Ray Serve

open_source

Ray Serve is an open-source, scalable model serving framework built on Ray for deploying ML models, LLMs, and multi-model pipelines in production.

AI Models & Infrastructure

LLM Developer Tools

AI Frameworks

About

Ray Serve is a flexible, high-performance model serving framework that is part of the broader Ray ecosystem. Designed for production ML infrastructure, it allows data scientists and ML engineers to deploy machine learning models — including large language models (LLMs) — as scalable HTTP services with minimal boilerplate code. Built on Ray's distributed computing primitives, Ray Serve supports horizontal scaling, resource-aware scheduling (including GPU acceleration), and fault-tolerant deployments. It is equally suited for simple single-model endpoints and complex multi-model pipelines, including retrieval-augmented generation (RAG) workflows, multi-modal AI pipelines, and Model Context Protocol (MCP) server deployments. Key capabilities include online serving, offline batch inference, distributed training integration via Ray Train, and seamless composition of multiple models into a single pipeline. Ray Serve natively handles async requests, supports Python-first deployments, and integrates with popular ML frameworks like XGBoost, PyTorch, and HuggingFace. Ray Serve is ideal for ML platform teams at startups and enterprises who need a programmable, scalable serving solution that goes beyond what simple REST wrappers or managed inference endpoints offer. It is open-source and can be self-hosted on any cloud or on-premises infrastructure, with a managed option available through Anyscale.

Key Features

Scalable Online & Batch Inference: Deploy models as horizontally scalable HTTP services or run high-throughput offline batch inference pipelines using the same Python-first API.
LLM Serving & RAG Pipelines: First-class support for large language model deployment, including building and serving retrieval-augmented generation (RAG) applications at scale.
Multi-Model Pipeline Composition: Chain multiple models or preprocessing steps into complex pipelines — including multi-modal AI workflows — with built-in request routing and resource management.
MCP Server Deployment: Deploy and scale Model Context Protocol (MCP) servers in Streamable HTTP mode, enabling tool-using agents to connect to scalable AI backends.
Resource-Aware Scheduling & Fault Tolerance: Leverage Ray's scheduling to allocate CPUs, GPUs, and custom accelerators per deployment, with automatic fault recovery and replica management.

Use Cases

Deploying large language models (LLMs) as scalable REST API endpoints for production applications.
Building and serving retrieval-augmented generation (RAG) pipelines with distributed ingestion and online query handling.
Running high-throughput offline batch inference on large datasets using GPU clusters.
Composing multi-model AI pipelines — such as preprocessing, embedding, and generation steps — into a single scalable service.
Deploying Model Context Protocol (MCP) servers to power tool-using AI agents at scale.

Pros

Python-Native & Framework Agnostic: Works with any Python ML framework (PyTorch, HuggingFace, XGBoost, etc.) and requires no special DSL — just standard Python classes and decorators.
Production-Grade Scalability: Built on Ray's distributed runtime, enabling true horizontal scaling across multiple nodes and GPU clusters without re-architecting code.
Open Source & Self-Hostable: Fully open-source with an active community, deployable on any cloud or on-premises infrastructure with no vendor lock-in.
Unified Ecosystem: Deep integration with Ray Data, Ray Train, and Ray Tune creates a seamless end-to-end ML platform from training to serving.

Cons

Steep Learning Curve: Requires familiarity with distributed systems concepts and the Ray ecosystem; less approachable than simpler single-model serving tools for beginners.
Operational Overhead: Self-hosting a Ray cluster adds infrastructure complexity compared to fully managed model serving platforms.
Overkill for Simple Use Cases: For teams needing to serve a single lightweight model, Ray Serve's distributed architecture may introduce unnecessary complexity and resource usage.

Frequently Asked Questions

Ray Serve is used to deploy and scale machine learning models and LLMs as production HTTP services. It supports both real-time online inference and high-throughput batch inference, and enables building complex multi-model pipelines including RAG systems.

Yes, Ray Serve is fully open-source under the Apache 2.0 license and free to self-host. A managed cloud version is available through Anyscale for teams that prefer not to manage Ray clusters themselves.

Ray Serve is distinguished by its deep integration with Ray's distributed computing primitives, making it uniquely suited for complex multi-model pipelines, GPU cluster scaling, and end-to-end ML workflows. It is more flexible and programmable than many purpose-built serving frameworks.

Yes. Ray Serve has first-class support for LLM serving, including deploying HuggingFace Transformers, vLLM, and other LLM backends. It also supports building and serving RAG pipelines and deploying MCP servers for tool-using agents.

Ray Serve can be deployed on any cloud provider (AWS, GCP, Azure) or on-premises infrastructure. It runs on Linux and macOS and integrates with Kubernetes via KubeRay for container-orchestrated deployments.