Glean vs Raghim AI

Glean

Glean is the reference implementation of permission-aware enterprise search, and permissions are the hard part everyone else underestimates. It is also priced so that only companies with a real sprawl problem can justify it.

Pros

• +Permission inheritance from each connected system is done properly, not approximated
• +Answers cite and link the underlying documents rather than paraphrasing them away
• +Connector coverage across the mainstream SaaS estate is broad
• +Commercially solid: about $300M ARR by May 2026 at a $7.2B valuation

Cons

• −No published price anywhere, and contracts start in six figures
• −Value depends entirely on connector coverage of your specific stack
• −Index quality inherits whatever mess exists in the source systems
• −Overlaps increasingly with Microsoft and Google's bundled offerings, which cost nothing extra

Raghim AI

Raghim's whole pitch is control over data location: everything from document RAG to natural-language database queries can run self-hosted via Docker rather than only in someone else's cloud, which matters for regulated industries that can't send data off premises. That flexibility comes at the cost of a heavier setup than a plug-and-play SaaS chatbot, and there is no visible pricing to gauge cost until you talk to the vendor.

Pros

• +Genuine self-hosted deployment option via Docker, not just a hosted-only product with a compliance label
• +Combines document RAG, SQL and database natural-language queries, and voice AI in one platform instead of three separate tools
• +Enterprise controls such as RBAC, audit logging, retention policies and IP allowlisting are built in rather than bolted on

Cons

• −No published pricing, so budgeting requires a sales conversation
• −Self-hosted deployment requires real infrastructure and Docker operations work
• −Independent, non-vendor reviews of real-world deployments are hard to find

Which has a public API, Glean or Raghim AI?

Both ship a public API, so either can be built on; the fact table above shows the platforms each one runs on.