Choosing an LLM for a Product: Quality, Cost, Latency and Privacy

Benchmark your own task

A model that performs well on a public benchmark may still be a poor fit for your product. Build a small evaluation set from real, permissioned examples and score the qualities that matter: factuality, instruction following, structured output, tool use and language coverage.

Include difficult cases and examples where the correct answer is to ask a question or decline to act.

Compare the full operating cost

Measure end-to-end latency, input and output token usage, retries, caching and human review. Consider availability, rate limits, data retention terms, regional requirements and the cost of changing providers.

• Use the smallest model that meets the quality bar.
• Keep model configuration separate from product logic.
• Set timeouts, budgets and fallback behaviour.
• Review provider terms before sending sensitive data.

Keep the product replaceable

Use an internal interface for prompts, tools and model settings so you can test alternatives without rewriting the application. Version prompts and evaluation datasets. A good model decision is evidence-based and revisited as usage changes.