ML-based LLM Request Classifier for Cost-Optimized Routing
The article describes a machine learning-based request classifier that routes prompts to the appropriate LLM tier (economy, standard, or premium) for cost optimization. The system uses feature extraction, an MLP model, and a semantic cache to achieve sub-2ms inference.
Why it matters
This approach can help businesses optimize costs when using large language models by intelligently routing prompts to the appropriate tier.
Key Points
- 1ML-based classifier to route prompts to appropriate LLM tier for cost optimization
- 2Features include token count, complexity, conversation depth, code/math/reasoning markers, language detection
- 3MLP model trained on 50K labeled samples, exported to ONNX for fast inference (<2ms)
- 4Semantic cache using Qdrant to catch near-duplicate prompts
Details
The article describes a machine learning-based request classifier that aims to optimize costs by routing prompts to the appropriate LLM tier (economy, standard, or premium) before sending them to a provider. The system extracts features like token count, estimated complexity, conversation depth, presence of code/math/reasoning markers, and language detection. It uses an MLP model trained on 50K labeled samples, with a rule-based scorer acting as the teacher. The model is exported to ONNX format for fast sub-2ms inference. The system also includes a semantic cache layer using Qdrant to catch near-duplicate prompts. The goal is to route simple requests to cheaper models while keeping complex ones on premium tiers for cost optimization.
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