Practical overview
When it fits
Use Litserve when a standard single-model serving abstraction does not provide enough control over custom logic, multiple models, agents or non-standard pipelines.
Who it is for
- Python developers building inference APIs
- Teams serving PyTorch models, agents, RAG systems or multi-model pipelines
- Projects requiring custom request handling and serving behavior
Documented uses
- Expose custom model or pipeline logic through an inference API
- Combine multiple models or components in one serving workflow
- Add batching and streaming to model-serving implementations
- Serve AI workloads locally, on self-hosted infrastructure or through a managed deployment route
- Support multi-GPU inference workloads with scaling
Requirements & limits
The project is distributed under the Apache-2.0 license. The documented access paths include local execution, self-hosting and deployment through Lightning Cloud.
- Python is the primary implementation language.
- The framework supports PyTorch and lists FastAPI and vLLM among its ecosystems.
- Batching, routing, streaming and orchestration are explicit serving controls.
- The documented deployment approaches include local operation, self-hosting and managed Lightning Cloud deployment.
- The repository is public, active in the supplied status data and licensed under Apache-2.0.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Create and test a basic Litserve inference server
Set up a Python inference server with custom logic and test its REST endpoint.
Before you start
- Python environment with pip
- Litserve installed with pip
- A Python file defining a LitAPI implementation
Steps
Install Litserve with pip.
Define model or pipeline setup in a LitAPI class and implement its prediction method.
Create a LitServer with the API and run it on a port.
Send a JSON POST request to the documented /predict endpoint using curl.
Expected outcome
A locally running inference server that accepts a JSON request at the documented prediction endpoint.
Guide limitations
- The example uses a toy inference pipeline; production model configuration is not documented in the supplied material.
- The documented curl test assumes the server is running on 127.0.0.1:8000.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Litserve | Expose custom model or pipeline logic through an inference API | Linux, macOS, Self-hosted, Server | The project is distributed under the Apache-2.0 license. The documented access paths include local execution, self-hosting and deployment through Lightning Cloud. |
| Cloud AI SDK | Adapt and run documented model recipes for computer-vision and NLP inference. | Cloud, Server | The GitHub repository is public. Its public visibility does not establish license terms. |
| Turboquant | Replace a model’s existing inference cache with a compressed cache. | Cloud, Linux, macOS, Self-hosted | The project describes itself as open source. The README documents installation through pip or from a source checkout. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- Lightning-AI/LitServe · Official homepage title · retained in its source language