Duplicate LLM responses waste money — dedupe and cache answers

Enterprises and mid-market engineering teams — together representing an addressable market of about $30B (roughly 300,000 organizations averaging $100K/year in LLM spend) — are routinely paying for repeated or semantically duplicate model generations, which shows up as higher token bills and procurement pressure. A conservative 5–15% reduction in token usage from deduplication and caching would equal roughly $5K–$15K saved per organization annually on that $100K baseline, so even modest effectiveness produces clear ROI. You could build a developer-first platform: an SDK and proxy that intercepts requests, computes embeddings, and uses a tenant-aware vector-backed cache with exact+semantic matching, answer-level TTLs, policy rules, and enterprise deployment options (SaaS, VPC, on-prem). Pair that with cost dashboards, hit/miss analytics, integrations for major LLM providers, and a pricing model tied to demonstrated savings to make procurement decisions straightforward. To differentiate in a medium-competition landscape, focus on semantic matching accuracy, explainable match decisions, strict data residency/privacy controls, and fast, low-latency inference so operators trust cached answers as much as live generations. Timing favors this play: token-based pricing creates a direct financial incentive to avoid re-generation, mature embeddings and vector DBs enable reliable semantic matching, and rising enterprise LLM spend makes cost controls a procurement priority. This is a high-reward but execution-heavy opportunity — pursue it if you can secure early enterprise pilots and shoulder the engineering and compliance burden; otherwise long sales cycles and integration complexity will limit traction.