The easiest way to deploy agents, RAG, pipelines, any model.
No MLOps. No YAML.

 

Most tools serve one model with rigid abstractions. LitServe lets you build full AI systems - agents, chatbots, RAG, pipelines - with full control, custom logic, multi-model support, and zero YAML.

Self host or deploy in one-click to Lightning AI.

 

✅ Build full AI systems   ✅ 2× faster than FastAPI     ✅ Agents, RAG, pipelines, more
✅ Custom logic + control  ✅ Any PyTorch model          ✅ Self-host or managed        
✅ Multi-GPU autoscaling   ✅ Batching + streaming       ✅ BYO model or vLLM           
✅ No MLOps glue code      ✅ Easy setup in Python       ✅ Serverless support          

Quick start • Examples • Features • Performance • Hosting • Docs

 

 

Quick start

Install LitServe via pip (more options):

pip install litserve

Example 1: Toy inference pipeline with multiple models.
Example 2: Minimal agent to fetch the news (with OpenAI API).
(Advanced examples):

Inference pipeline example

import litserve as ls

# define the api to include any number of models, dbs, etc...
class InferencePipeline(ls.LitAPI):
    def setup(self, device):
        self.model1 = lambda x: x**2
        self.model2 = lambda x: x**3

    def predict(self, request):
        x = request["input"]    
        # perform calculations using both models
        a = self.model1(x)
        b = self.model2(x)
        c = a + b
        return {"output": c}

if __name__ == "__main__":
    # 12+ features like batching, streaming, etc...
    server = ls.LitServer(InferencePipeline(max_batch_size=1), accelerator="auto")
    server.run(port=8000)

Deploy for free to Lightning cloud (or self host anywhere):

# Deploy for free with autoscaling, monitoring, etc...
lightning deploy server.py --cloud

# Or run locally (self host anywhere)
lightning deploy server.py
# python server.py

Test the server: Simulate an http request (run this on any terminal):

curl -X POST http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"input": 4.0}'

Agent example

import re, requests, openai
import litserve as ls

class NewsAgent(ls.LitAPI):
    def setup(self, device):
        self.openai_client = openai.OpenAI(api_key="OPENAI_API_KEY")

    def decode_request(self, request):
        return request.get("website_url", "https://text.npr.org/")

    def predict(self, website_url):
        website_text = re.sub(r'<[^>]+>', ' ', requests.get(website_url).text)

        # ask the LLM to tell you about the news
        llm_response = self.openai_client.Completion.create(
           model="text-davinci-003",
           prompt=f"Based on this, what is the latest: {website_text}",
        )
        output = llm_response.choices[0].text.strip()
        return {"output": output}

    def encode_response(self, output):
        return {"response": output}

if __name__ == "__main__":
    server = ls.LitServer(NewsAgent())
    server.run(port=8000)

Test it:

curl -X POST http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"website_url": "https://text.npr.org/"}'

 

Key benefits

A few key benefits:

• Deploy any pipeline or model: Agents, pipelines, RAG, chatbots, image models, video, speech, text, etc...
• No MLOps glue: LitAPI lets you build full AI systems (multi-model, agent, RAG) in one place (more).
• Instant setup: Connect models, DBs, and data in a few lines with setup() (more).
• Optimized: autoscaling, GPU support, and fast inference included (more).
• Deploy anywhere: self-host or one-click deploy with Lightning (more).
• FastAPI for AI: Built on FastAPI but optimized for AI - 2× faster with AI-specific multi-worker handling (more).
• Expert-friendly: Use vLLM, or build your own with full control over batching, caching, and logic (more).

⚠️ Not a vLLM or Ollama alternative out of the box. LitServe gives you lower-level flexibility to build what they do (and more) if you need it.

 

Featured examples

Here are examples of inference pipelines for common model types and use cases.

Toy model:      Hello world
LLMs:           Llama 3.2, LLM Proxy server, Agent with tool use
RAG:            vLLM RAG (Llama 3.2), RAG API (LlamaIndex)
NLP:            Hugging face, BERT, Text embedding API
Multimodal:     OpenAI Clip, MiniCPM, Phi-3.5 Vision Instruct, Qwen2-VL, Pixtral
Audio:          Whisper, AudioCraft, StableAudio, Noise cancellation (DeepFilterNet)
Vision:         Stable diffusion 2, AuraFlow, Flux, Image Super Resolution (Aura SR),
                Background Removal, Control Stable Diffusion (ControlNet)
Speech:         Text-speech (XTTS V2), Parler-TTS
Classical ML:   Random forest, XGBoost
Miscellaneous:  Media conversion API (ffmpeg), PyTorch + TensorFlow in one API, LLM proxy server

Browse 100+ community-built templates

 

Host anywhere

Self-host with full control, or deploy with Lightning AI in seconds with autoscaling, security, and 99.995% uptime.
Free tier included. No setup required. Run on your cloud

lightning deploy server.py --cloud

deploy.mp4

 

Features

Feature	Self Managed	Fully Managed on Lightning
Docker-first deployment	✅ DIY	✅ One-click deploy
Cost	✅ Free (DIY)	✅ Generous free tier with pay as you go
Full control	✅	✅
Use any engine (vLLM, etc.)	✅	✅ vLLM, Ollama, LitServe, etc.
Own VPC	✅ (manual setup)	✅ Connect your own VPC
(2x)+ faster than plain FastAPI	✅	✅
Bring your own model	✅	✅
Build compound systems (1+ models)	✅	✅
GPU autoscaling	✅	✅
Batching	✅	✅
Streaming	✅	✅
Worker autoscaling	✅	✅
Serve all models: (LLMs, vision, etc.)	✅	✅
Supports PyTorch, JAX, TF, etc...	✅	✅
OpenAPI compliant	✅	✅
Open AI compatibility	✅	✅
Authentication	❌ DIY	✅ Token, password, custom
GPUs	❌ DIY	✅ 8+ GPU types, H100s from $1.75
Load balancing	❌	✅ Built-in
Scale to zero (serverless)	❌	✅ No machine runs when idle
Autoscale up on demand	❌	✅ Auto scale up/down
Multi-node inference	❌	✅ Distribute across nodes
Use AWS/GCP credits	❌	✅ Use existing cloud commits
Versioning	❌	✅ Make and roll back releases
Enterprise-grade uptime (99.95%)	❌	✅ SLA-backed
SOC2 / HIPAA compliance	❌	✅ Certified & secure
Observability	❌	✅ Built-in, connect 3rd party tools
CI/CD ready	❌	✅ Lightning SDK
24/7 enterprise support	❌	✅ Dedicated support
Cost controls & audit logs	❌	✅ Budgets, breakdowns, logs
Debug on GPUs	❌	✅ Studio integration
20+ features	-	-

 

Performance

LitServe is designed for AI workloads. Specialized multi-worker handling delivers a minimum 2x speedup over FastAPI.

Additional features like batching and GPU autoscaling can drive performance well beyond 2x, scaling efficiently to handle more simultaneous requests than FastAPI and TorchServe.

Reproduce the full benchmarks here (higher is better).

These results are for image and text classification ML tasks. The performance relationships hold for other ML tasks (embedding, LLM serving, audio, segmentation, object detection, summarization etc...).

💡 Note on LLM serving: For high-performance LLM serving (like Ollama/vLLM), integrate vLLM with LitServe, use LitGPT, or build your custom vLLM-like server with LitServe. Optimizations like kv-caching, which can be done with LitServe, are needed to maximize LLM performance.

 

Community

LitServe is a community project accepting contributions - Let's make the world's most advanced AI inference engine.

💬 Get help on Discord
📋 License: Apache 2.0