Uber's CTO told The Information in April 2026 that AI coding tools had already exhausted the company's entire 2026 AI budget just months into the year. This is not an isolated incident. It reflects a structural problem in how enterprise AI inference is billed and consumed.
The problem is not that AI is too expensive per query. The problem is that most enterprise AI workloads contain a high proportion of repeated or semantically equivalent queries that are being sent to the model as if they were entirely new — and billed accordingly.
In document-heavy workflows — contract review, lease abstraction, due diligence, research synthesis — the same questions appear constantly across teams, sessions, and documents. Without a memory routing layer, every one of those queries triggers full inference at full cost.
The strongest signal that repeated queries are economically significant comes from the model providers themselves. Both OpenAI and Anthropic have introduced discounted pricing for cached inputs — a direct acknowledgment that a meaningful share of production AI traffic is repeated context.
Independent academic research on semantic caching of LLM responses has produced consistent results across multiple studies and architectures.
| Study | Key Finding |
|---|---|
| GPT Semantic Cache arXiv 2411.05276, 2024 |
API call reductions up to 68.8%, cache hit rates 61.6–68.8%, positive hit accuracy above 97% |
| Alura / Real-world deployment ResearchGate, 2024 |
45.1% of LLM requests served from cache; response times 4–12x faster on cache hits |
| Cache Saver framework EMNLP 2025 |
Up to 60% cost and carbon emission reduction in LLM reasoning workflows |
| Developer benchmark DEV Community, 2026 |
72% API cost reduction using MPNet embeddings with cosine similarity on production query logs |
Across all studies, the consistent finding is that semantically similar queries are common enough in real workloads to make memory routing economically significant — and that accuracy remains high when the routing layer is designed carefully.
MemStorage routes each incoming query through a three-tier decision layer before it reaches the model.
The incoming query is scored against stored memory using similarity matching. If a high-confidence match is found, the stored answer is returned instantly. Zero tokens consumed. Zero API cost.
If the match score falls in an uncertainty band, a lightweight confirmation call uses approximately 20 tokens to verify semantic equivalence before serving the stored answer.
If no match exists, the query escalates to the model. The answer is stored for future reuse. Every future similar query becomes a free memory hit.
For public reproducibility, we used real commercial lease agreements filed as exhibits in SEC EDGAR filings. These are genuine enterprise documents — not synthetic templates — and they represent the kind of repetitive document workflow that makes memory routing most valuable.
We ran repetitive extraction queries across both documents simulating how legal, CRE, and operations teams actually use AI in document review workflows. Query categories included: rent schedule and escalation, parking rights, security deposit terms, renewal options, tenant improvement allowances, permitted use, broker identification, and late payment terms.
Each category was tested with the original query and two to three semantic paraphrases — for example "How many parking spaces does the tenant receive?" alongside "What is the parking allocation for the tenant?" and "What parking rights does athenahealth have?" — to measure whether the routing layer correctly identified semantic equivalence across varied phrasing.
You can run this benchmark yourself at memstorage.com/app. The live demo is loaded with both SEC lease documents and routes queries in real time.
Based on external research and the structural economics of enterprise AI workloads, a well-designed memory routing layer in repetitive document workflows should reasonably target:
We are looking for enterprise teams with repetitive AI workflows — legal, CRE, insurance, financial services, or any document-heavy operation — to run a 30-day pilot. You bring the workflow. We instrument the routing layer. At the end of 30 days you see your actual savings number.