Skyrocketing LLM bills — automated strategies to cut inference & infra spend

Skyrocketing LLM bills — automated strategies to cut inference & infra spend targets a $15.0B = 500K AI-using companies x $30K ACV (annual spend on inference, infra & tooling) total addressable market with medium saturation and a year-over-year growth rate of 35%+ (driven by AI adoption and cloud service growth).

Key trends driving demand: Model proliferation -- dozens of public and private LLMs create optimization choices (price/perf tradeoffs) that tooling can exploit.; Pay-as-you-go inference -- variable billing makes unit economics volatile and creates immediate ROI for optimization.; Edge & quantized runtimes -- cheaper inference runtimes and model distillation open new routing opportunities.; Observability-first development -- teams demand per-request, per-model telemetry to justify spending to execs..