Keep LLMs stateful by providing a persistent runtime memory and agent layer

Keep LLMs stateful by providing a persistent runtime memory and agent layer targets a $4.8B = 600,000 developer teams × $8K ACV (annual spend on developer tooling and runtime subscriptions for AI builders) total addressable market with medium saturation and a year-over-year growth rate of ≈25% YoY combined growth for AI developer tooling and platform services (industry estimates for AI platforms and developer services, 2024-2027).

Key trends driving demand: Developers are productizing LLM features — as experimentation turns into production, teams need durable runtime semantics which creates demand for persistent runtimes.; Vector databases and retrieval-augmented approaches are standard practice — that creates an opportunity to build opinionated runtimes that integrate memory and execution consistently.; Managed infra and serverless patterns lower the operational cost of running stateful services, making a hosted runtime commercially viable for SMBs and mid-market companies.; Enterprise AI teams demand compliance, observability, and deterministic behavior for agents, which a specialized runtime can surface as a differentiator..