LangServe vs FastAPI: Which One Should You Choose?

Choosing between LangServe and FastAPI depends on what you're building. If you're deploying LangChain-based LLM applications, LangServe offers a streamlined path. If you need a general-purpose API framework with maximum flexibility, FastAPI is the right choice. 

Let’s break down the technical differences, use cases, and practical setup steps for both frameworks.

LangServe and FastAPI

Both frameworks are part of the Python ecosystem but built for different purposes. FastAPI has grown quickly since its release in late 2018 and is now one of the most popular frameworks for building modern Python APIs, with over 90,000 stars on GitHub. LangServe came later as part of the LangChain ecosystem, created specifically to deploy LLM applications and agents.

FastAPI is widely used in production systems and known for its speed, simplicity, and async performance. LangServe targets developers working with LangChain who need to expose chains, agents, and tools as API endpoints without adding boilerplate code.

Important note: The LangChain team now recommends using LangGraph Platform for new projects instead of LangServe. LangServe still receives maintenance updates and bug fixes but no new features. This is worth keeping in mind if you’re planning long-term deployments or need LangGraph compatibility.

What is LangServe?

LangServe is a deployment framework within the LangChain ecosystem. It wraps your LangChain runnables and chains into API endpoints using FastAPI as its core framework. It automatically manages serialization, streaming responses, and input/output validation.

You write LangChain logic, and LangServe converts it into a production-ready API with minimal configuration. It generates multiple endpoints automatically: /invoke for single requests, /batch for multiple inputs, /stream for streaming responses, and /stream_log for intermediate steps.

The framework integrates with Pydantic for data validation and includes a built-in playground UI at /playground/ where you can test your runnables with streaming output. It also supports optional tracing to LangSmith for debugging and monitoring.

What is FastAPI?

FastAPI is a modern Python web framework built on Starlette and Pydantic. It provides async request handling, automatic API documentation via OpenAPI, and type-based validation. The framework has become popular because it combines Python's simplicity with performance comparable to Node.js and Go frameworks.

FastAPI gives you complete control over your API architecture. You define routes, middleware, dependencies, and error handling explicitly. This flexibility makes it suitable for any API project, from simple CRUD apps to complex microservices handling millions of requests.

Core Differences Between LangServe and FastAPI

LangServe is built on top of FastAPI but focuses on serving LangChain applications, while FastAPI remains a general-purpose framework for any type of API.

1. Design Philosophy

FastAPI follows a general-purpose design. You can build REST APIs, GraphQL servers, webhooks, or any HTTP service. The framework provides primitives like routing, validation, and dependency injection without imposing structure on your application logic.

LangServe optimizes for one workflow: deploying LangChain runnables as APIs. It assumes you're working with chains, agents, and tools from the LangChain library. The framework generates endpoints automatically based on your runnable definitions, reducing the code you need to write by 70-80% compared to raw FastAPI. However, it's tightly coupled to LangChain and doesn't work practically without it.

2. Performance and Speed

FastAPI delivers strong performance through async/await support and Starlette's underlying architecture. Benchmarks show it handles 10,000-20,000 requests per second on standard hardware, competing with Node.js and Go frameworks.

LangServe inherits this performance baseline but adds overhead from LangChain's abstraction layers. Your actual throughput depends on the LLM calls and processing in your chains.

The framework itself doesn't create significant bottlenecks, but LangChain's dependency graph and prompt processing add latency compared to a minimal FastAPI endpoint. For most LLM applications, the API overhead is negligible compared to LLM inference time.

3. Ease of Use and Developer Experience

For LangChain projects, LangServe significantly reduces setup time. You define a runnable and add it to your server with the add_routes() function. The framework handles input schemas, output formatting, streaming responses, and even generates a playground UI automatically.

FastAPI requires more explicit code but offers better debugging and customization. The framework has extensive documentation at fastapi.tiangolo.com, a large community on GitHub, and integration examples for most common use cases. You'll find solutions faster when debugging FastAPI issues because the community is larger and the framework has been in production longer.

In short, LangServe offers convenience with some constraints tied to LangChain’s abstractions, while FastAPI provides more flexibility at the cost of extra setup work..

4. Integration and Ecosystem Support

FastAPI integrates with the broader Python ecosystem through well-maintained libraries. You can add SQLAlchemy for databases, Celery for task queues, Redis for caching, and OAuth libraries for authentication. The framework doesn't prescribe these choices, letting you build your stack based on requirements.

LangServe connects naturally with LangChain's ecosystem: vector databases like Pinecone and Weaviate, LLM providers through LangChain's unified interface, and memory stores. If your application uses these components, LangServe handles the integration patterns automatically. For components outside this ecosystem, you'll need to write custom FastAPI routes or middleware since you're still working with FastAPI underneath.

When to Use LangServe vs FastAPI

No framework is inherently better. The right choice depends on your project’s goals and technical requirements.

Best Use Cases for LangServe

Use LangServe when you're building with LangChain and need to deploy quickly. If your application consists primarily of runnables, chains, agents, and RAG pipelines, LangServe eliminates repetitive API code.

Rapid prototyping scenarios benefit most. You can deploy a conversational agent or document Q&A system as an API in under 50 lines of code. This matters for startups validating ideas or research teams testing approaches without building production infrastructure.

LangServe works well for simpler LangChain chains where you need basic API endpoints with minimal setup. The automatic endpoint generation and playground UI speed up development for straightforward use cases.

A Note on LangGraph Platform

LangChain now recommends LangGraph Platform over LangServe for new projects. LangGraph Platform provides production-grade features that LangServe lacks: built-in persistence, memory management, human-in-the-loop workflows, cron job scheduling, webhooks, and advanced debugging through LangGraph Studio.

If you're starting a new project and need stateful agents, complex workflows, or production-level deployment capabilities, evaluate LangGraph Platform first. LangServe remains viable for simpler chains and existing projects, but the ecosystem is shifting toward LangGraph for more sophisticated applications.

Best Use Cases for FastAPI

Use FastAPI for general-purpose APIs or projects that need detailed control. It’s a good fit for applications with complex logic, multiple data sources, or custom authentication.

FastAPI performs efficiently under high load and lets you fine-tune async behavior, caching, and endpoint performance without framework limits.

It also fits mixed workloads. If your API combines LLM requests with CRUD operations, real-time features, or third-party integrations, FastAPI provides a consistent base without tying you to LangChain’s abstractions.

It’s also a solid option for custom LLM integrations outside LangChain, or when you need full control over request handling and response formatting.

Building an API with LangServe

Here's how to set up a basic LangServe application.

Set Up Your Development Environment

You need Python 3.9 or higher. Create a virtual environment to isolate dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Dependencies

Install LangServe with all optional dependencies:

pip install "langserve[all]"

Or install client and server separately if you only need one:

pip install "langserve[server]"  # For server
pip install "langserve[client]"  # For client

You'll also need LangChain and a model provider:

pip install langchain langchain-openai uvicorn

Create the LangServe App

Create a file called server.py:

from fastapi import FastAPI
from langserve import add_routes
from langchain.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

app = FastAPI(
    title="LangServe Example",
    version="1.0",
    description="Simple API using LangServe"
)

model = ChatOpenAI(model="gpt-3.5-turbo")
prompt = ChatPromptTemplate.from_template("Tell me a fact about {topic}")
chain = prompt | model

add_routes(app, chain, path="/fact")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="localhost", port=8000)

This code creates multiple endpoints automatically: /fact/invoke for single requests, /fact/batch for batch processing, /fact/stream for streaming, and /fact/playground/ for testing in a UI.

Test and Run the API

Start the server:

uvicorn server:app --reload

Test the invoke endpoint using curl:

curl -X POST http://localhost:8000/fact/invoke \
  -H "Content-Type: application/json" \
  -d '{"input": {"topic": "Python"}}'

Visit http://localhost:8000/docs for interactive API documentation and http://localhost:8000/fact/playground/ to test your chain in a playground UI with streaming output.

Building an API with FastAPI

Here's the equivalent setup using pure FastAPI.

Set Up the FastAPI Environment

Use the same Python 3.9+ virtual environment approach:

python -m venv venv
source venv/bin/activate

Define Endpoints and Routes

Install FastAPI and dependencies:

pip install fastapi uvicorn langchain-openai pydantic

Create main.py:

from fastapi import FastAPI
from pydantic import BaseModel
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

app = FastAPI()

class FactRequest(BaseModel):
    topic: str

class FactResponse(BaseModel):
    fact: str

@app.post("/fact", response_model=FactResponse)
async def get_fact(request: FactRequest):
    model = ChatOpenAI(model="gpt-3.5-turbo")
    prompt = ChatPromptTemplate.from_template("Tell me a fact about {topic}")
    chain = prompt | model
    response = await chain.ainvoke({"topic": request.topic})
    return {"fact": response.content}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="localhost", port=8000)

You define the request and response schemas explicitly using Pydantic models. This gives you complete control over validation, serialization, and documentation.

Add Middleware and Dependencies

FastAPI lets you add middleware for concerns like CORS, authentication, or request logging:

from fastapi.middleware.cors import CORSMiddleware

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

You can inject dependencies like database connections or API keys through FastAPI's dependency injection system, giving you a clean way to share resources across endpoints.

Test and Run the FastAPI Server

Start the server:

uvicorn main:app --reload

Test with curl:

curl -X POST http://localhost:8000/fact \
  -H "Content-Type: application/json" \
  -d '{"topic": "Python"}'

FastAPI provides interactive documentation at http://localhost:8000/docs through Swagger UI, and an alternative interface at http://localhost:8000/redoc.

Deployment Options

Both frameworks deploy similarly since LangServe uses FastAPI internally.

Deploying LangServe Apps

Package your app in Docker for consistent deployments:

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"]

Deploy to platforms like Fly.io, Render, Railway, or cloud providers. For AWS, use AWS Copilot CLI or deploy to ECS. For Azure, use Azure Container Apps. For GCP, use Cloud Run.

Serverless deployment works with adapters but adds cold start latency that affects user experience for LLM applications. The framework documentation includes specific deployment guides for major platforms.

Deploying FastAPI Apps

Use the same Docker approach. FastAPI's broader adoption means more deployment examples exist for platforms like Kubernetes, AWS ECS, Google Cloud Run, and Azure Container Apps.

For serverless deployment on AWS Lambda or Azure Functions, use adapters like Mangum. Note that async operations can hit timeout limits on some serverless platforms, so test your specific workload. Lambda's 15-minute maximum timeout may not suit long-running LLM requests.

Both frameworks work well with container orchestration platforms like Kubernetes, where you can scale horizontally based on request load and implement health checks through standard HTTP endpoints.

Which One is Right for You?

Both frameworks are useful but built for different goals.Pick the one that fits your project’s scale and flexibility requirements.

Use LangServe if you’re building with LangChain and want a fast way to deploy simple chains or tools. It handles setup and provides a built-in playground for quick testing.

Choose FastAPI if you need more control, plan to scale, or work beyond LangChain. Its flexibility and mature ecosystem make it better for complex or long-term projects.

Many teams start with LangServe for rapid prototypes and move to FastAPI as their systems evolve. Since LangServe is built on FastAPI, that transition is smooth.

You can also connect with our experts to explore the best approach for designing and deploying your API stack.

Frequently Asked Questions

Is LangServe built on FastAPI?

Yes. LangServe uses FastAPI as its underlying web framework and adds convenience functions for deploying LangChain runnables. When you create a LangServe app, you're working with a FastAPI application under the hood. All FastAPI features like middleware, dependencies, and route customization remain available.

Is LangServe faster than FastAPI?

No. LangServe inherits FastAPI's performance characteristics but adds overhead from LangChain's abstraction layers and automatic routing logic. The performance difference is negligible compared to the latency of LLM API calls in most applications. Your bottleneck will almost always be the LLM inference time, not the framework.

Can you use LangServe without LangChain?

Not practically. LangServe is designed specifically for LangChain runnables and expects chains, agents, or runnables that follow LangChain's interface. If you're not using LangChain, FastAPI provides a better foundation without unnecessary abstractions.

Is FastAPI good for machine learning APIs?

Yes. FastAPI works well for ML APIs, including model serving, prediction endpoints, and data processing pipelines. Many ML platforms like HuggingFace, Ray Serve, and BentoML provide FastAPI integration examples. The framework's async support and automatic validation make it particularly suitable for serving models that require preprocessing or batch prediction.

Which is better for deploying LLM tools?

LangServe is better if you're using LangChain for simple chains and want rapid deployment with automatic endpoint generation, streaming support, and a built-in playground. FastAPI is better if you're building custom LLM integrations, combining multiple providers, need fine-grained control over request handling, or have requirements beyond what LangChain provides. 

For new projects with complex agent workflows, stateful applications, or production-level requirements, evaluate LangGraph Platform first as it's now the recommended solution from the LangChain team.