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Evaluating LLM vendors before production

Leaderboard scores do not predict whether your feature survives a bad Tuesday afternoon in production.

Score what your users actually trigger

Build evaluation sets from real prompts: support replies, summarization, code assist, or onboarding flows. Include edge cases—empty input, very long context, non-English, and adversarial prompts. Run them across every vendor you might use, with the same system prompts and tools.

Operational criteria matter more than IQ

Measure p95 latency under your expected concurrency, token pricing at your traffic shape, rate limits, and regional availability. Ask about data retention, training opt-out, and whether prompts can be logged for your compliance team.

Design for failover on day one

Abstract the provider behind an interface. Keep a secondary model or degraded mode (shorter answers, cached responses) when the primary API errors or slows. Incidents are when single-vendor bets hurt most.

Security and governance checklist

Review SOC reports, subprocessors, and whether customer data can be isolated per tenant. For healthcare or financial clients, document what leaves your VPC and what stays in your RAG index only.

Contract and cost guardrails

Set per-tenant budgets, alert on spend anomalies, and cap max tokens per request. Vendor choice is often a finance decision once you exceed pilot traffic—plan that conversation early.

See also our AI launch checklist for rollout steps after you pick a vendor.