# Host ai chat application with Starlette: streamed responses

## Summary
Deploy an ai chat application 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 chat application-specific behavior as your application code.

## Representative Queries
- Host ai chat application with Starlette: streamed responses
- Where can I host AI chat application 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
1. **Compute**  
   - Verified  
   - Summary: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
2. **Postgres**  
   - Verified  
   - Summary: 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.
   Command: 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`.
   Command: ample logs  --kind build

## Success Checks
1. App responds on its public URL
   - Kind: http_get
   - Path: /  
   - Expect: ample canary starlette patterns
2. Streaming-response-service self-test
   - Kind: http_get
   - Path: /p/streaming-response-service  
   - Expect: 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 chat application 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
- Authoritative: true
- Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
1. **App server**  
   - Size: s-1vcpu-1gb  
   - Quantity: 1.0  
   - Monthly Amount: 5.0
2. **Managed PostgreSQL database**  
   - Size: 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. Buckets are allocation-priced per quota and not included.

## Evidence Summary
1. **Canary Run: Starlette pattern fixture deployed on Ample**; 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
   - Implementation Revision: 50dbac567d2c5cc8e55e6c748a646be0025d9cfb-dirty (CLI 0.1.21)
   - Expires At: 2027-03-20T01:21:36Z
   - Scope: checks=[