Litellm

Litellm offers a free plan with limited features. Paid plans are available for additional features and capabilities. Available plans include: Open Source, LiteLLM Hosted, Enterprise.

LiteLLM acts as a universal API wrapper, allowing developers to call any supported LLM (e.g., OpenAI, Anthropic, Google, Hugging Face) using a single, consistent OpenAI-style interface. It intelligently routes requests, handles provider-specific nuances, and implements robust features to ensure reliability and optimize performance. This gateway simplifies development, reduces vendor lock-in, and provides a centralized control plane for LLM operations.

Key features of Litellm include: Unified API for 100+ LLMs: Access models from OpenAI, Anthropic, Google, Azure, Hugging Face, and more using a single, consistent OpenAI-compatible API call, simplifying integration across providers.. Automatic Load Balancing: Distribute requests across multiple LLM providers or API keys to prevent rate limits, optimize performance, and ensure high availability for your applications.. Intelligent Retries and Fallbacks: Automatically retry failed requests or seamlessly fall back to a different LLM provider if the primary one fails, significantly improving application resilience and uptime.. Comprehensive Cost Tracking: Monitor and analyze LLM token usage and costs across all providers and models from a single dashboard, enabling better budget management and optimization.. Response Caching: Cache LLM responses to reduce latency, decrease API costs, and improve the responsiveness of your applications for frequently asked prompts.. Streaming Support: Efficiently handle real-time LLM responses with built-in streaming capabilities, providing a faster and more interactive user experience.. Guardrails and Moderation: Implement content moderation, safety checks, and custom business logic as guardrails to ensure LLM outputs are aligned with desired standards and policies.. Key Management and Virtual Keys: Securely manage API keys for various providers and create virtual keys for different teams or projects, simplifying access control and usage monitoring..

Litellm is best suited for This tool is primarily for developers, AI engineers, and enterprises building and deploying large language model applications. It's ideal for teams seeking to manage multi-LLM strategies, reduce operational overhead, and ensure the reliability and cost-efficiency of their AI infrastructure..

Access diverse LLM providers through one unified API, drastically reducing development time and effort compared to integrating each individually.

Leverage built-in retries, fallbacks, and load balancing to ensure your LLM-powered applications remain operational and performant even when providers experience issues.

Gain full visibility and control over LLM spending with comprehensive cost tracking, allowing for informed decisions and budget adherence across all models.

Easily switch between LLM providers or utilize multiple simultaneously without significant code changes, maintaining flexibility and bargaining power.

Focus on building innovative AI features rather than managing complex API integrations and infrastructure, speeding up time-to-market for LLM-based products.

Develop chatbots that maintain high availability by automatically retrying failed requests or falling back to alternative LLMs when a primary provider is down.

Deploy production-grade LLM applications with features like load balancing, cost tracking, and guardrails, ensuring scalability, security, and compliance.

Easily compare the performance, latency, and cost-effectiveness of different LLMs for specific tasks by routing a percentage of traffic to each model.

Integrate and manage LLMs from various cloud providers (e.g., Azure, AWS Bedrock, Google Cloud) under a single API, optimizing for cost and regional availability.

Track token usage and costs across all models and providers to identify areas for optimization, potentially by switching to cheaper models for certain tasks.

Provide a unified interface for internal development teams to access and experiment with various LLMs, standardizing integration and reducing onboarding time.