Every question their AI answered had likely been answered before. Nobody knew. The bill kept growing.
A mid-market SaaS platform with 400,000+ users deployed a large language model to handle customer support queries across four product lines. The deployment was considered a success: resolution rates improved, handle times dropped, and CSAT scores climbed.
Twelve months in, the monthly inference bill had grown to $41,000. Engineering flagged it. Leadership asked questions. Nobody had a clear answer because nobody had visibility into what the model was actually being asked.
Up from an initial projection of $9,200/month at rollout. Cost had scaled 4.5x while query volume had grown 2.1x.
No logging, no categorization, no pattern analysis. Every query was treated as a unique event regardless of content.
The platform had no memory layer. Every query hit the model cold, even questions asked and answered thousands of times before.
MEMStorage was integrated in read-only audit mode for 14 days before any routing was activated. The goal was to map the query landscape before touching production traffic.
The first report came back and nobody believed it. Nearly three-quarters of all queries were repeats. The model had answered the same questions tens of thousands of times and charged full price every single time.Head of Infrastructure, anonymized enterprise client
MEMStorage sits above the model as a routing layer. No changes to the underlying AI, no retraining, no new model. Queries are intercepted, classified, and routed in milliseconds before any inference cost is incurred.
Query matches a stored response with high confidence. Returned instantly with no model call. Covers 41% of all traffic.
Query matches stored response semantically but not exactly. A fast, low-cost confirmation call verifies before serving. Covers 32% of traffic at ~15% of full inference cost.
Genuinely new question with no close match in memory. Full model call executed and result stored for future routing. Covers 27% of traffic.
MEMStorage connected via API. No traffic routing yet. Query logging and classification begins in the background.
3.4M queries analyzed, classified, and clustered. Memory populated with high-confidence response pairs. The 73% repeat rate identified and quantified.
Confidence thresholds tuned in staging. Edge cases reviewed. Team validated that Tier 2 semantic responses matched expected quality bar.
Full three-tier routing live in production. First day: 68% of queries intercepted before reaching the model. Cost impact immediate.
SEC EDGAR corpus · Fintech / legal workflows · Peer-reviewed research citations
Most teams don't know. The audit is free, takes 14 days, and requires no changes to your production system.
Request a pilot auditPatent pending · memstorage.com