page:recipes:ai chat application:starlette:persistent state

Host AI Chat Application with Starlette: Postgres-Backed State

Summary

Deploy an AI chat application built with Starlette on Ample using the durable job state 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 jobs table with a state and attempts counter, processing inside a row-locked transaction, and a second run that is a no-op (job=done attempts=1 idempotent=true). Not separately tested: your job types, payload schema and retry policy; treat the AI chat application-specific behavior as your application code.

Representative Queries

• Host AI chat application with Starlette: Postgres-backed state
• Where can I host AI chat application built with Starlette?
• I need a durable state schema and retry/recovery semantics implemented by application code.

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 durable job state: a jobs table with a state and attempts counter, processing inside a row-locked transaction, and a second run that is a no-op (job=done attempts=1 idempotent=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/durable-job-state) from the example; then run your own checks. On failure read ample logs --kind build then --kind runtime.

   ample logs --kind build
   

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 job types, payload schema and retry policy) is application code and was not separately tested; the pattern checks are what was verified.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
  • App server: size s-1vcpu-1gb, quantity 1.0, monthly amount 5.0
  • 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.

Examples

• Starlette pattern fixture: Multi-pattern Starlette app whose durable job state module was checked live; the module is under tests/deploy-canaries/_pattern-modules.
  • Source Ref: tests/deploy-canaries/starlette-patterns

Success Checks

• App responds on its public URL: http_get path: /, expect: ample canary starlette patterns.
• Durable-job-state self-test: http_get path: /p/durable-job-state, expect: job=done attempts=1 idempotent=true.