Production Deployment Patterns for AI Apps

AI Deployment Needs Release Discipline

Deploying AI applications is different from deploying deterministic features because model behavior can shift with prompts, retrieval, tool calls, context, and user input. Production deployments need staged rollouts, monitoring, and rollback paths.

Deployment Patterns

• Use feature flags for prompts, models, retrieval settings, and tool access.
• Run eval gates before promotion to production.
• Roll out to internal users, then small cohorts, then wider traffic.
• Use fallback models, fallback prompts, and safe degraded modes.
• Monitor latency, cost, error rate, tool failures, refusal rate, and user feedback.
• Prepare incident response for unsafe outputs, data leakage, or runaway tool use.

Rollback Must Be Easy

Version prompts, policies, schemas, retrieval configs, and model settings so teams can quickly revert when behavior regresses.