Stateless service launch

1. Confirm all stateful data resides in managed databases, caches, queues, or object stores; no persistent data stored on instance disks.
2. Validate endpoints and background jobs are idempotent; retries do not create duplicate side effects.
3. Configure connection pools, timeouts, and circuit breakers via configuration; monitor exhaustion thresholds.
4. Implement readiness, liveness, and startup probes covering dependencies; include health endpoints for platform checks.
5. Ensure logging emits correlation IDs, request IDs, and trace context for distributed debugging.
6. Run load tests across scale-up and scale-down scenarios; capture resource usage and adjust autoscaling policies.
7. Execute chaos tests (pod kills, node drains, network partitions) to confirm recovery without data loss.
8. Document operational runbook: dependency SLAs, scaling limits, failure modes, and recovery procedures in the service catalog.

Prerequisites

• Runtime platform supporting autoscaling and health probes (Kubernetes, serverless, etc.).
• Observability stack capable of tracing, metrics, and alerting across distributed services.

Pitfalls

• Hidden session data on local disk or in-memory caches without external persistence.
• Ignoring database connection limits when scaling horizontally.
• Treating caches as authoritative sources rather than performance layers.

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