AI Adoption · Written and maintained by Haink’s AI adoption team · Updated July 2026 · 11 min read

Build vs Buy AI: The Strategic Decision (Not the Software One)

Ask “should we build or buy our AI?” and you’ll usually get a debate about engineering effort. That’s the wrong altitude. At the strategy level, build-vs-buy is a question about where your advantage comes from — and the honest answer is almost never one or the other.

Build vs buy AI is the decision, taken per use case, to develop an AI capability in-house, purchase the outcome from a vendor, or partner to co-build it — decided on how differentiating the capability is and whether you hold a data advantage. Buy the commodity; build (or co-build) the thing that makes you different; expect to end up hybrid. The rest of this guide is how to draw those lines without fooling yourself.

The market has already voted — read what it voted on

The direction of travel is unambiguous. In Menlo Ventures’ 2025 State of Generative AI in the Enterprise, 76% of enterprise AI solutions were bought rather than built — up sharply from 53% a year earlier, as total enterprise AI spend tripled to roughly $37B.

It’s tempting to read that as “the debate is over, just buy.” It isn’t — because it matters what got bought. The surge is overwhelmingly in commoditized capability: general copilots, transcription, generic assistants, horizontal tools where one vendor’s model is much like another’s. That’s exactly the category where building your own would be a waste. What the number does not say is that companies are buying their competitive edge off the shelf, because that product doesn’t exist. Nobody sells you the capability that makes you different from your competitors — by definition, you can only build that. So the real reading is sharper: buy the commodity aggressively, and reserve build for the narrow set of things that are genuinely yours.

It’s a spectrum, not a switch

“Build or buy” is a false binary. In practice there are four positions, and the useful ones are in the middle:

The consensus across recent analyses is that the hybrid middle — buy the foundation, build what’s differentiating on top — wins more often than either pure position. Treat “build” and “buy” as the ends of a dial you set per use case, not a single company-wide lever you throw once.

The two questions that actually decide it

Strip away the noise and every build-vs-buy call comes down to two axes: how differentiating is this capability, and do we have a data advantage in it? Plot a use case on those two and the answer usually falls out.

Two guardrails on this grid. First, “differentiating” means differentiating to your customers, not interesting to your engineers — a fascinating internal tool that no customer will ever feel is a commodity in disguise. Second, a data advantage you could build is not one you have; if the proprietary data isn’t usable yet, the honest classification is “buy for now, revisit when the data is ready.”

The costs each side hides

Most build-vs-buy comparisons are dishonest in the same way: they compare the full cost of building against the sticker price of buying. Both sides carry costs that don’t show up in the first spreadsheet.

The single most common estimating error is treating “buy” as cheap and instant. A purchased AI tool that isn’t integrated into the workflow and fed the right data returns almost nothing — the buy still lands you with the integration, data and adoption work, just not the model-building. Run both options through the same full-cost lens; see how much AI adoption costs for the complete picture, including the run bill that outlives the build.

What foundation models changed

The whole debate shifted under everyone’s feet in the last few years, and plenty of build-vs-buy advice hasn’t caught up. It used to be that “build” meant training a model — enormously expensive, reserved for a handful of companies. That is rarely what building means now. You almost never build a model from scratch; you build on a bought or open foundation model.

So the modern “build” is mostly building the layers around a purchased model: the application, the retrieval and data pipeline, the workflow integration, the guardrails and evaluation. That has two consequences. It has made building far more accessible — a small team can now build a differentiated solution on top of a frontier model — and it has made pure “buy it all” and pure “build it all” both rarer, because nearly everything real is now a hybrid: bought foundation, built specifics. When someone frames your decision as train-vs-license a model, they’re answering a question most companies no longer face.

Vendor lock-in: the buy-side risk nobody prices

Buying’s biggest long-run cost is the one least likely to appear in the pilot business case. As you integrate a vendor deeper into your workflows and data, three things quietly compound:

• Switching cost. The deeper the integration, the higher the price of ever leaving — and vendors know it when it’s renewal time.
• Roadmap dependence. Your capability advances at your vendor’s pace and priorities, not yours.
• Strategic exposure. For workflows that are your advantage, routing them through an outside model can hand a competitor visibility into how you work — the same concern that drives private and sovereign architecture for a sensitive core.

None of this means “never buy” — it means price the exit before you sign, prefer portable designs, and keep the genuinely differentiating core where you control it. A blueprint you own is one hedge here: because the design is written down and portable, it can be implemented or re-implemented by any competent team rather than living only inside one vendor’s platform.

A build-vs-buy checklist

Run a candidate use case through these. The more that lean one way, the clearer the call — and mixed answers usually point to the hybrid middle, not paralysis.

• Is this capability something customers would notice we do better — or just internal plumbing?
• Do we hold proprietary, usable data that a vendor can’t replicate?
• Is there a mature product that already does 80% of this well?
• Can we hire and keep the talent to run a build for years, not just ship it once?
• What does leaving cost — if we buy and want out in three years?
• Does the data or workflow have to stay inside our perimeter for security or compliance?
• Is speed-to-value the priority now, or long-term control?
• If we build, is it really build-on-a-foundation — or are we about to reinvent a model we could license?

When each is the right call

Buy when the capability is commoditized, a good product exists, and speed matters more than control — which describes most horizontal, general-purpose AI. Take the market’s best, integrate it well, and spend your scarce talent elsewhere.

Build (on a foundation) when AI is a genuine competitive lever, you have a data advantage a vendor can’t match, or sovereignty and deep integration rule out off-the-shelf. This is the smaller set — and the one worth owning.

Partner / co-build when the outcome must be yours but the capability isn’t in-house yet: an outside team builds it with you, ideally leaving you the design, the data and the ability to run it — not a dependency. Haink’s own delivered work sits here — for example a custom LLM ecosystem for a talent-visa platform, where the AI was the product and had to be built, versus the many commodity capabilities such a company should simply buy. Whichever way a use case falls, decide it against your value map and your priorities, not against a vendor’s pitch or an engineer’s enthusiasm.

How this differs from build-vs-buy at the software level

Two different decisions share this name, and it’s worth keeping them apart. This page is the strategy-level call — build an internal capability, buy the outcome, or partner — made per use case on differentiation and data, and it feeds your transformation strategy. One layer down sits the software-level question: for a use case you’ve already committed to, do you develop a custom LLM application or adopt an off-the-shelf tool, and how do RAG, fine-tuning and deployment factor in. That’s covered in depth in build vs buy: custom AI/LLM application or off-the-shelf — read this page to decide whether and where to build, that one to decide how.

Frequently asked questions

Should you build or buy AI?
Buy the commodity, build or co-build your differentiator, and expect a hybrid. Most enterprises now buy the majority of their AI (76% in 2025), but nobody buys the capability that makes them different — decide per use case on differentiation and data advantage.

When should you build a custom AI solution?
When the capability is strategic, depends on proprietary data or workflows, or sovereignty and integration rule out off-the-shelf. Build where AI is a competitive lever and you have a data advantage; buy elsewhere.

Does buying AI still require engineering?
Yes — buying removes the model-building, not the integration, data prep, governance and change management. A bought tool that isn’t wired into the workflow returns little.

What did foundation models change?
They moved the line: you build on a bought or open model now, not from scratch — so most real solutions are hybrids of bought foundation and built specifics.

What’s the biggest hidden cost of buying?
Lock-in and its effects — compounding usage pricing, a roadmap you don’t control, data exposure, and a switching cost that grows with integration.

How is this different from software-level build-vs-buy?
This is the strategy call (build a capability, buy the outcome, or partner) per use case; the software question of custom app vs off-the-shelf tool is one layer down, once you’ve decided to pursue the use case.