Pre-Launch AI Checklist: What Teams Must Verify Before Production Deployment

Deploying an AI feature to production requires far more than a successful demo, as real traffic exposes issues like bad inputs, token spikes, and slow dependencies that test environments rarely replicate. Teams should verify reliability through fallback paths, human escalation routes, and clearly defined SLOs before exposing any system to live users. Latency must be measured end to end — covering model response time, retrieval, network overhead, and time to first token — since staging performance often understates real-world slowness. Cost controls, including token usage tracking, query caching, and model selection reviews, should be built in early rather than addressed after launch. Security measures such as prompt validation, data redaction, secrets management, and access logging are considered essential from day one, alongside post-launch monitoring to catch gaps that testing alone cannot surface.