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=[