Choosing AI models for production: a practical framework

New models ship every few months. The best choice for production is rarely the newest one—it's the one that fits your constraints and delivers consistent results.

Consider latency. Customer-facing chat needs sub-second response; internal summarization can batch and wait. Match model size and routing (e.g. fast model for routing, larger model for complex queries) to your SLA.

Consider cost. Token pricing varies widely. A cheap model that needs 10x more tokens may cost more than a premium one. Run experiments with real traffic patterns before committing.

Consider data sensitivity. PII, health data, or trade secrets may require private deployment (your VPC, your keys) rather than third-party APIs. Document where data flows and who can access it.

Consider vendor lock-in. Proprietary APIs are convenient but tie you to one provider. Open models and standard interfaces (e.g. OpenAI-compatible endpoints) give flexibility to switch or self-host later.

We help teams evaluate and select models for production: benchmarking, cost modeling, and architecture decisions so you choose confidently.