): Do not build commodity infrastructure. If a mature managed service exists for a component like experiment tracking, buying is almost always the correct choice. Early-stage companies in particular should default to buying, as they rarely have requirements so unique that they justify the overhead of a custom build. A common mistake is building for full control you don't actually need.
One canonical example
For experiment tracking, almost always buy. Services like Weights & Biases or managed MLflow are mature. Building a custom tracker is a waste of resources unless strict data residency rules prevent using a third-party service; even then, self-hosting MLflow is better than building from scratch. For model serving, a simple FastAPI app is a reasonable start, but it's a footgun at scale. As traffic grows, you'll spend months optimizing it instead of switching to a managed serving solution like Triton or Ray Serve that handles batching and GPU utilization out of the box.
Interview question
Which scenario most strongly justifies building a custom MLOps infrastructure component rather than buying a managed service?
- a.The company has a large team of senior engineers available with spare capacity.
- b.The component directly contributes to a unique product feature that differentiates the company in the market.Correct
- c.The estimated initial development cost of building is slightly lower than the annual subscription for a managed service.
- d.The company wants complete control over every aspect of its MLOps stack.
Why? this is the answer
The card's core mental model is to build only if the infrastructure provides a competitive advantage or meets unique, non-negotiable requirements. Option B directly reflects this principle. Option D is a common mistake, as the card warns against building for control you don't need.
Just read this? Test yourself on what you have been reading.
Read the original → mlai.qa
- #mlops
- #infrastructure
- #build vs buy
- #systems design
You just looked this up. Could you explain it out loud?
That is the part interviews actually test. Tezvyn takes questions like this one and gives you what the interviewer is really checking, the answer that lands, and the mistake that ends the conversation, in the four minutes before your next meeting.
The iPhone app is on the way
We are building it. Until it lands, nothing here is held back from you: every interview card, your saved cards, streaks and the job board all work in Safari, plus hundreds of free practice quizzes of thirty questions each. Sign in and it all carries over to the app the day it arrives.
Want it as an icon? Tap Share at the bottom of Safari, then Add to Home Screen. It opens full screen and the cards you have read stay available offline.
We are hiring for this. Open roles that interview on mlops — each one lists the topics its interview covers.
See open roles