# Host ai workflow runner with Gin: streamed responses

## Summary
Deploy a ai workflow runner built with Gin on Ample using the streaming response service 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. Verified on Gin: a Server-Sent Events endpoint read incrementally through the public HTTPS gateway: five one-second chunks arrived spread over time with the first within seconds (not buffered), a 70-second stream of 36 chunks completed past common 60-second idle timeouts, and a client that disconnected after two chunks was observed and recorded by the server (disconnected=true). Not separately tested: your event schema, reconnection strategy and any per-request duration ceiling beyond the 70 seconds measured; treat the ai workflow runner-specific behavior as your application code.

## Representative Queries
- Host ai workflow runner with Gin: streamed responses
- Where can I host AI workflow runner built with Gin?
- I need a tested end-to-end streaming transport, timeout behavior and client disconnect handling.

## Resource Requirements
- **Compute**: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.  
- **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.

## Infrastructure Requirements
- **Compute**: Verified
- **Postgres**: Verified

## 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)
- An Ample account token with servers:write, databases:read

## Tested Configuration
- **Template**: ubuntu-24.04
- **Runtime**: go
- **Size**: s-1vcpu-1gb
- **Build**: CGO_ENABLED=0 go build -o app ./...
- **Start**: ./app

## 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 streaming response service: a Server-Sent Events endpoint read incrementally through the public HTTPS gateway: five one-second chunks arrived spread over time with the first within seconds (not buffered), a 70-second stream of 36 chunks completed past common 60-second idle timeouts, and a client that disconnected after two chunks was observed and recorded by the server (disconnected=true). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **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`
4. **Verify**  
   Fetch the live URL and the pattern self-test route(s) (/p/streaming-response-service) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.  
   Command: `ample logs  --kind build`

## Success Checks
- **App responds on its public URL**  
  Expect: ample canary gin patterns
- **Streaming-response-service self-test**  
  Expect: an SSE stream: data: id=, then chunk events one interval apart (read incrementally: first chunk within seconds, chunks spread over the interval), then data: done; /status?id= reports sent=N disconnected=true|false done=true|false

## Limitations
- Verified 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 itself (your event schema, reconnection strategy and any per-request duration ceiling beyond the 70 seconds measured) 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.

## Cost Estimate
- Currency: USD
- Monthly Amount: 10.0
- Authoritative: true
- Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
- Components:
  - Name: app server, Size: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
  - Name: managed PostgreSQL database, Size: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
- 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
1. **Kind**: canary_run  
   **Summary**: Gin pattern fixture deployed on Ample; checks passed for streaming-response-service and configuration-secrets.  
2. **Kind**: canary_run  
   **Summary**: The same Gin app deployed with a --release-command migration; the marker it created was readable after activation.

## Last Verified At
2026-09-21T01:15:54Z

## Formats
- **HTML**: https://ample.computer/recipes/ai-workflow-runner/gin/response-streaming
- **Markdown**: https://ample.computer/recipes/ai-workflow-runner/gin/response-streaming.md
- **JSON**: https://api.ample.computer/v1/catalog/recipes/page%3Arecipes%3Aai-workflow-runner%3Agin%3Aresponse-streaming

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