Build vs. Buy Guide for AI Infrastructure

Agent usage is scaling fast. But agents that you can't see, control, or maintain aren't agents that will survive in production. The need for a comprehensive solution to unify, run, and maintain agents at scale is fast becoming essential infrastructure for enterprise teams.

This pull towards operational rigor has been the driving force behind a variety of new off-the-shelf and point solutions like Claude Managed Agents, LangChain, and more.

Most teams aren't questioning if they need some kind of centralized solution to run their agents. The question most teams are asking is "should I buy it or build it myself?"

In a world where anyone can build anything, you might be tempted to think that the build vs. buy conversation has been largely reset. Like the cloud era before it, how you build agent tooling has very little to say about whether or not you should.

Even in the age of vibecoding, the question of build vs. buy (if nothing else) still boils down to a few fundamental questions. When does it make sense to buy a solution vs. building in-house? And how do you start to answer those questions for AI infrastructure?

Let's find out.

Four factors that shape the build-vs-buy call.

The decision to "build vs. buy" is nothing new. It's a question that's vexed engineering orgs for decades. But in the context of a nascent and rapidly evolving technology, the question feels like it's getting fuzzier.

Nearly every engineer we've talked to who's tried to build a control layer for their agents thought it would be an easy problem to solve — right up to the point when they hit their first issue.

While the spirit of Silicon Valley would tell you to build everything and pay nothing, there's a reason best-in-class solutions still win the day for many enterprise organizations. Now does that mean there's no value to building? Of course not! Even in the largest organizations, there are still plenty of reasons to build. The question is simply "what and when?"

In this article, we'll explore a simple framework to examine the build vs. buy question as it relates to your agent infrastructure journey.

• Cost
• Complexity
• Expertise
• Time-to-value

First things first. Cost is obviously a critical factor in any build vs. buy conversation. (In some situations, it might be the only consideration.) Which means how we calculate our costs is also critically important.

At first glance, cost considerations might seem like a clear winner for the build camp. But ask yourself this question: how many enterprise-grade solutions have you built that turned out to be materially more cost-efficient over the long term?

When it comes to building, it's not simply a question of subscription fees or contract terms. Unlike an out-of-the-box solution, the costs aren't bundled into a simple subscription fee with a sliding scale. Some won't even materialize until after that solution is already in deployment — and by that point your development time is already a sunk cost.

• Hosting/subscription fees
• Cost to build
• Cost to maintain
• And, of course, the new elephant in the room — token costs throughout the lifecycle.

Most of the build cost lives below the waterline.

If you're considering a home-built solution for your agent infrastructure, here are a handful of helpful questions to ask:

• What's your budget for the run and control layers of your stack?
• How many hours will it take to build a new agent management platform in-house?
• How many hours do you estimate will be required to maintain that solution as the AI landscape evolves? (What was visionary yesterday could be obsolete tomorrow.)
• Will it cost more to hire and/or train engineers to build this part of your stack or leverage a managed tool out of the box?
• What opportunities would you lose by redirecting resources to build out specialized tooling in-house?

What's more, a home-built solution will often be less reliable than something that's maintained as a standalone solution. And you'll be on the hook for every one of those unplanned incidents.

In the early months of the AI boom, analysts assumed the AI race would be a winner-takes-all market. Now, a few years into the journey, the opposite is proving to be the case. Far from rallying around a single solution, agents are becoming increasingly heterogeneous — integrating a bevy of models and tool calls to tune agents for production use cases.

What's more, the agent landscape is still evolving by the day. New agent builders, observability solutions, security platforms — and tools to lash it all together — appear and disappear seemingly overnight.

And as agent sprawl takes hold in the enterprise, each new agent deployment (seen and unseen) is compounding the complexity of the problem to solve.

The question in that scenario isn't whether you can build something that works today. The question becomes whether or not you can maintain what you built tomorrow and the next day. And don't forget, each time you need to rebuild your system for the latest product evolution, that's another engineering cost that will need to be calculated into your cost-benefit analysis — whether you budgeted for it or not.

Now you might be reading this and thinking "this is a very one-sided argument." And that's probably true.

But there are still very legitimate reasons to build in 2026. For example, if your organization has scaled so much that it already depends largely on in-house technology to power internal technology, building may be your only solution. This is primarily true for big tech and some regulated industries, where their technical challenges extend beyond typical cloud software deployment.

In that case, it's unlikely you would find an off-the-shelf solution that could integrate easily with all your systems right out of the box. And depending on the scope of the request, some software providers won't be ready to commit the hours to make it happen.

In that case, your build vs. buy decision becomes pretty simple.

But remember, the longer you run in any one direction, the harder it becomes to unwind at an operational level. So, this is one question you'll absolutely want to consider early in your journey. If you need to build, start building. But if you need to buy, bite the bullet and do it sooner rather than later.

Engineering resources are an obvious consideration in any build vs. buy discussion — but resourcing isn't only constrained by available hours. It's also constrained by available expertise.

When most teams approach the question of resources, what they're often asking is "are we capable of building this technology internally?" And that's not a bad question to ask. But the more salient question is not "are you capable," but instead, "are you the best team for the job?" Or to put it a different way, "can your engineers deliver more value somewhere else?"

The principle is simple: build where you can create unique value, and buy where you can't.

Again, there's no one-size-fits-all answer here. For example, if your team is already building enterprise infrastructure at scale, this might be a no-brainer for you. But if you're in a consumer industry and primary deliverable is a model for transactional data, building a centralized control layer for agents might not be the best fit.

Here are some questions to consider when it comes to AI infrastructure:

• Do you know what you need today and how to build it?
• Do you think you'll still know what you need tomorrow?
• Will you have the time to maintain it efficiently without impacting other core competencies?
• Are you an expert at organizing credentials, access control, and observability?
• What will happen to your infrastructure if an engineer leaves?

If there's one thing AI coding tools have wrought on the world, it's an overabundance of confidence. Fear of new problems isn't a good reason not to build. But it is a reason to ask if it's really where you want your team spending their time.

If the answer is yes, full speed ahead! If it's no, you made a hard decision early — and that's better than the alternative.

Most infrastructure is more akin to a cost-center than a value creator (at least on paper), so unless you can do it better or substantially cheaper, it's usually more impactful to reserve engineering resources for core competencies wherever possible.

Sometimes the most important resource in any organization is time. That's no less true in your build vs. buy discussion.

The question here isn't simply, "how quickly can we build this?" The question is also "how quickly can we get our team onboarded and realizing value?" Is it a complex architecture that needs to cover multiple models, tools, and use-cases simultaneously? Or is it a simple dashboard you can spin up in a couple hours?

Again, buying is often the easier solution here — but not always. Sometimes extensive security reviews, long RFP processes, or arduous budget discussions can slow the development of necessary infrastructure — particularly if that infrastructure is fairly simple.

Still, time-to-value isn't a single moment. It's a series of moments. There's the required time to stand up a solution. But there's also the time required to identify and connect the right data sources, the time to integrate the right observability and evaluations for each use case, the time to assign the right credentials, and eventually (hopefully) the time to train your domain teams to actually use it.

Your first home-built go-around is unlikely to be as dialed in as the average off-the-shelf solution. It will need to be fine-tuned, the UI adjusted, and some components rebuilt while deprecating others. And each setback is a change that has to be communicated and managed with the team who's using the system.

This goes back to scale. At a small scale, that's probably okay. At a very large scale — not so much. And when everyone is rushing to build the next great agent first, time is a resource that's becoming increasingly expensive to lose.

While purchasing a best-in-class solution is often the right call for a lot of enterprise teams, that doesn't mean it's the right answer for every enterprise team. Or that it's the right answer forever.

If your agent sprawl is relatively limited, you might be fine building a platform to manage agents internally for a time. Or if you're looking for a few quick wins, you might be fine defaulting to a basic point solution like Claude Managed Agents and accepting lock-in for a time.

What's important is that your build vs. buy conversation remains an open discussion. Anytime you're building as a strategy, you need to be ready and waiting for the moment when building stops making sense.

And if you're a healthy and growing business, more often than not, that time is going to come a lot sooner than later.

You can stand up or cancel an enterprise contract pretty easily. But you can't claw back the hours before you realize you need one.

Costs are complicated

Don't take every line item at face value. Just because something seems affordable doesn't mean that it is. And just because something seems expensive doesn't mean it's not the most cost-efficient solution to the problem.

Complexity goes both ways

Only you know what matters to you and your platform goals. If interoperability with cloud infrastructure is top of your list, buying is usually the simplest solution. But if you're already building in-house in a variety of adjacent functions — whether that's your data platform, application observability, or something else your engineers cooked up — building might be your only path forward.

Speed matters more than perfection

It's easy to get caught up comparing dollars and demos, but sometimes the right solution is just the one you can get off the ground first. And with boards everywhere starting to demand results from AI investments, the solution that works out of the gate might be an easy answer.

What you can do isn't always what you should do

The world is full of amazing engineers. And with the right resources, the majority of "10x" coders could build just about anything. But build vs. buy should never come down to what you can build. It's simply a question of what you should build.