serve

Unlike single-model serving frameworks, Ray Serve is designed for building complex inference services that chain multiple models, route requests based on content, and compose outputs from several models into a final responseenabling the multi-model architectures that production AI systems increasingly require without custom orchestration code.

The framework handles production serving infrastructure: auto-scaling up or down based on request queue depth, request batching that amortizes GPU inference overhead across many concurrent requests, rolling model updates without downtime, and Python-native API that allows arbitrary business logic between model calls.

This makes it natural to implement ensemble scoring, conditional routing, A/B testing, and business rule integration within the serving layer itself rather than in a separate application layer.

ML platform teams building centralized model serving infrastructure, engineers deploying LLM applications requiring dynamic batching and GPU auto-scaling, and developers creating multi-model pipelines for recommendation, search ranking, or NLP chains use Ray Serve as their production foundation.

Its position within the Ray ecosystem integrates naturally with Ray's training, data processing, and orchestration toolsallowing teams that have standardized on Ray to use a consistent framework across the full ML lifecycle.