Build vs. Buy: A Practical Framework for AI Features
Not every AI feature is worth building custom, and not every one is well served by an off-the-shelf tool either. The decision is worth making deliberately rather than defaulting to whichever option is more exciting to build.
Start with how central the feature is to the product
If the AI capability is a supporting feature — summarization, tagging, a search improvement — an existing API or off-the-shelf tool is usually the right call. The differentiation isn't there, so the engineering effort is better spent elsewhere. If the AI capability is the product, or is close to the core value proposition, custom work earns its cost because you need control over behavior, cost, and iteration speed that a generic tool won't give you.
Run the cost math before committing, not after
Per-request API costs for AI features scale with usage in a way flat-rate software licenses don't. A feature that looks cheap at pilot scale can look very different at real usage volume. Before committing to an approach, it's worth modeling cost at expected scale under a few different usage assumptions — not just at today's volume — because the breakeven point between "buy the API" and "invest in a cheaper custom pipeline" moves a lot depending on that number.
Consider what you're actually building expertise in
Building custom means your team accumulates real operational knowledge of prompt behavior, model quirks, and failure modes specific to your use case — which compounds over time if the feature is core to the product. Buying means that expertise stays with the vendor. Neither is wrong, but it's worth being honest about which one your team actually wants to be responsible for maintaining a year from now.
A reasonable default
When in doubt: buy or use an existing API for the first version, instrument it well, and only invest in custom infrastructure once real usage data shows where the off-the-shelf option is actually falling short — on cost, latency, or capability. That data is worth far more than a guess made before the feature has any real users.