LiteLLM
vs
docs.litellm.ai
LiteLLM
LiteLLM serves as a powerful LLM gateway platform that streamlines the integration and management of over 100 Language Learning Model providers. The platform offers a unified API format consistent with OpenAI's standards, enabling seamless authentication, load balancing, and comprehensive spend tracking capabilities.
The solution provides essential enterprise features including virtual keys management, budget controls, team administration, and RPM/TPM limits. With proven reliability demonstrated by serving over 20 million requests and maintaining 100% uptime, LiteLLM has garnered trust from a substantial user base with over 90,000 docker pulls and contributions from more than 200 developers.
docs.litellm.ai
LiteLLM is a versatile tool designed to streamline interactions with over 100 large language models (LLMs). It offers a unified interface, allowing users to access various LLMs through a consistent OpenAI-compatible input/output format. This simplifies the development process and reduces the complexity of integrating multiple LLMs into applications.
LiteLLM offers functionalities like consistent output formatting, retry/fallback logic across deployments, and spend tracking. It can be used via a Python SDK for direct integration into code or as a proxy server (LLM Gateway) for centralized management and access control.
LiteLLM
Pricing
docs.litellm.ai
Pricing
LiteLLM
Features
- Unified API Integration: Support for 100+ LLM providers in OpenAI format
- Load Balancing: Advanced request distribution and RPM/TPM limits
- Authentication Management: Virtual keys, JWT auth, and SSO capabilities
- Monitoring & Logging: Integration with Langfuse, Langsmith, and OTEL
- Budget Control: Spend tracking and budget management across providers
- Enterprise Security: Audit logs, LLM guardrails, and Prometheus metrics
docs.litellm.ai
Features
- Unified Interface: Access 100+ LLMs using a consistent OpenAI-compatible format.
- Consistent Output: Text responses are always available at ['choices'][0]['message']['content'].
- Retry/Fallback Logic: Built-in mechanisms for handling failures and switching between deployments (e.g., Azure/OpenAI).
- Cost Tracking: Monitor and set budgets for LLM usage per project.
- Proxy Server (LLM Gateway): Centralized service for managing access to multiple LLMs, including logging, and access control.
- Python SDK: Integrate LiteLLM directly into Python code for streamlined development.
- Streaming Support: Enable streaming for real-time interactions with LLMs.
- Exception Handling: Maps exceptions across providers to OpenAI exception types.
- Observability: Pre-defined callbacks for integrating with MLflow, Lunary, Langfuse, Helicone, and more.
LiteLLM
Use cases
- Enterprise LLM API management
- Multi-provider LLM integration
- API request load balancing
- Usage monitoring and cost tracking
- Team access management
- Security and compliance enforcement
docs.litellm.ai
Use cases
- Developing applications requiring access to multiple LLMs.
- Building LLM-powered features with fallback and redundancy.
- Centralized management of LLM access and usage within an organization.
- Integrating various LLMs into existing Python projects.
- Tracking and controlling costs associated with LLM usage.
- Creating a unified LLM gateway for internal teams.
LiteLLM
Uptime Monitor
Average Uptime
98.91%
Average Response Time
239.7 ms
Last 30 Days
docs.litellm.ai
Uptime Monitor
Average Uptime
100%
Average Response Time
132.86 ms
Last 30 Days
LiteLLM
docs.litellm.ai
