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

Deploy an AI workflow runner built with Express 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 Express: 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 Express: stored input and output artifacts
• Where can I host AI workflow runner built with Express?
• 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.
   Status: Verified
   Canonical Path: /discover/primitive/compute

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.
   Status: Verified
   Canonical Path: /discover/primitive/postgres

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.
   Status: Verified
   Canonical Path: /discover/primitive/s3-compatible-object-storage

Prerequisites

• A Express project that builds and starts with the documented commands (npm install, npm run build --if-present, npm run start on the node-22 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
   npm install, npm run build --if-present, npm run start on the node-22 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.
   Command: ample deploy . --name --public --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.
   Command: ample logs --kind build

Limitations

• Verified on the node-22 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Express 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.

Cost Estimate

• Currency: USD
• Monthly Amount: $10.00
• Basis: Size prices from pricing.toml (loaded by the API) at build revision.
  • Components:
    • App server (s-1vcpu-1gb): $5.00
    • Managed PostgreSQL database (s-1vcpu-1gb): $5.00

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.

Evidence Summary

• Express pattern fixture deployed on Ample (npm install, npm run build --if-present, npm run start on the node-22 template); checks passed for generated-downloads and configuration-secrets. Pattern proof: 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).

Formats

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Next Actions

• Browse the catalog index
  GET /v1/catalog
• Search published recipes by intent, stack and constraints
  POST /v1/catalog/search
  Body: {"limit":5,"query":"Host ai workflow runner with Express: stored input and output artifacts"}
• Prepare a side-effect-free deployment plan for an authorized project
  POST /v1/catalog/plan
  Body: {"inputs":{},"projectId":"","recipeId":"page:recipes:ai-workflow-runner:express:artifact-storage","recipeRevision":"r1"}
• Read the existing agent authentication setup
  GET /mcp/setup
• Browse AI workflow runner
  GET /v1/catalog/nodes/workload%3Aai-workflow-runner
• Browse Express
  GET /v1/catalog/nodes/stack%3Aframework-express
• Browse Generated downloads
  GET /v1/catalog/nodes/pattern%3Agenerated-downloads
• Browse Deploy AI app
  GET /v1/catalog/nodes/intent%3Adeploy-ai-app