Vector Retrieval & Gateway Optimization
Pure Edge Computation
LLM Semantic Cache Hit Rate & Cost Savings Calculator
Models semantic vector embedding similarity thresholds and cost savings for enterprise AI gateways.
Formula:
Hit Rate = f(Cosine Threshold, Query Clusters); Monthly Savings = Tokens Saved * Price/M
Model Parameters
Outputs & Underwriting Summary
Calculation Status
VECTOR CACHE REDUCED
Verified Model
Predicted Cache Hit Rate
24.4%
Intercepted: 244,000 queries
Monthly Token Cost Savings
$732.00
Tokens Intercepted: 292.8M
Semantic vector cache predicts a 24.4% hit rate at 0.92 cosine similarity threshold. Offloading 244,000 queries eliminates 292.8M LLM tokens, saving $732.00 monthly.
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