Deploying FastAPI to Managed Containers

A managed container platform is the default production target for most teams because it provides routing, identity, secret injection, health management, scaling, logs, and revisions without requiring Kubernetes ownership. A correct deployment separates web and worker processes, keeps data in managed services, controls egress, runs one migration job, and proves rollback.

What will you be able to do?

• map application components to managed platform resources;
• configure private data paths, TLS, secrets, probes, and identity;
• size web/worker concurrency and autoscaling from measured load;
• deploy migrations and canaries without downtime.

What is the production topology?

Prefer private connectivity for database/cache/object storage, encrypted transport, workload identity, tightly controlled outbound access, and separate identities for API, worker, and migration job. Keep the administration plane protected by stronger authentication and network policy.

How should you deploy?

1. Provision environment through reviewed infrastructure as code.
2. Create managed PostgreSQL, backups, private network/DNS, and least-privilege roles.
3. Add optional Redis/queue only for enabled features.
4. Inject secrets/references; never bake them into images.
5. Run an expand migration as a singleton job.
6. Deploy a no-traffic revision and run smoke checks.
7. Shift a small traffic percentage; compare SLOs and AI evaluation samples.
8. Increase gradually or abort to the previous revision.
9. Complete backfills/contract migration after compatibility window.

Use startup, liveness, and readiness probes with their actual meanings. Autoscale web on concurrent requests/latency and workers on ready queue depth plus processing time. Cap maximum instances so PostgreSQL and provider quotas survive a scale-out.

How should configuration differ across environments?

Configuration values and secret references differ; the container digest does not. Use separate accounts/projects/subscriptions and data. Never test a migration or load generator against production by changing only one URL.

Why does this matter in AI-native systems?

AI latency makes request concurrency and worker queueing critical. Scale does not override provider quota or spend. Route provider egress through observable policy, keep regional data requirements explicit, and maintain a degraded mode when external models fail.

Common mistakes

• Public database/cache endpoints with password-only protection.
• Autoscaling to thousands of instances against 100 database connections.
• Web requests perform long ingestion after client disconnect.
• Production and staging share model quotas, indexes, or customer data.
• Rollback restores image but not prompt/model/index configuration.
• No cost alarm or maximum scale.

AI Pair-Programmer Prompt

Exercise

Create a deployment runbook and capacity table for 10 web instances, 20 workers, a 200-connection database limit, and a constrained model quota.

Acceptance criteria: summed pools leave operational headroom, autoscaling respects model and cost limits, data services are private, migrations are singleton, and rollback restores the complete version set.

Knowledge check

1. Why prefer a managed container platform initially?
2. Should every replica run a migration?
3. What must maximum scale protect?

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Answers

1. It supplies common operational controls without Kubernetes complexity.
2. No; use one controlled job.
3. Database connections, downstream quotas, spend, and service stability.

Completion checklist

• Network and identities follow least privilege.
• Capacity math includes every replica and dependency.
• Full-version rollback is proven.

Primary references

• Azure Container Apps
• Google Cloud Run
• AWS App Runner