LiteLLM (Berri.ai)
an open-source platform that serves as a unified gateway for integrating and managing over 100 large language models (LLMs) from various providers, including OpenAI, Azure, Anthropic, Cohere, and Hugging Face. It offers an OpenAI-compatible API interface, enabling developers to switch between different models seamlessly without significant code changes. Additional features include cost tracking, team-based budgeting, rate limiting, automatic model fallback, and integration with observability tools like Langfuse and Prometheus. Ideal for platform teams and developers aiming to streamline access and management of multiple LLMs in production environments
plataforma de código aberto que atua como um gateway unificado para integração e gerenciamento de mais de 100 modelos de linguagem (LLMs) de diversos provedores, como OpenAI, Azure, Anthropic, Cohere e Hugging Face. Ela oferece uma interface compatível com a API da OpenAI, permitindo aos desenvolvedores alternar entre diferentes modelos com facilidade e sem necessidade de alterações significativas no código. Recursos adicionais incluem rastreamento de custos, orçamentos por equipe, limites de taxa, fallback automático entre modelos e integração com ferramentas de observabilidade como Langfuse e Prometheus. Ideal para equipes de plataforma e desenvolvedores que buscam simplificar o acesso e o gerenciamento de múltiplos LLMs em ambientes de produção.
an open-source platform that serves as a unified gateway for integrating and managing over 100 large language models (LLMs) from various providers, including OpenAI, Azure, Anthropic, Cohere, and Hugging Face. It offers an OpenAI-compatible API interface, enabling developers to switch between different models seamlessly without significant code changes. Additional features include cost tracking, team-based budgeting, rate limiting, automatic model fallback, and integration with observability tools like Langfuse and Prometheus. Ideal for platform teams and developers aiming to streamline access and management of multiple LLMs in production environments
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