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

Summary

Deploy an AI workflow runner built with Gin 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 Gin: 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.

Prerequisites

• A Gin project that builds and starts with the documented commands (CGO_ENABLED=0 go build -o app ./... then ./app on the ubuntu-24.04 template (go.sum committed for a reproducible build))
• 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

Framework and Workload

• Framework: Gin
• Workload: AI workflow runner

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: CGO_ENABLED=0 go build -o app ./... then ./app on the ubuntu-24.04 template (go.sum committed for a reproducible build). The server must bind 0.0.0.0 on PORT.
2. Implement the Pattern on PostgreSQL: 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 Keep private data in an unpublished bucket.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).
5. Verify: Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks.

Success Checks

• App responds on its public URL: ample canary gin patterns
• Generated-downloads self-test: 401 without token, export=ok rows=3.

Limitations

• Verified only on the ubuntu-24.04 template at s-1vcpu-1gb with the example fixture; other sizes, templates, and Gin major versions are not verified.
• The AI workflow runner-specific behavior is application code and was not separately tested.

Cost Estimate

• Currency: USD
• Monthly Amount: $10.00
• Components:
  • App server: size s-1vcpu-1gb, quantity 1.0, monthly amount $5.00
  • Managed PostgreSQL database: size s-1vcpu-1gb, quantity 1.0, monthly amount $5.00.

Examples

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

Next Actions

1. Browse the catalog index
2. Search published recipes by intent, stack, and constraints
3. Prepare a side-effect-free deployment plan for an authorized project
4. Read the existing agent authentication setup
5. Browse AI workflow runner
6. Browse Gin
7. Browse Generated Downloads
8. Browse Deploy AI App