Host ai chat application with Starlette: worker execution and job state

Summary

Deploy an AI chat application built with Starlette as a public web service plus a dedicated worker process on Ample. ample plan --write discovers both services and their shared database and writes ample.toml; ample up reconciles the topology: a managed PostgreSQL 16 database, a public web microVM and a private worker microVM (kind worker, no public URL), both reading DATABASE_URL. Verified on Starlette: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1). Not separately tested: your job payloads, retry policy and scheduling; treat the ai chat application-specific behavior as your application code.

Representative Queries

• Host ai chat application with Starlette: worker execution and job state
• Where can I host AI chat application built with Starlette?
• I need a separately runnable worker, durable job records and idempotent retries; not a managed queue.

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.

Framework

Starlette

Workload

AI chat application

Release Status

published

Support Status

verified

Execution Status

ready

Prerequisites

• A repository with the web app and the worker as separate service directories (the fixture uses apps/web and apps/worker), each building and starting 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; the worker starts with python3 worker.py and binds no port)
• Both services read DATABASE_URL at runtime and share one jobs table; the worker claims rows with SELECT ... FOR UPDATE SKIP LOCKED and marks them done once
• An Ample account token with servers:write and databases:write (ample up creates the database)

Tested Configuration

• Template: python-3.12
• Runtime: python
• Size: s-1vcpu-1gb
• Install: python3 -m pip install --target .ample/python -r requirements.txt
• Start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Workflow Steps

1. Plan the topology: Run the planner once. It discovers the web and worker services, infers kind = "worker" for the process without a port, declares the database each service needs, and writes ample.toml. Exit 2 means it left questions in the manifest; answer them with --answer or by editing the file.

• Command: ample plan --write .

2. Share one database: Keep a single [databases.main] with engine = "postgres", drop any per-service database the planner added, and set DATABASE_URL = { database = "main" } under both [services.web.env] and [services.worker.env]. Python services also need an explicit start command in the manifest (the fixture sets start for both services).

• Command: ample plan --offline .

3. Apply: Reconcile the whole manifest in dependency order: the database first, then both services. It is idempotent and never destructive; exit 0 means everything applied, 2 needs input, 1 a failed resource (independent siblings still proceed and re-running resumes).

• Command: ample up .

4. Verify: Fetch the web service URL and its worker status route (/p/worker-queue/status in the example) and confirm the worker processed the enqueued job; on failure read the worker service's runtime logs.

• Command: ample logs --kind runtime

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb for both services with the example fixture; other sizes, templates and Starlette major versions are not verified.
• The ai chat application itself (your job payloads, retry policy and scheduling) is application code and was not separately tested; the worker-queue check is what was verified.
• The worker is a single long-running process supervised in its own microVM; there is no scheduler, cron or horizontal worker scaling in this recipe, and the check ran immediately after deploy so idle behavior of the worker VM is not verified.
• The worker service is private: it has no public URL and is reached only through the shared database; both services are placed with their database.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.
• Region, compliance attestations and request-duration limits are unknown and not claimed.

Cost Estimate

• Monthly Amount: $15.00
• Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
• Components:
  • Web server: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00
  • Worker server: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00
  • Managed PostgreSQL database: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00
• Note: Always-on monthly price of the tested sizes for the web service, the worker service and the database; apps auto-pause when idle.

Evidence Summary

• Kind: canary_run
• Summary: Starlette web service plus a separate Python worker process deployed on Ample with ample plan --write then ample up (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template); the web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database. Pattern proof: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1).
• Observed At: 2026-09-20T03:56:46Z
• Implementation Revision: 03b6402b1b3e19178985f08ea4033804521d85d4-dirty (CLI b75e104)
• Expires At: 2027-03-19T03:56:46Z
• Scope:
  • Checks: ["/","/p/worker-queue/status"]
  • Pattern: pattern:dedicated-worker-process
  • Services: ["web","worker"]
  • Template: python-3.12
  • Tool: ample_up

Formats

• HTML
• Markdown
• JSON

Next Actions

• Browse the catalog index: GET /v1/catalog
• Search published recipes by intent, stack and constraints: POST /v1/catalog/search
• Prepare a side-effect-free deployment plan for an authorized project: POST /v1/catalog/plan
• Read the existing agent authentication setup: GET /mcp/setup
• Browse AI chat application: GET /v1/catalog/nodes/workload%3Aai-chat-application
• Browse Starlette: GET /v1/catalog/nodes/stack%3Aframework-starlette
• Browse Dedicated worker process: GET /v1/catalog/nodes/pattern%3Adedicated-worker-process
• Browse Deploy AI app: GET /v1/catalog/nodes/intent%3Adeploy-ai-app