LiteLLM
LiteLLM is an open-source unified invocation library and proxy middleware for large language models. It provides a standardized API interface compatible with the OpenAI format and supports routing requests to more than 100 LLM providers, including OpenAI, Anthropic, Azure, and locally deployed models. Core features include multi-model load balancing, request format conversion, cost tracking, and access control, enabling developers to integrate and manage LLM services from different sources in a unified manner.
Core Facts
LiteLLM is an open-source unified LLM access layer project whose Proxy mode can uniformly convert API requests in different formats into OpenAI-compatible format.
LiteLLM, Portkey, and Kong AI Gateway are typical open-source or commercial implementations of AI gateways.
Open-source projects such as LiteLLM and OneAPI also provide similar unified interface layers.
Timeline (last 90 days)
An LLM API pricing analysis covering 137 providers and 4,033 model entries shows that, among the 375 models for which at least two providers offer a comparable input price, the median input-price spread is 0.0%.
LiteLLM's model_cost table is a community-maintained pricing database that records per-model metadata such as each provider's input/output price per million tokens and context window, periodically scraped from providers' public pages by a script and merged, with different entries updated at inconsistent frequencies.
For 72% of the models, the input-price spread is within 20%.
In the same sample of 375 models, 42 models (11%) have an input-price spread exceeding 200%.
The three-tier semantic routing matrix sends 80% of traffic to tier-fast (gemini-2.0-flash-001), 15% to tier-frontier (claude-3.7-sonnet), and 5% to tier-reasoning (qwq-32b or o3-mini).
In LiteLLM configuration, num_retries: 2 combined with fallbacks forms a two-layer fault tolerance that retries first and then switches providers, handling 429 rate limits and regional jitter.
Relying solely on a single provider's direct API key means a single rate-limit peak at that provider will interrupt user-facing SSE streaming output.
Each routing layer should be configured with fallback models of similar price magnitude to avoid bill spikes caused by failover.
The tier-fast layer targets an SLA of TTFT < 250ms and blended cost < $0.20/1M.
The article reviewed 232 inference providers and 470 models.
40 more timeline events
All Facts (20)
LiteLLM is an open-source unified LLM access layer project whose Proxy mode can uniformly convert API requests in different formats into OpenAI-compatible format.
80%VerifiedLiteLLM, Portkey, and Kong AI Gateway are typical open-source or commercial implementations of AI gateways.
75%VerifiedOpen-source projects such as LiteLLM and OneAPI also provide similar unified interface layers.
75%VerifiedLiteLLM是一个开源的LLM API统一代理层，将数十家模型提供商的API统一封装为OpenAI兼容格式
75%VerifiedSimilar open-source LLM gateway solutions in the industry include LiteLLM, OneAPI, and PortKey.
75%VerifiedProjects such as OpenRouter and LiteLLM are also exploring multi-model orchestration solutions.
75%VerifiedRouteLLM and LiteLLM are open-source frameworks for implementing LLM intelligent routing functionality.
70%VerifiedOpenRouter is an AI model aggregation and routing platform that allows developers to access hundreds of models from multiple vendors through a unified OpenAI-compatible API interface.
70%VerifiedIn testing, the p99 latency per 160-millisecond tick was 147.4–147.5 milliseconds, with zero missed deadlines across three rounds totaling 84,000 session ticks measured.
65%VerifiedThe core of the OpenAI-compatible protocol is the /v1/chat/completions REST interface specification, which defines fields such as model, messages, and temperature, as well as the streaming response (Server-Sent Events) format.
65%VerifiedThe LiteLLM project is developed in Python.
65%VerifiedLiteLLM provides two usage modes: a Python SDK and a Proxy Server (AI Gateway).
65%VerifiedLiteLLM is an open-source project developed by the BerriAI team.
65%VerifiedLLM routing is an inference optimization strategy that dynamically selects models based on task complexity, cost budget, and response speed requirements; open-source frameworks such as RouteLLM and LiteLLM are already exploring this direction.
65%UnverifiedLiteLLM的日志系统支持与Langfuse、Helicone、Datadog和Prometheus等可观测性平台集成
90%UnverifiedLiteLLM的护栏系统支持集成第三方审核服务如Lakera和Presidio
90%Unverified该方案的所有数据和推理都在本地完成，不会外传到第三方服务器，适合对数据隐私有严格要求的企业场景
90%Unverified通过设置环境变量ANTHROPIC_BASE_URL指向LiteLLM Proxy的本地端口，可以将Claude Agent SDK的请求重定向到本地模型，无需修改SDK源码
90%UnverifiedLiteLLM和OneAPI等开源项目将不兼容OpenAI格式的模型（如Claude的原生API、Gemini的Vertex AI接口）统一适配为OpenAI格式
85%Unverified业界知名的合规LLM网关方案包括LiteLLM、OneAPI以及企业级的Portkey
85%