From 9d7a255d5534c9eff80e5ace8ff2b1c6d3f45137 Mon Sep 17 00:00:00 2001 From: Shivam Rawat <161387515+shivamrawat1@users.noreply.github.com> Date: Wed, 10 Dec 2025 19:14:49 -0800 Subject: [PATCH] made litellm proxy and sdk difference cleaner in overview (#17790) --- docs/my-website/docs/index.md | 54 +++++++++++++++++------------------ 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/docs/my-website/docs/index.md b/docs/my-website/docs/index.md index c6e335e4cc3..f393b300f73 100644 --- a/docs/my-website/docs/index.md +++ b/docs/my-website/docs/index.md @@ -13,36 +13,36 @@ https://github.com/BerriAI/litellm - Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy) ## How to use LiteLLM -You can use litellm through either: -1. [LiteLLM Proxy Server](#litellm-proxy-server-llm-gateway) - Server (LLM Gateway) to call 100+ LLMs, load balance, cost tracking across projects -2. [LiteLLM python SDK](#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking -### **When to use LiteLLM Proxy Server (LLM Gateway)** +You can use LiteLLM through either the Proxy Server or Python SDK. Both gives you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs: -:::tip +
| + | LiteLLM Proxy Server | +LiteLLM Python SDK | +
|---|---|---|
| Use Case | +Central service (LLM Gateway) to access multiple LLMs | +Use LiteLLM directly in your Python code | +
| Who Uses It? | +Gen AI Enablement / ML Platform Teams | +Developers building LLM projects | +
| Key Features | +• Centralized API gateway with authentication & authorization • Multi-tenant cost tracking and spend management per project/user • Per-project customization (logging, guardrails, caching) • Virtual keys for secure access control • Admin dashboard UI for monitoring and management |
+• Direct Python library integration in your codebase • Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router • Application-level load balancing and cost tracking • Exception handling with OpenAI-compatible errors • Observability callbacks (Lunary, MLflow, Langfuse, etc.) |
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