The Rise of CaaS: Context-as-a-Service for Agentic AI — Omer Primor, Bright Data

Web data is evolving from a static information source into dynamic context for AI agents, necessitating a shift in how developers approach data retrieval. While AI search engines provide immediate, ad-hoc access, they struggle with high-frequency, persistent knowledge work due to compounding token costs and rapid data decay. Context-as-a-Service (CAS) providers offer structured, vertical-specific data, yet they often lack the breadth of general search. For high-scale, recurring tasks, building custom, self-healing scrapers allows for data ownership, eliminating ongoing token burn and enabling deeper integration with proprietary business logic. Determining the optimal strategy requires identifying the "tipping point" where the upfront investment of building custom pipelines yields superior cost-efficiency and performance compared to renting context through third-party services.