LitServe
Python framework on FastAPI for custom AI inference servers with batching, streaming, and GPU autoscaling.
About
Lightning AI built LitServe as a thin serving layer on top of FastAPI for teams that want a custom inference server without adopting a heavyweight MLOps stack. A model gets wrapped in a LitAPI class with setup, decode, predict, and encode hooks; the framework then supplies the parts plain FastAPI lacks for ML traffic: dynamic request batching, token and byte streaming, multi-worker and multi-GPU autoscaling, and optional OpenAI-compatible endpoints, which Lightning benchmarks at more than 2x plain FastAPI throughput. Because the serving loop is arbitrary Python, the same pattern serves LLMs, vision models, audio, RAG pipelines, agents, and classical ML from PyTorch, JAX, or TensorFlow, and roughly 100 community templates cover common models. Installation is pip install litserve under Apache 2.0 with no restriction on commercial use; servers run on a laptop, an on-prem GPU box, or any cloud, and a one-command deploy to Lightning's managed cloud exists for teams that would rather not run infrastructure.
Should you use LitServe?
Pick it when
Pick LitServe when you want a custom Python inference server for an LLM, vision, audio, or RAG model with batching, streaming, and multi-GPU workers, but without adopting a full MLOps platform or learning Kubernetes.
Look elsewhere when
Skip it if you need multi-model pipelines with independent autoscaling across a cluster, where Ray Serve fits, or Kubernetes-native rollouts, where KServe fits. For pure LLM throughput, a dedicated engine like vLLM is the better core.
Alternatives to LitServe
- FastAPI
Fewer abstractions and no extra dependency, fine for light CPU models, but dynamic batching and multi-GPU worker management are yours to build.
- BentoML
Adds a standardized build artifact and Docker packaging for multi-model systems, with a larger framework to learn than LitServe.
- Ray Serve
Scales composed models across a multi-node cluster with fractional GPUs, at the cost of running and learning Ray.
- vLLM
Purpose-built LLM engine with PagedAttention and continuous batching, less flexible for non-LLM models but tuned for LLM throughput.
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Details
- Category
- AI Deployment & MLOps
- Price
- Freemium
- Platform
- Hybrid
- Difficulty
- Intermediate (3/5)
- License
- Apache-2.0
- Added
- Aug 24, 2026