Host AI Chat Application with Flask: Worker Execution and Job State

Deploy an AI chat application built with Flask 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 Flask: 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.

Summary

• Framework: Flask
• Workload: AI chat application
• Release Status: Published
• Support Status: Verified
• Execution Status: Ready

Representative Queries

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

Resource Requirements

• Compute
• Postgres

Infrastructure Requirements

1. Compute

   • Status: 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

   • Status: 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

1. 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 waitress from app.py reading PORT on the python-3.12 template; the worker starts with python3 worker.py and binds no port).
2. 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.
3. An Ample account token with servers:write and databases:write (ample up creates the database).

Workflow Steps

1. Plan the topology

   • Body: 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

   • Body: 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].
   • Command: ample plan --offline .

3. Apply

   • Body: 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

   • Body: 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

1. Verified on the python-3.12 template at s-1vcpu-1gb for both services with the example fixture; other sizes, templates, and Flask major versions are not verified.
2. 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.
3. 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.
4. Managed PostgreSQL 16 only; extensions, connection limits, and backup or restore procedures are not verified; apps and their databases are placed together.

Success Checks

1. Web Service Responds

   • Kind: http_get
   • Path: /
   • Expect: ample canary flask patterns

2. Worker Consumed Job

   • Kind: http_get
   • Path: /p/worker-queue/status
   • Expect: worker=done attempts=1 result=processed-by-worker

Cost Estimate

• Currency: USD
• Monthly Amount: 15.0
• Components:
  1. Web Server
     • Size: s-1vcpu-1gb
     • Monthly Amount: 5.0
  2. Worker Server
     • Size: s-1vcpu-1gb
     • Monthly Amount: 5.0
  3. Managed PostgreSQL Database
     • Size: s-1vcpu-1gb
     • Monthly Amount: 5.0

Evidence Summary

• Summary: Flask web service plus a separate Python worker process deployed on Ample with ample plan --write then ample up. The web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database.
• Implemented At: 2026-09-20T03:56:46Z

Next Actions

1. Browse the catalog index
2. Search published recipes by intent, stack, and constraints
3. Prepare a side-effect-free deployment plan for an authorized project
4. Read the existing agent authentication setup
5. Browse AI chat application
6. Browse Flask
7. Browse Dedicated worker process
8. Browse Deploy AI app