GPT-Load's Enterprise API Gateway Build Guide
Enterprises integrating multi-model APIs often face problems such as protocol differences and billing confusion. Using GPT-Load's standardized agent function can:
- protocol conversion: built-in OpenAI-compatible format converter to unify Gemini/Claude responses into the same JSON structure (automatically handled via /proxy/routing)
- Endpoint Aggregation: Aggregation of chat/completion/embed interfaces of different models into a single domain, e.g. /proxy/openai/v1/chat/completions → /v1/chat
- Audit trail: The MySQL version supports full request logging with fields for model type, elapsed time, token usage, etc.
Implementation process: 1) Install enterprise version dependencies via go mod tidy; 2) Configure DATABASE_DSN for group-level MySQL; 3) Deploy multi-availability zone instances using k8s. An e-commerce customer case shows that the program reduces interface maintenance costs by 70%, while saving 15% in API costs through intelligent routing.
This answer comes from the articleGPT-Load: High Performance Model Agent Pooling and Key Management ToolThe
May not be reproduced without permission:AI productivity tools " How to achieve unified access to multi-model APIs in enterprise-level AI applications?
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