For Enterprise Folks - Is Building In‑House Agent Memory Worth It?

With all the recent threads here about self‑hosting, GLM‑5.x on consumer hardware, Bonsai in the browser, and Satya Nadella’s warning that companies “pay for intelligence twice” by giving cloud models proprietary knowledge, I’m curious how enterprises are thinking about AI memory. If you’re working in an enterprise setting (or advising one), I’d love to hear your perspective: Are you building your own in‑house memory stack for agents (RAG, knowledge graphs, event logs, long‑term profiles), or mostly relying on whatever memory features come with cloud LLM platforms? What concrete ROI have you actually seen from investing in that custom memory layer (fewer hallucinations, faster workflows, better reuse of knowledge, compliance wins, etc.)? When you factor in data risk (like the concern that cloud providers can learn from your proprietary workflows and become competitors), does the effort of building and maintaining an internal memory system feel justified? From your experience, is a well‑designed memory layer now “must‑have” infrastructure for serious agent deployments, or still a nice‑to‑have on top of good prompts and tools? Personally, it looks like a dedicated memory layer (structured logs, embeddings, project histories, user profiles) is becoming the real differentiator for agents, especially for self‑hosted and open‑weight setups that want to keep knowledge inside the organization. But I’m wondering if that actually shows up in enterprise ROI, or if teams are finding that the cost/complexity outweighs the benefits today. Would really appreciate concrete examples: what you built, what it cost, and what you got back.