Vllm vs Litellm

Vllm and Litellm are both popular ml inference & agents tools. This page compares their internal architecture, technology stack, data flow patterns, and system behavior — based on automated structural analysis of their source code. They share 2 technologies including fastapi, pytest.

vllm-project/vllm

74,266
Stars
Python
Language
10
Components
1.3
Connectivity

berriai/litellm

40,426
Stars
Python
Language
10
Components
1.6
Connectivity

Technology Stack

Shared Technologies

fastapi pytest

Only in Vllm

pytorch cuda/c++ ray triton huggingface cmake

Only in Litellm

prisma pydantic httpx docker

Architecture Layers

Vllm (4 layers)

CUDA Kernels
Performance-critical C++/CUDA kernels for attention, quantization, and layer operations
Model Executor
Core LLM execution engine with model implementations, attention mechanisms, and memory management
Entrypoints
Multiple serving interfaces including CLI, OpenAI API, and offline inference
Configuration
Comprehensive configuration system for models, parallelism, and device settings

Litellm (4 layers)

Core LLM Abstraction
Provider adapters and completion API in litellm/llms/ and litellm/
Proxy Server
AI Gateway with auth, routing, rate limiting in litellm/proxy/
Enterprise Features
Commercial guardrails, moderation, and hooks in enterprise/
Infrastructure
Database utilities and deployment tools in litellm-proxy-extras/

Data Flow

Vllm (6 stages)

  1. Request Ingestion
  2. Scheduling
  3. Memory Allocation
  4. Model Execution
  5. Token Generation
  6. Response Streaming

Litellm (7 stages)

  1. Request Authentication
  2. Pre-call Hooks
  3. Model Routing
  4. Provider Translation
  5. LLM API Call
  6. Response Translation
  7. Post-call Hooks

System Behavior

DimensionVllmLitellm
Data Pools33
Feedback Loops33
Delays33
Control Points54

Code Patterns

Unique to Vllm

plugin system custom cuda ops backend abstraction config-driven architecture distributed execution

Unique to Litellm

provider adapter pattern hook system enterprise extensions unified api surface

When to Choose

Choose Vllm when you need

  • Unique tech: pytorch, cuda/c++, ray
View full analysis →

Choose Litellm when you need

  • Unique tech: prisma, pydantic, httpx
View full analysis →

Frequently Asked Questions

What are the main differences between Vllm and Litellm?

Vllm has 10 components with a connectivity ratio of 1.3, while Litellm has 10 components with a ratio of 1.6. They share 2 technologies but differ in 10 others.

Should I use Vllm or Litellm?

Choose Vllm if you need: Unique tech: pytorch, cuda/c++, ray. Choose Litellm if you need: Unique tech: prisma, pydantic, httpx.

How does the architecture of Vllm compare to Litellm?

Vllm is organized into 4 architecture layers with a 6-stage data pipeline. Litellm has 4 layers with a 7-stage pipeline.

What technology does Vllm use that Litellm doesn't?

Vllm uniquely uses: pytorch, cuda/c++, ray, triton, huggingface. Litellm uniquely uses: prisma, pydantic, httpx, docker.

Explore the interactive analysis

See the full architecture maps, code patterns, and dependency graphs.

Vllm Litellm

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Compared on March 25, 2026 by CodeSea. Written by .