Build vs Buy AI: The Decision Framework
Build versus buy is the question every company asks when it gets serious about AI: do we build our own system, or buy an off-the-shelf tool? It is a reasonable question, and taken as a strict either-or, it is the wrong one. Almost no company should build everything, and almost none should buy everything. The useful decision is not which side to pick. It is which parts to buy, which parts to own, and how to connect them so the whole thing works and stays yours.
Getting this wrong is expensive and common. Forrester found that 67% of software projects fail because of the wrong build-versus-buy choice (via CIO). The failures do not usually come from picking a bad tool. They come from building something generic that a vendor had already solved, or buying something for the one capability that was supposed to be the company’s advantage. The framework below is about not making either mistake.
The decision has shifted to own versus orchestrate
The sharpest reframe in the current thinking is that the question has moved from build versus buy to own versus orchestrate (HatchWorks). Most companies now land on “yes to both”: buy the heavy commodity core, build the thin layer that differentiates, and use AI to connect them. The binary was always a bit of a fiction; the modern version is a portfolio decision about which layer each capability belongs in.
The data supports the both-and answer. MIT’s 2025 research found that buying from specialised vendors and building through strategic partnerships succeeds roughly 67% of the time, while fully internal builds succeed at about half that rate (via TechAhead). Gartner expects around 70% of enterprise AI workloads to run on hybrid architectures by 2026 (via Hire Fraction). Pure-build and pure-buy are both minority strategies for a reason: each gets one thing badly wrong that the hybrid gets right.
Buy the commodity
Buy when the capability is generic, the tool is mature, and the AI does not need your proprietary data to be useful.
A meeting transcription service does not need to understand your business to transcribe a call. A general writing tool does not need your data to draft a first pass. When dozens or hundreds of companies have the same need, a vendor has already solved it, worked through the edge cases, and priced it at a scale you cannot match by building. Buying there means paying for their learning curve instead of funding your own, and it gets you a working solution in weeks rather than quarters.
The test for “buy” is simple: if the capability is not a source of competitive advantage and the data feeding it is not uniquely yours, buy it, keep it cheap, and stay ready to switch. Building commodity capability in-house is how companies spend a year rebuilding something they could have licensed on Tuesday.
Build and own what differentiates
Build, and own, when the capability is your competitive advantage and it depends on your specific data, workflows, and judgment to work.
This is the layer where a general-purpose tool cannot help you, because the whole point is that it does something only your company could do: the workflow that reflects how you actually operate, the model that learns from your proprietary data, the decision logic that encodes your hard-won judgment. Hand that to a vendor and you have outsourced your advantage and rented it back. Own it, document it, and keep it portable, and it becomes the compounding asset a competitor cannot simply subscribe to.
Note that “build” does not mean “build alone from scratch.” Partnering, co-building a custom system with a specialist while keeping the IP, outperforms both pure in-house builds and pure buying in the MIT data, precisely because it combines your domain knowledge with people who have solved the deployment problem many times. The thing that matters is not who writes the code. It is who owns the result.
The failure mode is what you cannot operate or own
Here is the part most build-versus-buy frameworks miss. In 2026 the expensive mistake is rarely picking the wrong tool. It is picking something you cannot operate, or building your advantage on something you do not own.
Buy the wrong commodity tool and switching is annoying but survivable. Rent the layer that was supposed to be your advantage, though, and you have handed a vendor the power to raise your price, throttle your capability, or disappear, on the exact part of your business you most needed to control. That is not a tooling error; it is a vendor lock-in error, and it is the one that compounds. The right question at every decision point is not only “build or buy?” but “if we buy this, can we leave it, and does it hold our advantage or just run a commodity task?”
A simple decision test
You can settle most build-versus-buy AI decisions with two questions.
First, the data test: does this capability need our proprietary data, workflows, or judgment to be valuable? If yes, it belongs in the layer you own. If no, buy it.
Second, the doubling-price test: if this vendor doubled its price or degraded tomorrow, what would it cost us to leave? If the answer is “not much,” buying is safe, because you retain the power to switch. If the answer is “more than we could stomach,” the capability is in the wrong layer, and you should either own it or architect it so the vendor underneath stays swappable.
Run every candidate capability through those two questions and the portfolio sorts itself: commodity and low-switching-cost to buy, advantage-creating and data-dependent to own, and an interface you control between your systems and any vendor so “buy” never quietly becomes “locked in.”
Own the blueprint, orchestrate the rest
The through-line is the one Thane Alaric builds every engagement on: own the blueprint, rent the factory. Own the parts that carry your advantage, the workflows, the data, the judgment, the decision logic. Rent the commodity capability and the raw models, keep them cheap, and orchestrate everything through a layer you control so nothing you rent can hold you hostage. Build versus buy stops being a fraught either-or and becomes ordinary portfolio management, once you are clear about which layer each capability belongs in.
The one thing not to do is make these calls by default, one tool purchase at a time, with no one owning the pattern. That is how a company ends up having quietly built its advantage on rented ground. Deciding it deliberately is exactly the kind of call a Head of AI owns rather than a vendor answers. The build-or-buy call gets easier once you have built a few things and know what building actually costs. The Workshop is the cheapest place to find that out.
Frequently Asked Questions
What does build vs buy mean in AI?
Build vs buy is the decision between building your own AI capability and buying an off-the-shelf tool. Taken as a strict either-or, it is the wrong framing: almost no company should build everything or buy everything. The useful decision is which parts to buy, which to build and own, and how to connect them, so the system works and stays under your control.
Should I build or buy AI?
Buy the capabilities that are generic, mature, and do not depend on your proprietary data; you would only be rebuilding what a vendor has already solved. Build and own the capabilities that are your competitive advantage and depend on your specific data, workflows, and judgment. Most companies do both, and MIT found that buying plus building through partnerships succeeds far more often than fully internal builds.
What is the difference between build vs buy and own vs orchestrate?
Build vs buy is the old binary. Own vs orchestrate is the modern version: buy the heavy commodity core, build the thin layer that differentiates, and orchestrate everything through an interface you control. It reframes the question as a portfolio decision about which layer each capability belongs in, rather than a single all-or-nothing choice.
When should you build custom AI instead of buying?
Build custom when the capability is a genuine source of competitive advantage and needs your proprietary data, workflows, or judgment to work, so a general-purpose tool cannot deliver it. Building does not mean building alone; co-building with a specialist while keeping the IP outperforms both pure in-house builds and pure buying, because it pairs your domain knowledge with proven deployment experience.
Why do so many AI build vs buy decisions fail?
Forrester found 67% of software projects fail because of the wrong build-versus-buy choice. The failures come from building something generic a vendor had already solved, or buying the one capability that was meant to be the company’s advantage. The deeper failure in 2026 is picking something you cannot operate, or renting the layer that holds your advantage and losing control of it.
How do I decide what AI to build versus buy?
Use two questions. Does this capability need our proprietary data, workflows, or judgment to be valuable? If yes, own it; if no, buy it. And if this vendor doubled its price tomorrow, what would it cost us to leave? If not much, buying is safe; if more than you could stomach, own it or architect it so the vendor underneath stays swappable.
About the Author
Daniel Förster is the Managing Partner of Thane Alaric. For over a decade he has built and run companies, served over 2,000 founders, and worked as the embedded operator (COO, CFO and CMO in function) inside founder-led businesses. He now leads Thane Alaric, where companies become AI-native the right way: own the engine, rent the models, keep the judgment that’s yours.
