### 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.