Solution: Intelligent Key Polling with GPT-Load
Multi-model API key management challenges are reflected in three levels: keys are scattered and stored easily lost, quotas are unevenly used, and manual switching is inefficient. The following is a professional solution based on GPT-Load:
- Centralized Management ArchitectureAdd all keys (OpenAI/Gemini/Claude, etc.) in the web management interface, the system automatically categorizes and stores them in the database, supports MySQL/PostgreSQL cluster storage.
- dynamic polling algorithm: When a key is detected to have reached the rate limit, it automatically switches to the backup key, and synchronizes the state across nodes through Redis.
- Quota visualization and monitoring: real-time display of each key usage in the management interface, support for setting priority policies (e.g., prioritize the use of keys with more remaining quota)
Specific implementation steps: 1) access port 3001 after Docker deployment; 2) click the Add button on the key management page; 3) fill in the platform to which the key belongs and the note information; 4) enable the automatic load switch. Advanced Tips: Configure AUTH_KEY via .env file to strengthen the security of the management side, combined with Makefile to customize the polling policy.
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 solve the problem of multiple large models API key management confusion?
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