Hidden AI spend - automated observability and optimization for infra and models

Executive Summary

Many mid and large enterprises running production AI lack visibility into model-level and infra-level spend, so teams in finance, ML engineering, and cloud ops cope with unpredictable bills, idle GPU time, suboptimal instance choice, and no clear cost attribution to models or features. The addressable base is roughly 200,000 mid+ enterprises, giving an $18.0B market at a $90K ACV, which aligns with the market score of 95/100 and high revenue potential of 88/100. You could build an automated observability and optimization platform that ingests telemetry, model metadata, and cloud billing to produce per-model cost attribution, cost-per-inference and cost-per-training metrics, and then recommend or apply optimizations such as instance resizing, spot scheduling, batching, precision reduction, and autoscaling. The product should include policy controls, audit trails, and prebuilt connectors for Kubernetes, SageMaker, Vertex AI, and major cloud billing APIs to shorten time-to-value. Market timing favors this idea because AI commoditization and MLOps maturity mean teams will treat models as recurring IT costs and expect predictable bills, while cloud pricing complexity - many instance types, spot markets, and discount models - creates clear optimization upside. To stand out against medium competition from APMs, cloud cost tools, and MLOps vendors, focus on high-fidelity model-level attribution, closed-loop optimization, and measurable 90-day pilot savings; the main challenges will be instrumenting legacy pipelines, reconciling cross-cloud billing, and proving ROI in enterprise sales cycles.