page:recipes:ai workflow runner:starlette:artifact storage

Host AI Workflow Runner with Starlette: Stored Input and Output Artifacts

Summary

Deploy an AI workflow runner built with Starlette 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, while a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Starlette: 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).

Representative Queries

• Host AI workflow runner with Starlette: stored input and output artifacts
• Where can I host AI workflow runner built with Starlette?
• I need an input/output artifact contract, object metadata and access boundaries.

Resource Requirements

• Compute
• Postgres
• 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.

Workflow Steps

1. Build and start
   pip install into .ample/python from requirements.txt, then uvicorn from run.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 Keep private data in an unpublished bucket.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.
   ample deploy . --name --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
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.

Examples

• Starlette Pattern Fixture
  Multi-pattern Starlette app whose generated downloads module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

Success Checks

1. App Responds on Public URL
   • Method: http_get
   • Path: /
   • Expect: ample canary starlette patterns
2. Generated-downloads Self-test
   • Method: http_get
   • Path: /p/generated-downloads
   • Expect: export=ok rows=3 (with ?token=...; 401 without)

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates, and Starlette 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.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
• Components:
  • App server: s-1vcpu-1gb, quantity: 1.0, monthly amount: 5.0
  • Managed PostgreSQL database: 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.