Cut LLM token waste: detect hidden token sinks and save cost

Many engineering teams are paying for tokens they never intend to use: hidden token sinks such as verbose system prompts, repeated context in RAG pipelines, duplicated embeddings, long chat histories and inefficient tokenization can inflate bills by 10–30% or more. This hits SaaS companies, enterprise AI teams, and multi-product developer platforms—the same ~600,000 companies spending an average of $40,000 per year on LLM/APIs (a $24.0B market) that directly measure tokens to control costs. You could build a cross‑provider developer tooling platform that instruments and attributes tokens end-to-end: deterministic tokenization across vendors, token-level tracing and hot‑path detection, a prompt linting engine, automated compress/cache/prune recommendations, and SDKs/plugins for CI and pipeline integrations. Offer real‑time dashboards, historical analytics, policy-driven alerts and CI checks that estimate savings and optionally implement safe refactors, with a SaaS pricing model and an outcome tier tied to verified savings. Include connectors for OpenAI, Anthropic, Azure, AWS and self‑hosted models plus privacy-preserving telemetry so customers get value without sending raw user data. The timing is strong because token-based pricing, growth in RAG and chat memory, and model proliferation make token waste both visible and costly; with a market score of 92/100 and revenue potential of 88/100, the ROI for customers is straightforward when you can prove 10–30% savings. To stand out you must deliver deterministic, cross‑vendor token accounting and seamless developer UX—those are defensible advantages—but expect engineering complexity, privacy concerns, and adoption friction, so prioritize quick wins, measurable first‑month savings, and tight integrations with existing observability and cost tools.