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

Composable middleware for LLM inference to enforce safety, caching, and sanitization targets a $6.0B = 500K engineering teams × $12K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% YoY (industry estimates for AI infrastructure and LLM-related tooling growth, based on analyst coverage and vendor growth rates).

Key trends driving demand: Centralization of inference controls — teams are consolidating safety, caching, and routing into shared layers to control cost and compliance, creating demand for middleware.; Shift to provider-agnostic stacks — companies want portability across LLM providers to avoid vendor lock-in, which creates an opportunity for standard middleware.; Rising API costs — as token and model costs rise, engineering teams prioritize caching, batching, and cost-aware routing to reduce spend.; Regulatory and privacy pressure — data protection and auditability requirements push teams to adopt standardized redaction and policy enforcement in inference..