Reduce runaway AI API costs by profiling, routing, and optimizing models

Reduce runaway AI API costs by profiling, routing, and optimizing models targets a $6.0B = 300,000 businesses × $20K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% YoY (estimated; driven by enterprise LLM workload growth and increasing AI infra spend; source: industry forecasts for AI infrastructure growth).

Key trends driving demand: Rising unit cost for LLM inference — organizations are urgently optimizing production workloads to reduce recurring API and energy taxes, creating demand for cost tooling.; Multi-model orchestration is mainstream — runtimes and SDKs now allow switching models at call-time, enabling automated downgrade strategies.; FinOps and cloud cost management expectations are moving into AI — procurement and finance teams now treat LLM spend as a recurring op-ex item, accelerating purchase cycles for governance tools.; Privacy and on-prem options increasing — demand for VPC or hybrid deployments is creating willingness to pay for connectors that route sensitive calls off public APIs..