Self-hosted embeddings + vector index platform for private, cost-controlled RAG

Self-hosted embeddings + vector index platform for private, cost-controlled RAG targets a $6.0B = 2M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% YoY — vector DB and AI infrastructure market growth driven by LLM adoption (industry analyst synthesis, 2024).

Key trends driving demand: Open-source embedding models and optimized runtimes have reduced inference cost and enabled local/offline embedding generation, creating opportunities for local-first products.; Rising cost sensitivity from heavy embedding usage is forcing engineering teams to seek hybrid approaches that combine local inference with managed fallbacks.; Privacy and data residency requirements are pushing companies to prefer on-prem or private-cloud solutions over fully hosted APIs for embeddings.; Standardization of RAG tooling (LangChain, Llama-index) increases the addressable audience that needs reliable embedding pipelines and index migration tools..