Unified API proxy for 100+ LLM providers with load balancing, fallbacks, spend tracking, and OpenAI-compatible interface.
One API for 100+ AI models — switch providers, add failovers, and track costs without changing your code.
LiteLLM solves the critical challenge of managing multiple LLM providers in production by offering a unified API that abstracts away provider-specific differences and complexities. Instead of maintaining separate integrations for OpenAI, Anthropic Claude, Google PaLM, AWS Bedrock, and dozens of other providers, developers can use LiteLLM's standardized OpenAI-compatible interface to switch between models seamlessly. The platform excels at production reliability with features like intelligent load balancing that distributes requests across multiple providers, automatic failover when providers experience downtime, and sophisticated retry logic with exponential backoff. Cost management becomes effortless with LiteLLM's built-in spend tracking, budget controls, and rate limiting that prevent unexpected billing surprises. The proxy supports advanced features like model fallbacks where requests automatically cascade to backup providers if the primary model fails, caching to reduce redundant API calls, and request logging for debugging and analytics. LiteLLM's routing capabilities enable A/B testing between different models, gradual rollouts of new providers, and intelligent model selection based on cost, latency, or capability requirements. For enterprise deployments, the platform provides detailed analytics on usage patterns, cost optimization recommendations, and compliance features for data governance. The system integrates seamlessly with existing applications through its OpenAI-compatible API, requiring minimal code changes while adding robust multi-provider capabilities, monitoring, and cost controls that are essential for production LLM applications.
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View Pricing Options →Centralize access to 100+ LLM providers with failover, load balancing, and cost tracking
Add automatic failover and retry logic to prevent AI application downtime
Track spending across providers, set budgets, and optimize model selection for cost efficiency
Standardize LLM access across teams with centralized logging, rate limits, and compliance controls
Compare model performance and gradually roll out new providers with traffic splitting
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