Duplicate LLM responses cost you twice — dedupe & cache responses to cut API spend

Many engineering teams running production LLM workloads are being billed multiple times for equivalent requests — duplicate responses occur when organizations use multi-provider fallbacks, model ensembles, retries, or parallel prompt experiments. The pain is most acute for mid-to-large SaaS and data teams with high query volumes; targeting roughly 400,000 such businesses at an assumed $30K ACV implies a $12.0B market opportunity (Market Score 90/100). A practical product is a dedupe-and-cache layer: an edge-friendly proxy that canonicalizes requests, applies deterministic hashing plus low-cost embedding-based fuzzy matching against a vector-indexed cache, and returns cached outputs or routes to the provider while logging precise cost attribution and observability. With common duplication patterns, such a layer could plausibly cut billed API spend by 20–60% depending on reuse rates and TTL policies; Revenue Potential scores highly (88/100) because savings translate directly to customer ROI. Offerings should include on-prem or hybrid deployment for privacy-sensitive workloads and SDKs to integrate into retry/fallback logic to minimize developer friction. This is an opportune moment: LLM commoditization is pushing teams to multi-provider stacks, edge and hybrid deployments create natural interception points, and cheap embeddings make semantic fuzzy-dedupe practical across paraphrases. To stand out you must focus on robust cross-provider deduplication, low-latency proxying, strong false-positive controls, and clear metrics showing dollars saved; realistic challenges include model nondeterminism, integration complexity across evolving APIs and versions, and the adoption friction of inserting another critical network hop.