# Stateless Serving Function

> Exports models as pure stateless functions for production serving

- **Category**: Machine Learning
- **Subcategory**: Serving and Operational Resilience
- **Canonical URL**: https://designpattern.fyi/machine_learning/stateless-serving-function/

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## Description
**Intent**: Make models suitable for production-scale serving by exporting them as pure, stateless functions that can handle many concurrent, independent prediction requests reliably.

**Context**: The way a model is trained—as a program with internal state in a script or notebook—isn't directly suited to handling many concurrent, independent prediction requests reliably at production scale.

**Solution**: Export the trained model as a pure, stateless function—given an input, it always returns the corresponding output with no dependency on state from previous calls. This lets it be wrapped behind standard web-scale serving infrastructure and replicated freely. Many identical instances can run behind a load balancer, handling requests independently and concurrently.



## Use Cases
- Real-time, online prediction serving
- High-throughput web services
- API endpoints for model inference
- Any production serving requiring horizontal scaling






## Trade-offs


### Advantages

- Enables horizontal scaling through load balancing

- Handles concurrent requests independently

- Standard web-scale serving infrastructure compatibility

- Simplifies deployment and autoscaling




### Considerations & Drawbacks

- Not suited for workloads needing session/sequential state

- Requires explicit external state store for stateful needs

- Not ideal for extremely heavy per-call computation

- May need infrastructure for stateless serving







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**Reference**: [Original Source](https://github.com/GoogleCloudPlatform/ml-design-patterns)