Lightning-AI/LitServe

What it solves

LitServe addresses the rigidity of existing serving tools that are often built for single model types and enforce strict abstractions. It allows developers to build custom inference servers in pure Python without needing complex MLOps glue code or configuration files, making it easier to deploy non-standard pipelines, multi-model systems, and AI agents.

How it works

Developers define an inference engine by inheriting from ls.LitAPI, where they specify how models are loaded in the setup() method and how requests are processed in the predict() method. This logic is then wrapped in a ls.LitServer, which manages the underlying performance, concurrency, scaling, and deployment. It is built on FastAPI but optimized specifically for AI workloads, offering better multi-worker handling.

Who it’s for

It is designed for AI developers who need full control over their inference logic, batching, routing, and orchestration, whether they are building chatbots, RAG systems, MCP servers, or complex multi-model pipelines.

Highlights

• Flexible Deployment: Supports any PyTorch, JAX, or TensorFlow model and can be self-hosted or deployed via Lightning AI.
• AI-Optimized Performance: Claims to be 2“ faster than plain FastAPI for AI tasks.
• Advanced Serving Features: Includes built-in support for batching, streaming, and GPU autoscaling.
• C-Suite Compatibility: OpenAPI compliant and supports the OpenAI specification.
• Broad Use-Case Support: Capable of serving everything from LLMs and multimodal models to classical ML models like XGBoost.