gpt-load is a self-hosted AI gateway that routes requests across multiple LLM providers and credential types.
The tool addresses the complexity of managing multiple AI service providers and authentication methods in a single application. Rather than hardcoding API keys or switching between provider SDKs, developers deploy gpt-load as a central gateway that accepts requests and intelligently routes them to configured providers. It supports both API key and subscription account credentials, allowing teams to consolidate access to services like OpenAI, Anthropic, and Google Gemini through a unified interface. The gateway includes request scheduling, failover mechanisms to handle provider outages, and comprehensive logging and usage tracking to monitor consumption across channels.
gpt-load suits teams running self-hosted infrastructure who need to abstract away provider-specific details and manage multiple LLM services without embedding provider logic throughout their codebase. It works well for applications that require high availability across providers, need to track usage per channel or credential, or want to experiment with different LLM backends without code changes. Organizations already committed to self-hosting will find it a natural fit; those preferring managed solutions should evaluate whether the operational overhead justifies the control and cost visibility it provides.
The project shows active development with regular commits addressing bug fixes and feature additions. Pull requests receive timely review and feedback from maintainers. The codebase maintains consistent code quality standards with attention to testing and documentation. Issue discussions demonstrate engagement with user questions and feature requests, with maintainers providing detailed responses and guidance.
