page:recipes:ai workflow runner:flask:response streaming

Host AI Workflow Runner with Flask: Streamed Responses

Deploy an AI workflow runner built with Flask 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 Flask: 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 workflow runner-specific behavior as your application code.

Representative Queries

• Host AI workflow runner with Flask: streamed responses
• Where can I host AI workflow runner built with Flask?
• I need a tested end-to-end streaming transport, timeout behavior and client disconnect handling.

Resource 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.

Execution Status

• Framework: Flask
• Workload: AI workflow runner
• Release Status: Published
• Support Status: Verified
• Execution Status: Ready

Prerequisites

• A Flask project that builds and starts 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)
• 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 waitress from app.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 a streaming response service: a Server-Sent Events endpoint read incrementally. 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.
4. Verify: Fetch the live URL and the pattern self-test route(s); run your own checks.

Cost Estimate

• Currency: USD
• Monthly Amount: $10.0
• Components:
  • App Server: 1 s-1vcpu-1gb at $5.0
  • Managed PostgreSQL Database: 1 s-1vcpu-1gb at $5.0

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Flask major versions are not verified.
• The AI workflow runner itself (your event schema, reconnection strategy and any per-request duration ceiling) is application code and was not separately tested.

Examples

• Flask Pattern Fixture: Multi-pattern Flask app whose streaming response service module was checked live; the module is under tests/deploy-canaries/_pattern-modules.