Composable inference middleware to enforce safety, caching, and sanitization for LLM pipelines

Composable inference middleware to enforce safety, caching, and sanitization for LLM pipelines targets a $5.0B = 500K companies × $10K ACV total addressable market with medium saturation and a year-over-year growth rate of 40% YoY (industry adoption of LLM infrastructure and AI platforms; sources: industry analyst reports and public cloud AI growth indicators).

Key trends driving demand: Rising production use of LLMs — more teams are deploying models beyond prototypes, creating demand for infrastructure that manages cost, safety, and latency.; Shift to multi-provider strategies — teams use multiple LLM providers to manage cost and risk, increasing need for provider-agnostic middleware.; Edge and low-latency inference — enterprises demand inference optimizations that lower token costs and latency, making in-process middleware attractive.; Regulatory and compliance pressure — privacy and AI safety guidance is driving companies to centralize policy enforcement and auditing of inference paths..