### Host AI Workflow Runner with Flask: Stored Input and Output Artifacts

#### Summary
Deploy an AI workflow runner built with Flask on Ample using the generated downloads 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, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Flask: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the AI workflow runner-specific behavior as your application code.

#### Representative Queries
- Host ai workflow runner with Flask: stored input and output artifacts
- Where can I host AI workflow runner built with Flask?
- I need an input/output artifact contract, object metadata and access boundaries.

#### Resource Requirements
- Compute
- PostgreSQL
- S3-compatible object storage

#### Infrastructure Requirements
1. **Compute**: 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**: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
3. **S3-Compatible Object Storage**: Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

#### 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)
- A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables
- An Ample account token with servers:write, databases:read, buckets: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 generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **Wire Object Storage**: Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing. Keep private data in an unpublished bucket.
4. **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.
5. **Verify**: Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.

#### 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 export formats, retention policy and entitlement model) is application code and was not separately tested; the pattern checks are what was verified.
- Region, compliance attestations and request-duration limits are unknown and not claimed.
- Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.
- Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.
- PutObject and GetObject with path-style addressing are verified; multipart upload, listing, presigned URLs and lifecycle rules are not verified.

#### Cost Estimate
- **Monthly Amount**: $10.00
  - **Components**:
    - 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
**Flask Pattern Fixture**: Multi-pattern Flask app whose generated downloads module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

#### Next Actions
- **Browse the Catalog Index**: [Catalog Index](./v1/catalog)
- **Search Published Recipes by Intent, Stack, and Constraints**: [Search Recipes](./v1/catalog/search) 
- **Prepare a Side-effect-free Deployment Plan**: [Prepare Deployment](./v1/catalog/plan)
- **Read the Existing Agent Authentication Setup**: [Existing Auth Setup](./mcp/setup)
- **Browse AI Workflow Runner**: [Browse AI Workflow Runner](./v1/catalog/nodes/workload%3Aai-workflow-runner)
- **Browse Flask**: [Browse Flask](./v1/catalog/nodes/stack%3Aframework-flask)
- **Browse Generated Downloads**: [Browse Generated Downloads](./v1/catalog/nodes/pattern%3Agenerated-downloads)
- **Browse Deploy AI App**: [Browse Deploy AI App](./v1/catalog/nodes/intent%3Adeploy-ai-app)