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

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
• Search Published Recipes by Intent, Stack, and Constraints: Search Recipes
• Prepare a Side-effect-free Deployment Plan: Prepare Deployment
• Read the Existing Agent Authentication Setup: Existing Auth Setup
• Browse AI Workflow Runner: Browse AI Workflow Runner
• Browse Flask: Browse Flask
• Browse Generated Downloads: Browse Generated Downloads
• Browse Deploy AI App: Browse Deploy AI App