# Host ai workflow runner with Starlette: streamed responses

Deploy a ai workflow runner built with Starlette on Ample using the streaming response service pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL. Verified on Starlette: a Server-Sent Events endpoint read incrementally through the public HTTPS gateway: five one-second chunks arrived spread over time with the first within seconds (not buffered), a 70-second stream of 36 chunks completed past common 60-second idle timeouts, and a client that disconnected after two chunks was observed and recorded by the server (disconnected=true). Not separately tested: your event schema, reconnection strategy and any per-request duration ceiling beyond the 70 seconds measured; treat the ai workflow runner-specific behavior as your application code.

## Representative Queries
- Host ai workflow runner with Starlette: streamed responses
- Where can I host AI workflow runner built with Starlette?
- I need a tested end-to-end streaming transport, timeout behavior and client disconnect handling.

## Resource Requirements
- primitive:compute
- primitive:postgres

## Infrastructure Requirements
- **Compute**: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
- **Postgres**: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.

## Prerequisites
- A Starlette project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template)
- A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
- An Ample account token with servers:write, databases:read

## Workflow Steps
1. **Build and start**  
pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. **Implement the pattern on PostgreSQL**  
The fixture's module implements streaming response service: a Server-Sent Events endpoint read incrementally through the public HTTPS gateway: five one-second chunks arrived spread over time with the first within seconds (not buffered), a 70-second stream of 36 chunks completed past common 60-second idle timeouts, and a client that disconnected after two chunks was observed and recorded by the server (disconnected=true). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **Deploy**  
Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op.  
`ample deploy . --name  --public --start "python3 run.py"`
4. **Verify**  
Fetch the live URL and the pattern self-test route(s) (/p/streaming-response-service) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.  
`ample logs  --kind build`

## Success Checks
- app responds on its public URL  
`http_get /` expects "ample canary starlette patterns"
- streaming-response-service self-test  
`http_get /p/streaming-response-service` expects "an SSE stream: data: id=, then chunk events one interval apart (read incrementally: first chunk within seconds, chunks spread over the interval), then data: done; /status?id= reports sent=N disconnected=true|false done=true|false"

## Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Starlette major versions are not verified.
- The ai workflow runner itself (your event schema, reconnection strategy and any per-request duration ceiling beyond the 70 seconds measured) is application code and was not separately tested; the pattern checks are what was verified.
- Region, compliance attestations and request-duration limits are unknown and not claimed.
- Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.
- Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.

## Cost Estimate
Currency: USD  
Monthly Amount: 10.0  
Basis: Size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty  
### Components
- App server: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0  
- Managed PostgreSQL database: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0  
**Note**: Always-on monthly price of the tested sizes; apps and databases auto-pause when idle.

## Evidence Summary
- **Canary Run**: Starlette pattern fixture deployed on Ample (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template); checks passed for streaming-response-service and configuration-secrets. Pattern proof: a Server-Sent Events endpoint read incrementally through the public HTTPS gateway: five one-second chunks arrived spread over time with the first within seconds (not buffered), a 70-second stream of 36 chunks completed past common 60-second idle timeouts, and a client that disconnected after two chunks was observed and recorded by the server (disconnected=true).  Observed At: 2026-09-21T01:21:36Z  
- **Canary Run**: The same Starlette app deployed with a --release-command migration; the marker it created was readable after activation. Observed At: 2026-09-20T02:16:51Z

## Formats
- HTML: [View on Ample](/content/recipes/ai-workflow-runner/starlette/response-streaming/index.html)  
- Markdown: [Download Markdown](/content/recipes/ai-workflow-runner/starlette/response-streaming.md)  
- JSON: [View JSON](https://api.ample.computer/v1/catalog/recipes/page%3Arecipes%3Aai-workflow-runner%3Astarlette%3Aresponse-streaming)

## Next Actions
- **Browse the catalog index**: GET /v1/catalog  
- **Search published recipes**: POST /v1/catalog/search  
- **Prepare a side-effect-free deployment plan**: POST /v1/catalog/plan  
- **Read the existing agent authentication setup**: GET /mcp/setup  
- **Browse AI workflow runner**: GET /v1/catalog/nodes/workload%3Aai-workflow-runner  
- **Browse Starlette**: GET /v1/catalog/nodes/stack%3Aframework-starlette  
- **Browse Streaming response service**: GET /v1/catalog/nodes/pattern%3Astreaming-response-service  
- **Browse Deploy AI app**: GET /v1/catalog/nodes/intent%3Adeploy-ai-app