Buy, Build, or Fine-Tune? A Decision Guide for Enterprise AI

One of the most expensive mistakes in enterprise AI is reaching for the wrong tool: fine-tuning a model when retrieval would do, or bolting on a SaaS API when the value was in your own data all along. Here is the decision we walk clients through.

Start with the question, not the technology

Buy (use a foundation model API as-is) when the task is general — summarization, drafting, classification on common categories — and your edge is speed, not differentiation. Lowest cost, fastest to ship.

Build with RAG (retrieval-augmented generation) when the value is in your knowledge — policies, documents, product data, tickets. Most enterprise use cases live here. You keep data current without retraining, and answers stay grounded in sources you control.

Fine-tune when you need a specific behavior, format, or domain style that prompting and retrieval can’t reliably produce — high-volume, narrow tasks where a smaller tuned model beats a large general one on cost and latency. It is the smallest slice of real problems, not the default.

The usual answer

For most enterprises, the first production system is RAG over their own data with a bought foundation model, wrapped in solid evaluation. Fine-tuning comes later, if a measured gap justifies it. Choosing deliberately — instead of by hype — is often worth more than the model choice itself.