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, pydantic.

vllm-project/vllm

77,364
Stars
Python
Language
8
Components
0.0
Connectivity

berriai/litellm

43,933
Stars
Python
Language
7
Components
0.0
Connectivity

Technology Stack

Shared Technologies

fastapi pydantic

Only in Vllm

pytorch triton flashattention transformers ray cutlass

Only in Litellm

prisma redis httpx docker postgresql

Architecture Layers

Vllm (3 layers)

API Layer
Exposes OpenAI-compatible REST APIs, CLI tools, and offline inference interfaces that handle request parsing, validation, and response formatting
Engine Layer
Orchestrates the inference pipeline by managing request queues, scheduling batches using continuous batching, and coordinating between tokenization and model execution
Executor Layer
Executes model inference using optimized attention kernels, manages distributed execution across multiple GPUs, and handles memory allocation with PagedAttention

Litellm (4 layers)

LLM Interface Layer
Normalizes requests across 100+ LLM providers into OpenAI format, handling authentication, rate limiting, and provider-specific quirks
Proxy Gateway Layer
HTTP server that routes requests, applies middleware (auth, logging, caching), and manages team/user permissions
Router & Load Balancer
Intelligently routes requests to available models, handles failovers, and distributes load across multiple providers
Enterprise Security
Guardrails, content moderation, banned keywords, and compliance hooks that run during request/response processing

Data Flow

Vllm (8 stages)

  1. Parse and validate requests
  2. Tokenize input text
  3. Schedule batch execution
  4. Allocate KV cache blocks
  5. Prepare model inputs
  6. Execute forward pass
  7. Sample next tokens
  8. Update sequences and detokenize

Litellm (7 stages)

  1. HTTP request ingestion
  2. Authentication and authorization
  3. Router model selection
  4. Provider API transformation
  5. LLM API call execution
  6. Response normalization
  7. Apply response middleware

System Behavior

DimensionVllmLitellm
Data Pools44
Feedback Loops33
Delays43
Control Points55

Code Patterns

Unique to Vllm

pagedattention continuous batching worker pool plugin system cuda graph optimization

Unique to Litellm

provider adapter pattern plugin hook system multi-tier caching config-driven architecture

When to Choose

Choose Vllm when you need

  • Unique tech: pytorch, triton, flashattention
View full analysis →

Choose Litellm when you need

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

Frequently Asked Questions

What are the main differences between Vllm and Litellm?

Vllm has 8 components with a connectivity ratio of 0.0, while Litellm has 7 components with a ratio of 0.0. They share 2 technologies but differ in 11 others.

Should I use Vllm or Litellm?

Choose Vllm if you need: Unique tech: pytorch, triton, flashattention. Choose Litellm if you need: Unique tech: prisma, redis, httpx.

How does the architecture of Vllm compare to Litellm?

Vllm is organized into 3 architecture layers with a 8-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, triton, flashattention, transformers, ray. Litellm uniquely uses: prisma, redis, httpx, docker, postgresql.

Explore the interactive analysis

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

Vllm Litellm

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Compared on April 20, 2026 by CodeSea. Written by .