In 2026, a CTO walks into a room with seven AI vendors clamoring at the door, eight open-source frameworks, and a board asking why "we can't just build it ourselves." The honest answer — yes for a few things, no for most — needs a framework, not a vibe. This is ours.
Start with the moat
The first question is not "can we build it?" The first question is: does this capability differentiate us in our market? If the answer is no, you should not be building it. Buy or assemble it from off-the-shelf parts. Spend your engineering budget on the things that do.
Three categories, three default answers
- Infrastructure (vector DBs, eval harnesses, observability): assemble. Use the best available open-source or commercial pieces. The competitive advantage of writing your own vector store is approximately zero.
- Domain logic (your underwriting model, your medical triage flow, your customer-segment-aware agent): build. This is where your moat lives. No vendor will build it for you to your standards.
- Horizontal AI features (chat, summarization, generic search): buy. The frontier labs and incumbents will out-invest you on these forever.
The cost of "build" is rarely the engineering
Teams underestimate two costs of building: the cost of the team you don't have to hire if you buy, and the cost of the eight months you spend building before you ship. We have watched companies build their own RAG framework for fourteen months while their competitors shipped on commercial tools and won the market. The build was technically excellent. It was strategically catastrophic.
The cost of "buy" is rarely the license
Teams underestimate two costs of buying: vendor lock-in (your data, your prompts, your fine-tunes) and the long tail of integration work. A vendor's $50K/year license can become a $400K/year integration project within eighteen months. Read the contract. Ask about export. Ask the reference customer how migration would go.
Assemble: the third path most teams ignore
There is a third option: assemble. Take three or four best-in-class commercial pieces (model API, vector DB, eval platform, observability) and write the thin layer of domain glue between them. This is what most successful AI products in 2026 actually look like. The IP is in the glue.
A four-question test
- Does this capability differentiate us in our market? (If no — don't build.)
- Will the best vendor for this category invest more than us in it over the next three years? (If yes — don't build.)
- Can we ship a competitive version in under four months? (If no — buy or assemble.)
- Do we have one engineer who is excited to own this for two years? (If no — don't build.)
Buy the boring parts. Build the parts that win you the deal. Assemble everything in between.
What we'd do differently
Five years ago we built more. Today we assemble more. The reason is that the commercial ecosystem has matured: there are now genuinely good vector databases, eval platforms, and agent runtimes you can buy for less than the cost of a single engineer. The bar for "build" has gone up because the alternative has gotten better. Not because building is wrong — because what counts as differentiated is now narrower than it was.