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Open Source AI Gateway
Manage multiple LLM providers with built-in failover, guardrails, caching, and monitoring.

Open Source AI Gateway
Free

Home: https://ai-gateway.dev

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What is Open Source AI Gateway?

Leverage the power of multiple Large Language Model (LLM) providers through a unified, production-ready gateway. This open-source tool simplifies the integration and management of services from OpenAI, Anthropic, Gemini, Ollama, Mistral, and Cohere. It provides both an OpenAI API compatible HTTP interface and a high-performance gRPC interface, catering to diverse developer needs.

Enhance application reliability with automatic failover between providers, ensuring continuous service even if one provider experiences issues. Optimize costs and performance using the built-in intelligent caching system with configurable time-to-live (TTL). Implement fine-grained control over usage with fully configurable rate limiting policies and ensure safety with content filtering measures. Monitor usage, tokens, and errors through the admin dashboard, and integrate logging with platforms like Splunk, Datadog, and Elasticsearch for comprehensive oversight.

Features

  • Multi-Provider Support: Integrate with OpenAI, Anthropic, Gemini, Ollama, Mistral, and Cohere.
  • Dual Interface: Offers OpenAI API compatible HTTP and high-performance gRPC interfaces.
  • Smart Failover: Automatically switches between providers for high availability.
  • Intelligent Caching: Built-in caching with configurable TTL reduces costs.
  • Rate Limiting: Configurable policies to manage request frequency.
  • Admin Dashboard: Monitor usage, tokens, errors, and configure the gateway.
  • Content Guardrails: Configurable content filtering and safety measures.
  • Enterprise Logging: Integrate with Splunk, Datadog, and Elasticsearch.
  • System Prompt Injection: Intercept and inject system prompts into requests.

Use Cases

  • Managing diverse LLM integrations within a single application.
  • Ensuring high availability for AI-powered features.
  • Reducing LLM API costs through caching.
  • Implementing rate limits for API usage control.
  • Monitoring and logging AI service interactions.
  • Enforcing content safety policies across different LLMs.
  • Standardizing LLM interactions via a unified gateway.

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