How to choose a language model (LLM) for your company

Asking which model is best has no good answer, because it is the wrong question. A better one is: which model is best for this task, at this budget, and with these privacy needs. The answer then differs from project to project — and often from step to step within a single project.

Four things that decide it

• Quality on the task: hard reasoning suits a large model, but sorting or extracting data is fine on a smaller one.
• Cost: a larger model costs several times more per call. At thousands of requests a day, that is not a detail.
• Latency: a user waiting on a reply feels every second. A smaller, faster model sometimes wins even at slightly lower quality.
• Privacy and data: where your data may go, and what the provider does with the inputs, is a question for legal, not just for engineering.

Large or small?

In practice a mix works best. A cheap, fast model does the rough work — it sorts, searches, prepares the ground. The large, capable model steps in only for the final stage, where its cost is justified. That cuts spend dramatically without losing quality where it matters. The trick is not to burden the large model with what a small one handles just fine.

Do not go by leaderboards alone

Benchmarks are a useful signal, but nothing replaces a test on your own data. A model that tops the tables can trail a cheaper alternative on your kind of text. So on every project we build a small eval — a set of real examples with expected outputs — and decide by that, not by the provider marketing.

To lock in or not

Committing to a model is not a marriage for life. We build so the provider can be swapped without rewriting half the app — the model call is one bounded step, not something scattered across the code. When a better or cheaper option arrives, we move in a day, not a month. You can see what we work with in our technologies overview.

Choosing and deploying a model is part of AI support. When you need a flow that ties several models and tools together, see the piece on AI orchestrators.