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AWS-Powered Dynamic LLM Routing: Reducing Costs, Maintaining Quality
- Dynamic model routing reduces LLM costs while maintaining high-quality responses across different use cases.
- The landscape of LLMs has expanded into a diverse ecosystem of models, each with its own strengths and cost structures.
- The first step is analyzing the complexity of incoming queries, followed by using AWS Step Functions State Machine and AWS Lambda Functions.
- DynamoDB Table stores execution results and metadata, and KMS encrypts keys for sensitive data.
- Our dynamic AI model routing approach needs to carefully analyze whether the overhead of complexity analysis justifies the potential cost savings.
- Dynamic model routing is most beneficial in scenarios with large token count differences.
- Skip complexity analysis if the system has low request volume, uniform input complexity, and similar model pricing.
- Our solution meticulously tracks and calculates the cost of every single AI interaction while storing comprehensive cost information in DynamoDB.
- The entire solution is implemented as a modular Terraform project, making it easy to deploy and customize.
- By expanding this architectural pattern, you can create an intelligent, cost-effective AI routing system that adapts to different use cases.
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