AWS ECS vs Fly.io — How I Deployed My Django API in 10 Minutes
A practical comparison showing how Fly.io dramatically simplifies deploying Python apps compared to AWS ECS — with setup for Django, FastAPI, PostgreSQL, Redis, and Celery.
Jul 1, 2026 · 2 min read · Som
Deploying a Django API to AWS ECS means task definitions, load balancers, target groups, VPCs, and an afternoon of YAML. On Fly.io the same app is live in about ten minutes. This is a hands-on comparison of the two — and why Fly.io wins for small-to-mid Python workloads.
Why Fly.io feels different
- Micro-VMs (Firecracker): your app runs in a real VM, not a shared runtime.
- Private networking: services reach each other over
.internalDNS, no VPC wiring. - One config file:
fly.tomlreplaces a stack of AWS resources.
| AWS ECS | Fly.io | |
|---|---|---|
| Setup | Task defs, ALB, VPC, IAM | fly launch |
| Time to first deploy | Hours | ~10 minutes |
| Rough monthly cost | ~$50–120 | ~$5–15 |
Deploying Django
A production Dockerfile plus a fly.toml with a release_command for
migrations is the whole story:
[deploy]
release_command = "python manage.py migrate --noinput"
[[services]]
internal_port = 8000
protocol = "tcp"
fly auth login
fly launch # generates fly.toml + Dockerfile
fly deploy # build, migrate, go live
FastAPI is the same flow with a Uvicorn start command instead of Gunicorn.
Postgres, Redis, and Celery
- Postgres / Redis:
fly postgres create/ a Redis app, reached over private.internalDNS — no public exposure. - Celery: run workers as an extra process group in the same app rather than a separate service.
Best practices
Use release_command for migrations, define health checks, and store secrets
with fly secrets set (never in fly.toml). Run through a short pre-deploy
checklist and the first deploy is genuinely uneventful.
See the full working setup in the companion repo: hfourtech/flyio-python-guide.