performance bottleneck
AI applications commonly suffer from high latency and high costs, and Portkey can improve both metrics through intelligent caching and route optimization.
Method of implementation
- Enabling semantic caching
In the Cache setting turn on the option that the system will automatically cluster queries with similarity ≥ 90% (adjustable threshold) - mixed model strategy
Configure routing rules: simple queries → fast small models (e.g. GPT-3.5), complex tasks → high performance large models (e.g. GPT-4) - Monitoring Optimization
Regularly analyze cost/delay reports in Analytics and eliminate models that are not cost effective
Estimated effect
As shown by typical test cases, the solution can increase the response speed of regular queries by 3-5 times and reduce the monthly API cost by 35%-60%.
This answer comes from the articlePortkey: a development tool for connecting multiple AI models and managing applicationsThe
May not be reproduced without permission:AI productivity tools " How to improve the responsiveness and reduce the cost of AI applications?
