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

Many engineering orgs building AI coding assistants are now paying recurring per‑token inference costs and re-sending overlapping repository and system context on every query, which drives both bill shock and variable assistant behavior; platform teams, developer tool vendors, and about 2.0M developer organizations that would pay for company‑wide memory plans face this pain. A practical product is a knowledge‑graph cache layer that deduplicates and normalizes code and context into semantically linked nodes, serves high‑confidence deterministic responses or compact retrievals to the LLM, and exposes SDKs/IDE plugins, provenance/versioning, and enterprise governance controls. Packaged as a managed service with an integration plan priced around the $4.8K ACV used in our market sizing, the opportunity maps to a $9.6B addressable market; even modest penetration (0.1% = ~2k orgs) translates to roughly $9.6M in ARR, while 1% penetration approaches $96M. The timing is favorable: retrieval‑first/RAG patterns are standardizing developer flows, vector DB managed services have lowered infrastructure barriers, and rising metered LLM costs make predictable caching economics compelling. You can differentiate from plain vector cache startups by offering an explicit knowledge graph for relationship queries, tighter IDE/CI integrations that capture provenance, and measurable cost‑savings dashboards that translate hit rates into dollars. That said, technical and go‑to‑market risks are real — delivering high hit rates without stale answers, integrating with diverse build systems and monorepos, meeting enterprise security/compliance, and competing in a medium‑strength field of vector‑based incumbents will require focused engineering and strong developer experience.