Composable inference middleware to enforce safety, caching, and sanitization

Composable inference middleware to enforce safety, caching, and sanitization targets a $3.0B = 500K developer/ML teams × $6K ACV (developer tooling + middleware average) total addressable market with medium saturation and a year-over-year growth rate of 30% YoY growth estimate for developer AI tooling and MLOps market (source: industry reports and growth in LLM adoption).

Key trends driving demand: LLM adoption in production — as more products embed LLMs, teams need consistent guardrails and cost controls which middleware can provide.; Regulatory and compliance focus — data protection and content safety requirements are pushing companies to adopt inference-level enforcement and audit trails.; Shift to polyglot providers — businesses use multiple LLM providers, creating demand for vendor-agnostic abstraction layers that normalize differences.; Cost optimization pressure — inference spend is material, so caching, batching, and model routing optimizations are high-value opportunities..