Should You Build Your AI Feature or Buy It?

The economics changed

Building AI capability used to mean training models, which meant a data science team. Now it usually means calling an API with good prompts, retrieval and evaluation — which a competent development team can do.

That has shifted the build-or-buy line considerably towards building, particularly where the AI is one step inside a larger workflow.

Buy when

• The problem is standard: transcription, translation, general document extraction, meeting notes
• A mature product exists with a real customer base in your sector
• You need it working in weeks rather than months
• The volume is modest enough that per-unit pricing is comfortable
• Nobody internally will own a bespoke system

Build when

• The AI step sits inside a workflow specific to your business
• Data residency or confidentiality requirements rule out the available products
• Product pricing becomes uneconomic at your volume
• You need it embedded in your own interface rather than in someone else's
• The output must feed systems no product integrates with

The most common trigger we see is the fifth one. The AI part works fine in the product; getting the result into the operational system is the problem, and that is a build.

The hybrid nearly everyone ends up with

Buy the model capability from a provider, build the workflow, evaluation and interface around it. That is what most custom AI projects actually are — not model training, but careful assembly.

It gives you control of the parts specific to your business and someone else's investment in the parts that are not.

Cost comparison, honestly

A sensible sequence

Buy something to establish whether the capability helps at all. If it does and the limits bite — pricing, integration, quality on your data — build the version that fits.

That sequence costs a few months of subscription and removes most of the risk from the build decision.

Frequently asked questions