Production Readiness Guide

Moving from a prototype to a production-grade application on Chutes requires attention to reliability, security, and scaling. This checklist covers the essential steps to ensure your chute is ready for the real world.

1. Reliability & Stability

✅ Handle Startup & Shutdown

Ensure your on_startup logic is robust. Pre-download all necessary models and artifacts so the first request is fast.

@chute.on_startup()
async def startup(self):
    # Fail fast if critical resources are missing
    if not os.path.exists("model.bin"):
        raise RuntimeError("Model file missing!")
    self.model = load_model("model.bin")

✅ Implement Health Checks

Define a lightweight cord for health monitoring (e.g., by load balancers).

@chute.cord(public_api_path="/health", method="GET")
async def health(self):
    if self.model is None:
        raise HTTPException(503, "Model not loaded")
    return {"status": "ok"}

✅ Graceful Error Handling

Don't let internal errors crash your service or leak stack traces to users. Wrap logic in try/except blocks and return appropriate HTTP status codes.

try:
    result = self.model.predict(data)
except ValueError:
    raise HTTPException(400, "Invalid input data")
except Exception as e:
    logger.error(f"Inference failed: {e}")
    raise HTTPException(500, "Internal inference error")

2. Performance & Scaling

✅ Concurrency Tuning

Set concurrency appropriately.

• 1: For heavy, atomic workloads (e.g., image generation) where batching isn't possible.
• High (e.g., 64+): For async engines like vLLM that handle internal batching.

✅ Auto-Scaling Configuration

Configure scaling parameters to handle traffic spikes without over-provisioning.

chute = Chute(
    ...
    min_instances=1,           # Keep one warm if low latency is critical
    max_instances=10,          # Cap costs/resources
    scaling_threshold=0.75,    # Scale up when 75% utilized
    shutdown_after_seconds=300 # Scale down after 5 min idle
)

✅ Caching

Use internal caching (LRU) or external caches (Redis) for frequent, identical queries to save compute.

3. Security

✅ Scoped API Keys

Never use your admin API key in client-side code. Create scoped keys for specific functions.

# Create a key that can ONLY invoke this specific chute
chute keys create --name "app-client" --action invoke --chute-ids <your-chute-id>

✅ Input Validation

Use Pydantic schemas strictly. Validate string lengths, image sizes, and numeric ranges to prevent DOS attacks or memory overflows.

class Input(BaseModel):
    prompt: str = Field(..., max_length=1000) # Prevent massive prompt attacks
    steps: int = Field(..., ge=1, le=50)      # Bound compute usage

4. Observability

✅ Logging

Log structured data (JSON) where possible for easy parsing. Log important events (startup, errors) but avoid logging sensitive user data (PII).

✅ Metrics

Use the built-in Prometheus client if you need custom metrics (e.g., "images_generated_total"), or rely on the platform's standard metrics (requests/sec, latency).

5. Deployment Strategy

✅ Pinned Versions

Always pin your dependencies in requirements.txt or your Image definition.

• Bad: pip install torch
• Good: pip install torch==2.4.0

✅ Immutable Tags

Don't rely on latest tags for base images. Use specific SHA digests or version tags to ensure reproducibility.

✅ Staging Environment

Deploy a separate "staging" chute (e.g., my-app-staging) to test changes before updating your production chute.

Production Checklist Summary

• Model Loading: Pre-loaded on startup, not per-request.
• Error Handling: User-friendly HTTP errors, no stack traces.
• Validation: Strict Pydantic schemas for all inputs.
• Scaling: max_instances set to protect budget.
• Security: Scoped API keys generated for clients.
• Dependencies: All packages pinned to specific versions.
• Monitoring: Health check endpoint exists and works.