Reduce LLM API bills: prompt compaction, model-switching, caching

Teams building AI features—startups, product teams in mid-market companies, and enterprise AI platforms—are seeing LLM API bills rise as they add more calls per user; with roughly 2 million companies spending an average of $12K/year, the addressable market is about $24B. High-volume use cases like chat, summarization, search, and personalization face eroding unit economics and lack centralized controls for cost-aware routing, caching, and governance. You could build a developer-focused middleware that combines prompt compaction (token trimming, dynamic instruction templates), model-switching (cost/latency-aware routing across model sizes and providers), and multi-layer caching (response and embedding caches with freshness controls), delivered as an SDK plus proxy and dashboard with a policy engine. The product should emphasize easy integration, attribution for billing, and safeguards to preserve semantic fidelity; conservative pilots could plausibly target 20–40% reductions in API spend for API-heavy workflows. The timing is favorable: model proliferation and diversity create tangible optimization opportunities, enterprises now expect observability and governance for AI spend, and the market metrics (Market Score 92/100, Revenue Potential 88/100) point to strong demand. Competition is medium, so prioritizing developer experience and enterprise integrations can win early adopters. To stand out you will need best-in-class compaction techniques, rigorous cross-model calibration to avoid accuracy regressions, and tight billing and SSO integrations—while being prepared for challenges such as shifting model pricing/APIs, latency trade-offs, fragmented vendor ecosystems, and longer enterprise sales cycles.