LiteLLM
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About
LiteLLM is an AI gateway designed to unify the interface for interacting with over 100 different Large Language Models (LLMs) from providers such as OpenAI, Azure, Anthropic, Gemini, and AWS Bedrock. By providing a single API that is fully compatible with the OpenAI format, it allows developers to swap models or implement automated fallbacks without rewriting their existing codebase. The tool functions as both a lightweight Python SDK and a standalone proxy server, acting as a middle layer that handles the complexities of request formatting and response parsing across diverse providers. Beyond simple model access, LiteLLM offers a robust suite of management tools essential for production-grade AI applications. It includes automatic spend tracking that can attribute costs to specific virtual keys, individual users, or entire teams, and allows for the implementation of strict budgets and rate limits (RPM/TPM). The gateway also supports load balancing across multiple instances of the same model and provides built-in logging to popular observability platforms like Langfuse, Arize Phoenix, and OpenTelemetry. This ensure that platform teams can maintain high availability and gain deep insights into their LLM usage. The tool is primarily built for platform engineers and developers who need to provide reliable LLM access to multiple internal projects or external users. It is particularly valuable for organizations that utilize a multi-cloud strategy or need to maintain vendor independence by easily switching between providers based on performance or availability. For teams already using the OpenAI API, LiteLLM offers a drop-in replacement that immediately expands their capabilities to include enterprise-grade features like SSO, audit logs, and custom SLAs through its professional and enterprise tiers. What sets LiteLLM apart is its commitment to maintaining an open-source core with a massive range of supported providers. Unlike proprietary wrappers, it allows for self-hosting on-premise, giving organizations full control over their data flow and security. The ability to integrate new models within a day of their release makes it an agile solution for staying at the forefront of the rapidly evolving AI landscape. By standardizing authentication and logging, it reduces the operational overhead of managing fragmented API keys and disparate monitoring tools across the stack.
Pros & Cons
Standardizes over 100 different model providers into a single, unified API format.
Enables model fallbacks and load balancing to maintain high application uptime.
Allows for detailed cost attribution and budget limits at the team level.
Integrates with major observability tools for deep insights into model performance.
Supports on-premise deployment for full control over data security and privacy.
Enterprise features like SSO and audit logs require a paid subscription.
Adding a proxy layer introduces a small amount of infrastructure management overhead.
Users are still responsible for managing individual API keys for each backend provider.
The vast range of configuration options may have a learning curve for new users.
Use Cases
Platform engineers can centralize LLM access for all internal developer teams to monitor usage and costs.
Software developers can implement model redundancy by setting up automatic fallbacks to alternative providers.
Security officers can host the gateway on-premise to ensure AI traffic adheres to internal data compliance standards.
Project managers can set hard spend budgets per team to prevent unexpected API bill spikes.
Product teams can rapidly prototype new AI features by switching between 100+ models with a single line of code.
Platform
Features
• built-in prompt management
• sso and jwt authentication
• rpm/tpm rate limiting
• virtual key management
• load balancing and fallbacks
• automated spend tracking
• 100+ llm provider integrations
• openai-compatible gateway
FAQs
Which LLM providers are supported by LiteLLM?
LiteLLM supports over 100 providers including OpenAI, Azure, Anthropic, Google Gemini, and AWS Bedrock. All of these can be called using a single OpenAI-compatible API format.
Can I track how much my different teams are spending on AI?
Yes, LiteLLM includes features to attribute costs to specific keys, users, teams, or organizations. You can also log this spend data to S3 or GCS for long-term reporting.
How does the fallback system work in LiteLLM?
You can configure multiple models in a list; if the primary provider fails or hits a rate limit, the gateway automatically routes the request to the next available provider to ensure uptime.
Does LiteLLM support self-hosting for security-sensitive data?
Yes, LiteLLM can be deployed on-premise or within your own private cloud using Docker. This ensures that all LLM traffic stays within your controlled infrastructure for compliance.
Is LiteLLM compatible with observability tools?
LiteLLM provides native integration with several observability and logging platforms including Langfuse, Prometheus, OpenTelemetry, Arize Phoenix, and Langsmith.
Pricing Plans
Enterprise
Unknown Price• Everything in OSS
• Enterprise Support
• Custom SLAs
• JWT Authentication
• SSO Access
• Audit Logs
Open Source
Free Plan• 100+ LLM Provider Integrations
• Langfuse/OTEL/Langsmith Logging
• Virtual Keys
• Budgets and Teams
• Load Balancing
• RPM/TPM Limits
• LLM Guardrails
Job Opportunities
There are currently no job postings for this AI tool.
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