Token-cost pain for AI coding assistants — knowledge-graph cache layer

Token-cost pain for AI coding assistants — knowledge-graph cache layer targets a $9.6B = 2.0M developer organizations x $4.8K ACV (company-wide memory & integration plan) total addressable market with medium saturation and a year-over-year growth rate of 30% (developer AI tooling and RAG adoption growth).

Key trends driving demand: RAG & retrieval-first architectures -- more apps persist context instead of re-prompting, increasing demand for memory layers; Rising token costs & metered LLM pricing -- organizations seek caching strategies to reduce recurring inference spend; Vector DB maturity & managed services -- easier infra lets startups build memory products quickly; Proliferation of coding assistants -- more sessions means repeated context and higher marginal token waste to address.