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

Summary

Deploy an AI workflow runner built with Echo 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, and a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Echo: 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 Echo: stored input and output artifacts
• Where can I host AI workflow runner built with Echo?
• I need an input/output artifact contract, object metadata, and access boundaries.

Resource Requirements

• primitive:compute
• primitive:postgres
• primitive:s3-compatible-object-storage

Infrastructure Requirements

• Compute

  • Status: verified
  • Summary: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
  • More Info

• Postgres

  • Status: verified
  • Summary: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
  • More Info

• S3-compatible Object Storage

  • Status: verified
  • Summary: Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.
  • More Info

Prerequisites

1. An Echo 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))
2. A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
3. A bucket from ample bucket create with its credentials passed as encrypted S3_* environment variables
4. An Ample account token with servers:write, databases:read, buckets:read

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

   • 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

Next Actions

• Browse the catalog index
• Search published recipes by intent, stack and constraints
• Prepare a side-effect-free deployment plan for an authorized project
• Read the existing agent authentication setup
• Browse AI workflow runner
• Browse Echo
• Browse Generated downloads
• Browse Deploy AI app

Limitations

• Verified on the ubuntu-24.04 template at s-1vcpu-1gb with the example fixture; other sizes, templates, and Echo 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.