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BerriAI-litellm

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BerriAI LiteLLM is a free Python SDK and proxy server that routes calls to 100+ LLMs including Claude, GPT-4, Gemini, and Bedrock through a single OpenAI-compatible interface.

Pricing Model
free
Skill Level
All Levels
Best For
Software DevelopmentAI ResearchEnterprise TechnologyDeveloper Tooling
Use Cases
LLM IntegrationAPI GatewayCost TrackingMulti-Provider Load Balancing
Visit Site
4.5/5
Overall Score
4+
Features
1
Pricing Plans
0
User Reviews
Updated 25 May 2026
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What is BerriAI-litellm?

BerriAI LiteLLM is an open-source Python SDK and proxy server that provides a unified OpenAI-compatible interface for calling over 100 large language model APIs — including Anthropic Claude, OpenAI GPT-4, Google Gemini, AWS Bedrock, Azure OpenAI, Cohere, VertexAI, HuggingFace, and NVIDIA NIM — without modifying the message format or request structure between providers. Released under the MIT license, the SDK is free to use and deploy on your own infrastructure; the only cost is your underlying LLM provider usage. The core engineering problem LiteLLM solves is API fragmentation. Every LLM provider exposes a different message schema, authentication flow, and error format. Switching a production application from GPT-4 to Claude 3.5 Sonnet without LiteLLM requires rewriting message array formatting, response parsing, and error handling. With LiteLLM, the same request object works across all providers — changing the model string is the only modification required. This provider-agnostic architecture is why LiteLLM has accumulated over 40,000 GitHub stars and is used in production by Adobe, Twilio, Siemens, and Rocket Money. The Proxy Server component adds enterprise-grade capabilities on top of the Python SDK: budget enforcement per API key, team, or project; rate limiting per model; retry-and-fallback routing that automatically reroutes requests to backup deployments when a provider returns a 429 rate limit error; load balancing across multiple Azure or OpenAI deployments; and a management API for multi-tenant key management and spend tracking. As of the March 2026 release cycle, LiteLLM added MCP server integration, SCIM and SSO support, and expanded RBAC for organization-level access control. LiteLLM is designed for engineers and AI researchers who are comfortable with Python, Docker, and YAML configuration files. It is not appropriate for non-technical users, product teams without an engineering counterpart, or organizations whose LLM usage is confined to a single provider with no multi-model routing requirements — in those scenarios, the abstraction layer adds deployment overhead without delivering corresponding workflow value.

BerriAI LiteLLM is a free Python SDK and proxy server that routes calls to 100+ LLMs including Claude, GPT-4, Gemini, and Bedrock through a single OpenAI-compatible interface.

BerriAI-litellm is widely used by professionals, developers, marketers, and creators to enhance their daily work and improve efficiency.

Key Features

1
Comprehensive LLM Integration
Supports over 100 LLM API providers through a centralized pricing and context window database maintained in model_prices_and_context_window.json, covering all major commercial providers — OpenAI, Anthropic, Google, AWS Bedrock, Azure, Cohere — and open-source model hosts including HuggingFace, vLLM, and NVIDIA NIM, under a single OpenAI-compatible message format.
2
Consistent Output Format
Translates all provider-specific message schemas, authentication headers, and response structures into the standard OpenAI format at the translation layer — meaning application code never needs to branch on provider type, and switching from one LLM to another is a single model-string change rather than a codebase refactor.
3
Retry and Fallback Logic
Configures automatic request rerouting to backup deployments when primary providers return 429 rate limit errors, 500 server errors, or timeout responses — maintaining application uptime during provider-side outages without requiring custom exception handler logic in the application layer.
4
Budget and Rate Limiting
Enforces per-API-key, per-team, per-project, and per-model budget limits and rate caps through the Proxy Server's management API — providing engineering and finance teams with precise cost control and attribution across multi-tenant LLM deployments without building custom spend tracking infrastructure.

Pros & Cons

✓ Pros (4)
Versatile Integration LiteLLM's 100+ provider support covers every major commercial and open-source LLM host, making it a single integration that satisfies both current provider requirements and future flexibility needs — eliminating the technical debt that accumulates when each new LLM provider requires a separate integration module.
User-Friendly Setup The Python SDK installs via pip in one command and requires only a model string change to switch providers. The Proxy Server deploys via Docker with a YAML configuration file that defines the model list, routing logic, and budget rules — a setup that most Python-proficient engineers complete in under an hour for basic configurations.
Cost Management Built-in budget enforcement per API key, team, and project prevents unexpected LLM cost overruns in multi-developer environments where individual engineers are issuing provider API calls without centralized spend visibility or per-team quota controls.
Scalable Solution The Proxy Server supports load balancing across multiple provider deployments, rate limiting at fine granularity, and multi-tenant key management with RBAC and SSO — features that scale from a two-person startup to enterprise deployments processing millions of LLM requests per day.
✕ Cons (2)
Initial Setup Complexity Configuring the LiteLLM Proxy Server for production — including Docker deployment, YAML model list definition, database setup for spend tracking, and RBAC configuration for multi-tenant key management — requires familiarity with container orchestration and proxy server concepts that engineers new to infrastructure tooling will find time-consuming to learn.
Dependency on External Models LiteLLM's performance, output quality, and latency are ultimately bounded by the capabilities of the underlying LLM providers it routes to — if all configured providers have outages simultaneously or all impose rate limits during peak usage, LiteLLM's fallback logic cannot compensate for universal provider-side unavailability.

Who Uses BerriAI-litellm?

Software Developers
Integrate LiteLLM as the LLM abstraction layer in production applications to maintain provider flexibility — allowing engineering teams to evaluate new models, respond to pricing changes, or fail over to alternative providers by changing a configuration value rather than deploying a code change.
AI Researchers
Use the SDK to run controlled experiments comparing output quality, latency, and cost across multiple LLM providers on the same prompt set — with consistent input-output format reducing the experimental variable count and improving comparability between provider evaluations.
Tech Enterprises
Deploy the LiteLLM Proxy Server as a centralized AI gateway that manages API keys, enforces per-department budget limits, and provides spend attribution reporting across multiple internal teams consuming LLM APIs — addressing the governance and cost visibility gap that arises when LLM access is distributed across individual developer API keys.
Data Scientists
Call multiple LLM providers within data pipelines and analysis workflows using a single Python function signature, avoiding the repeated overhead of reading five different API documentation pages when incorporating a new model into an existing analytical or classification pipeline.
Uncommon Use Cases
Educational institutions use LiteLLM to teach AI model integration in software engineering curricula, giving students a standardized abstraction to learn against before encountering raw provider APIs. Startup incubators deploy it as shared LLM infrastructure for cohort companies, managing API key issuance, budget allocation, and usage monitoring from a centralized proxy rather than distributing individual provider keys to each portfolio company.

BerriAI-litellm vs MyMap AI vs GPT for Sheets and Docs vs Pabbly Connect

Detailed side-by-side comparison of BerriAI-litellm with MyMap AI, GPT for Sheets and Docs, Pabbly Connect — pricing, features, pros & cons, and expert verdict.

Compare
B
BerriAI-litellm
Free
Visit ↗
MyMap AI
Freemium
Visit ↗
GPT for Sheets and Docs
Freemium
Visit ↗
Pabbly Connect
Freemium
Visit ↗
💰Pricing
FreeFreemiumFreemiumFreemium
Rating
🆓Free Trial
Key Features
  • Comprehensive LLM Integration
  • Consistent Output Format
  • Retry and Fallback Logic
  • Budget and Rate Limiting
  • AI-Native
  • Multiple Format Upload
  • Web Search
  • Internet Access
  • Bulk Processing Capabilities
  • Diverse Model Selection
  • Versatile Use Cases
  • Ease of Integration
  • 2,000+ Integrations
  • No-Code Automation
  • Advanced Multi-Step Workflows
  • Cost-Effective Pricing
👍Pros
LiteLLM's 100+ provider support covers every major comm
The Python SDK installs via pip in one command and requ
Built-in budget enforcement per API key, team, and proj
Converting a 30-page document or a complex topic descri
The chat-based creation model means there is no interfa
MyMap accepts source material from text, documents, URL
Running a language model prompt across an entire Google
The freemium model provides access to base AI processin
The add-on integrates as a standard Google Workspace si
Features a logical, step-by-step wizard that simplifies
The lifetime deal provides massive long-term ROI, espec
Backed by an active Facebook group of 21,000+ members a
👎Cons
Configuring the LiteLLM Proxy Server for production — i
LiteLLM's performance, output quality, and latency are
The chat-based creation model is intuitive for simple d
MyMap AI requires an active internet connection for all
MyMap's AI-driven layout produces diagrams that are str
While the formula syntax is straightforward, writing ef
GPT-4 Turbo and Claude 3 model calls generate token-bas
GPT for Sheets and Docs operates exclusively within Goo
While no-code, mastering the logic of deep routers and
While it covers 2,000+ apps, some niche enterprise trig
Workflow reliability is tied to the API stability of th
🎯Best For
Software DevelopersStudents & ResearchersContent CreatorsSmall to Medium-Sized Businesses
🏆Verdict
LiteLLM is the most pragmatic solution for engineering teams…
MyMap AI is the most accessible entry point for AI-generated…
For e-commerce managers, data analysts, and content teams wh…
Pabbly Connect is the 'utility player' of the automation wor…
🔗Try It
Visit BerriAI-litellm ↗Visit MyMap AI ↗Visit GPT for Sheets and Docs ↗Visit Pabbly Connect ↗
🏆
Our Pick
BerriAI-litellm
LiteLLM is the most pragmatic solution for engineering teams building applications that need to evaluate, compare, or fa
Try BerriAI-litellm Free ↗

BerriAI-litellm vs MyMap AI vs GPT for Sheets and Docs vs Pabbly Connect — Which is Better in 2026?

Choosing between BerriAI-litellm, MyMap AI, GPT for Sheets and Docs, Pabbly Connect can be difficult. We compared these tools side-by-side on pricing, features, ease of use, and real user feedback.

BerriAI-litellm vs MyMap AI

BerriAI-litellm — BerriAI LiteLLM is an AI Tool for software developers that eliminates the per-provider API integration overhead of working with multiple large language model ve

MyMap AI — MyMap AI is an AI Tool that generates diagrams and mind maps from conversational input, uploaded files, URLs, and live web search results. Its chat-native desig

  • BerriAI-litellm: Best for Software Developers, AI Researchers, Tech Enterprises, Data Scientists, Uncommon Use Cases
  • MyMap AI: Best for Students & Researchers, Professionals, Content Creators, Educators, Uncommon Use Cases

BerriAI-litellm vs GPT for Sheets and Docs

BerriAI-litellm — BerriAI LiteLLM is an AI Tool for software developers that eliminates the per-provider API integration overhead of working with multiple large language model ve

GPT for Sheets and Docs — GPT for Sheets and Docs is an AI Tool that brings multiple AI language models into Google Sheets and Docs through a simple add-on installation, enabling bulk te

  • BerriAI-litellm: Best for Software Developers, AI Researchers, Tech Enterprises, Data Scientists, Uncommon Use Cases
  • GPT for Sheets and Docs: Best for Content Creators, Data Analysts, E-commerce Managers, Marketers, Uncommon Use Cases

BerriAI-litellm vs Pabbly Connect

BerriAI-litellm — BerriAI LiteLLM is an AI Tool for software developers that eliminates the per-provider API integration overhead of working with multiple large language model ve

Pabbly Connect — Pabbly Connect is a high-value automation engine that disrupts the market with its 'pay-once' lifetime model. By offering 2,000+ integrations and a generous pol

  • BerriAI-litellm: Best for Software Developers, AI Researchers, Tech Enterprises, Data Scientists, Uncommon Use Cases
  • Pabbly Connect: Best for Small to Medium-Sized Businesses, E-commerce Platforms, Marketing Agencies, Freelancers, Uncommon Us

Final Verdict

LiteLLM is the most pragmatic solution for engineering teams building applications that need to evaluate, compare, or fail over between multiple LLM providers without maintaining separate integration codebases for each. The trade-off is operational: self-hosted deployment requires engineers to manage the Docker environment, handle YAML configuration, and maintain proxy server health — teams without dedicated DevOps capacity may find managed LLM gateway alternatives like TrueFoundry's hosted offering worth evaluating alongside the self-hosted LiteLLM setup.

FAQs

5 questions
What LLM providers does LiteLLM support?
LiteLLM supports over 100 LLM providers including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI, Cohere, VertexAI, HuggingFace, vLLM, NVIDIA NIM, and Sagemaker. The full provider and pricing list is maintained in the model_prices_and_context_window.json file in the GitHub repository, updated with each weekly release cycle.
Is LiteLLM free to use?
The LiteLLM Python SDK and Proxy Server are both released under the MIT license and free to self-host. You pay only for the underlying LLM provider API calls at each provider's published token rates. The open-source proxy server includes all features — budget tracking, rate limiting, fallback routing, and multi-tenant management — with no commercial licensing fee for self-hosted deployments.
How does LiteLLM handle LLM provider rate limit errors?
LiteLLM's Proxy Server implements configurable retry-and-fallback logic that automatically reroutes requests to backup deployments when primary providers return 429 rate limit errors or 500 server responses. The fallback model list is defined in the YAML configuration file, allowing engineers to specify ordered priority across providers or regions without writing custom exception handlers in application code.
Is LiteLLM suitable for non-technical teams or business users?
LiteLLM is designed for engineers and AI researchers who are comfortable with Python, Docker, and YAML configuration. Non-technical users cannot use LiteLLM without engineering support. Organizations whose LLM usage is entirely managed through a single provider's consumer interface have no practical need for LiteLLM's abstraction layer and would add deployment complexity without receiving corresponding workflow value.
Who uses LiteLLM in production?
LiteLLM is used in production by Adobe, Twilio, Siemens, and Rocket Money, among others. As of early 2026, the project has over 40,000 stars on GitHub and is a Y Combinator W23 company. The repository is actively maintained with multiple releases per week, with the last documented release cycle running through April 2026 based on public GitHub release history.

Expert Verdict

Expert Verdict
LiteLLM is the most pragmatic solution for engineering teams building applications that need to evaluate, compare, or fail over between multiple LLM providers without maintaining separate integration codebases for each. The trade-off is operational: self-hosted deployment requires engineers to manage the Docker environment, handle YAML configuration, and maintain proxy server health — teams without dedicated DevOps capacity may find managed LLM gateway alternatives like TrueFoundry's hosted offering worth evaluating alongside the self-hosted LiteLLM setup.

Summary

BerriAI LiteLLM is an AI Tool for software developers that eliminates the per-provider API integration overhead of working with multiple large language model vendors. The MIT-licensed SDK is free; the Proxy Server adds enterprise features including spend tracking, rate limiting, and multi-tenant key management. The project last indexed on GitHub as of May 22, 2026, remains under active development with multiple weekly releases. LiteLLM is a Y Combinator W23 company used in production at Adobe, Twilio, and Siemens.

It is suitable for beginners as well as professionals who want to streamline their workflow and save time using advanced AI capabilities.

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