Here's what we see happen at most companies: the eval problem lands on an engineering leader's desk, and they think "yeah, we can build this in-house." Next thing you know, there's a 2-FTE project that'll "ship in six months." Spoiler alert: it doesn't. The problem isn't that the team isn't capable. The problem is that nobody's actually thinking about this as a capital allocation decision. They're thinking about it as a technical one. But it's not. When you're spending $300K to $500K in salary costs plus infrastructure over two years, or dropping $150K annually on an external partner, you're not picking between engineering approaches. You're deciding whether to burn your budget on something the company doesn't have competitive advantage in, or put it elsewhere.
This shift in perspective: from "can we build it" to "should we build it" changes the entire conversation. Suddenly TCO matters. Risk tolerance matters. What your team actually needs matters. Whether you can "probably figure it out" stops being relevant.
Why the Build vs Buy Framework Matters
Every engineering org we work with has this bias baked in: build everything. It feels like you own it. It feels cheaper at first. And yeah, you're developing something. But let's be honest about what you're actually doing. If you pull 2.5 engineers off the product roadmap for eighteen months, those people aren't shipping features customers pay for. They're not fixing the garbage technical debt in your inference pipeline. They're not firefighting in production. And in the startup world, that's a massive cost that doesn't show up in spreadsheets as clearly as salary line items do.
A specialized agency? You know exactly what you're paying. You know what you're getting. You can point to it, measure it, show your board the tangible deliverable. With internal builds, scope creeps, timelines slide, and suddenly you've burned $800K and have nothing to show but a half-finished system that nobody wants to maintain.
The Build vs Buy Decision Matrix
So what actually matters here? There's no one-size-fits-all answer. But we can break down the signals.
The Numbers Game: Build vs Buy Over Three Years
Let's actually work through some real-world numbers. These aren't hypothetical. We've seen this pattern play out dozens of times.
Build Scenario: Do It Yourself
| Cost Category | Year 1 | Year 2 | Year 3 | 3-Year Total |
|---|---|---|---|---|
| Engineering (2.5 FTE @ $200K loaded) | $500K | $300K | $150K | $950K |
| Infrastructure (compute, storage) | $40K | $80K | $150K | $270K |
| LLM API costs for eval runs | $30K | $60K | $120K | $210K |
| Tooling, testing, deployment | $20K | $20K | $30K | $70K |
| Year Total | $590K | $460K | $450K | $1.5M |
But here's what you're not seeing on the balance sheet: opportunity cost. Those 2.5 engineers pulling 18 months on evals? That's not just $500K in salary. That's engineers who could've been shipping features. That's maybe $250-300K in ARR you don't generate because your product team is waiting on eval infrastructure. Add that in and we're talking closer to $800K in actual cost.
Buy Scenario: Bring in a Partner
| Cost Category | Year 1 | Year 2 | Year 3 | 3-Year Total |
|---|---|---|---|---|
| Eval system design & build | $150K | N/A | N/A | $150K |
| Monthly managed service | $72K | $72K | $72K | $216K |
| Infrastructure (partner handles) | Included | Included | Included | Included |
| LLM costs (on your account) | $30K | $60K | $120K | $210K |
| Year Total | $252K | $132K | $192K | $576K |
Okay. So $576K over three years versus $1.5M for the build scenario. That's roughly $924K in direct cost difference, but the real story is more interesting:
- Your engineers stay focused: Those 2.5 FTE aren't stuck maintaining eval infrastructure. They're shipping things. That's probably $250-300K in value you're not leaving on the table.
- Speed to market: Partner can have this running in 12-16 weeks. Building it internally? We're talking minimum 6 months, usually longer. Earlier deployment means earlier insights.
- You don't own the operational debt: No one's on call for eval system failures. No one's debugging at 2am because the eval pipeline is hanging. That's not free.
- You get people who do this for a living: Building evals is specialized. You're not hiring to do it. You're borrowing the expertise.
When Build Makes Sense (And When It Doesn't)
Real talk: there are scenarios where building is the right call. But they're fewer than most engineering leaders think.
1. Your Domain is Actually Weird
Maybe you're doing something genuinely unique. Financial derivatives modeling with real-time pricing constraints. Biotech simulations. Something where standard eval patterns don't apply. In those cases, yeah, you probably need to build. A generic eval framework isn't solving your problem. But be honest about this. Most companies think their use case is unique and it isn't. You're building text-generation evals or classification evals or hallucination detection. Those are solved problems.
2. Evals Are Actually Your Product
If you're a benchmarking company or your competitive edge is literally your evaluation methodology, then sure: you need to own the system. Your evals are proprietary. But again. Most companies aren't here. If evals are just a part of how you build better models, that's different.
3. You've Got the Bench and the Conviction
Do you have a senior engineer who actually knows this space? Not someone who thinks they do. Someone with real eval experience. Can you commit 18 months without the project getting deprioritized when there's a production fire? Can your exec team actually stomach the opportunity cost? If any of these is a "maybe," you're taking on way too much risk.
When Buying is the Right Move
And honestly, for most companies, buying is right. Here's why.
1. Speed Matters More Than You Think
If you're trying to compete in AI right now, 18 months is a lifetime. A partner gets you running in 12-16 weeks. That's the difference between shipping model improvements in month 2 versus month 18. Compounded across your product lifetime, that matters enormously.
2. Your Problems Aren't Special
Let's be real. You need to measure hallucination. You need quality metrics on generation. You need latency monitoring. These are the same problems everyone's solving. A partner who's built this fifty times will get you 85% of the way there in a quarter. Building it yourself? You'll spend twice as long to get to 75%.
3. Eval Engineers Are Hard to Find
This skillset's rare. Hiring someone who actually knows this space? That's a 6-month search minimum. Even then, they leave after 18 months because they find more interesting work. A partner has these people already. If someone leaves their team, not your problem.
4. Your Budget Won't blow up
Internal projects? They creep. You discover you need a real database. The team realizes they need monitoring. By month six you're $100K over and still shipping nothing. With an external partner you've got a fixed cost. You know what it is. You can predict what it costs next quarter.
The Hybrid Path: Start With Buy, Transition to Build
But you don't have to pick one path forever. Smart teams do this:
Bring someone in who knows what they're doing. Your team watches, learns, gets their hands dirty in the implementation. You ship evals in 12-16 weeks.
You start owning more of the operational work. Partner's still there, but in an advisory role now. Cost drops significantly. You're getting comfortable with the system without fully reinventing it.
Maybe you own it fully now. Maybe you keep the partnership running at lower cost. You've got enough knowledge to make the actual decision instead of guessing.
This works because you get speed first, learning second. You're not betting the company on an internal build. But you're also not locked in forever to an external vendor if you later decide you want to bring it in-house.
The Debt That Eats You Later
Here's something nobody talks about: eval infrastructure is boring. Nobody wants to maintain it. After month twelve when it's "done," nobody's excited about keeping it updated. So the code sits. Dependencies get old. You're not upgrading the LLM APIs when OpenAI changes their format. New engineers come on and can't figure out why the eval pipeline is hanging. By year three, the system that was "cheaper to build" is actually worse than useless. It's a liability.
A partner? That's their job. They care about keeping it working. You don't. And that difference compounds. We've seen companies with internal eval systems from 2023 that they literally can't maintain anymore because the knowledge left with the engineer who built it. That "cheaper" build decision? It costs them $2-3M to rebuild when they finally get tired of it failing.
How to Present This to Your Leadership
When you're trying to get buy-in from your board or your CFO, don't talk about engineering. Talk about money.
Where you land depends on your situation. Series A and early Stage B? Buy all day. You're competing on speed. Established company with $100M+ ARR and really weird requirements? Maybe you build. But if you're in the middle (and most companies are), you should be spending your time and money on things only you can do. Evals aren't that.
This Actually Matters More Than You Think
Here's the thing. Your eval decision isn't just about infrastructure. It's about whether you ship model improvements in 6 weeks or 16 weeks. It's about whether your engineering team is working on things customers pay for or working on maintenance. It's about whether you get to three model iterations by year-end or you're still trying to launch the first one.
The winners in AI right now? They're not the teams with the most sophisticated evaluation systems. They're the teams that got evals out in month 2, iterated twelve times, and shipped five different model versions while someone else was still building infrastructure. They made the buy decision and kept moving.