## Host ai workflow runner with FastAPI: worker execution and job state

### Summary
Deploy a ai workflow runner built with FastAPI 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 FastAPI: 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 FastAPI: worker execution and job state
- Where can I host AI workflow runner built with FastAPI?
- I need a separately runnable worker, durable job records and idempotent retries; not a managed queue.

### Resource Requirements
- primitive:compute
- primitive:postgres

### Infrastructure Requirements
1. **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.

2. **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
1. 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 (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template; the worker starts with python3 worker.py and binds no port)
2. 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
3. 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. Exit 2 means it left questions in the manifest; answer them with --answer or by editing the file.
   - **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. It is idempotent and never destructive; exit 0 means everything applied, 2 needs input, 1 a failed resource (independent siblings still proceed and re-running resumes).
   - **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; on failure read the worker service's runtime logs.
   - **Command:** `ample logs --kind runtime`

### Examples
- **FastAPI web + worker fixture**  
  A FastAPI 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.
  - **Source Reference:** tests/deploy-canaries/fastapi-worker

### Success Checks
1. **Web service responds on its public URL**
   - **Kind:** http_get
   - **Path:** /  
   - **Expect:** ample canary fastapi patterns

2. **Worker consumed the enqueued job**
   - **Kind:** http_get
   - **Path:** /p/worker-queue/status  
   - **Expect:** worker=done attempts=1 result=processed-by-worker

### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb for both services with the example fixture; other sizes, templates and FastAPI major versions are not verified.
- The ai workflow runner itself (your job payloads, retry policy and scheduling) is application code and was not separately tested; the worker-queue check is what was verified.
- The worker is a single long-running process supervised in its own microVM; there is no scheduler, cron or horizontal worker scaling in this recipe, and the check ran immediately after deploy so idle behavior of the worker VM is not verified.
- The worker service is private: it has no public URL and is reached only through the shared database; both services are placed with their database.

### Cost Estimate
- **Monthly Amount:** $15.00  
  - **Basis:** Size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty.
  - **Components:**
    - Web server (size: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00)
    - Worker server (size: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00)
    - Managed PostgreSQL database (size: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.00)  
  - **Note:** Always-on monthly price of the tested sizes for the web service, the worker service and the database; apps auto-pause when idle.

### Evidence Summary
- **Kind:** canary_run
- **Summary:** FastAPI web service plus a separate Python worker process deployed on Ample with `ample plan --write` then `ample up` (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template); the web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database.
- **Observed At:** 2026-09-20T03:56:46Z
- **Tool:** ample_up

### Last Verified At
- 2026-09-20T03:56:46Z

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