Host AI Workflow Runner with Gin: Worker Execution and Job State

Summary

Deploy an AI workflow runner built with Gin as a public web service plus a dedicated worker process on Ample. ample plan --write discovers both services and their shared database and writes ample.toml; ample up reconciles the topology: a managed PostgreSQL 16 database, a public web microVM and a private worker microVM (kind worker, no public URL), both reading DATABASE_URL. Verified on Gin: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1). Not separately tested: your job payloads, retry policy and scheduling; treat the AI workflow runner-specific behavior as your application code.

Representative Queries

• Host AI workflow runner with Gin: worker execution and job state
• Where can I host AI workflow runner built with Gin?
• I need a separately runnable worker, durable job records and idempotent retries; not a managed queue.

Resource Requirements

• Compute
• Postgres

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.

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.

Prerequisites

• A repository with the web app and the worker as separate service directories (the fixture uses apps/web and apps/worker), each building and starting 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); the worker starts with ./worker and binds no port)
• Both services read DATABASE_URL at runtime and share one jobs table; the worker claims rows with SELECT ... FOR UPDATE SKIP LOCKED and marks them done once
• An Ample account token with servers:write and databases:write (ample up creates the database)

Workflow Steps

1. Plan the topology: Run the planner once. It discovers the web and worker services, infers kind = "worker" for the process without a port, declares the database each service needs, and writes ample.toml.

   • Command: ample plan --write .

2. Share one database: Keep a single [databases.main] with engine = "postgres", drop any per-service database the planner added, and set DATABASE_URL = { database = "main" } under both [services.web.env] and [services.worker.env].

   • Command: ample plan --offline .

3. Apply: Reconcile the whole manifest in dependency order: the database first, then both services.

   • Command: ample up .

4. Verify: Fetch the web service URL and its worker status route (/p/worker-queue/status in the example) and confirm the worker processed the enqueued job.

   • Command: ample logs --kind runtime

Examples

• Gin web + worker fixture: A Gin web app (apps/web) and a worker process (apps/worker) sharing one managed PostgreSQL database; the queue module is under tests/deploy-canaries/_pattern-modules.

Success Checks

• Web service responds on its public URL.
• Worker consumed the enqueued job.

Limitations

• Verified on the ubuntu-24.04 template at s-1vcpu-1gb; other sizes and versions are not verified.
• The AI workflow runner itself was not separately tested; the worker-queue check is what was verified.

Cost Estimate

• Currency: USD
• Monthly Amount: 15.0
• Components:
  • Web server: 5.0
  • Worker server: 5.0
  • Managed PostgreSQL database: 5.0
• Note: Always-on monthly price of the tested sizes; apps auto-pause when idle.

Evidence Summary

• Summary: Gin web service plus a separate Gin worker process deployed on Ample.
• Observed At: 2026-09-20T03:56:46Z

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