A Glean alternative built as a context graph, not enterprise search
Glean is enterprise search. Suda is a context graph your AI agents query directly. Here is the difference and when each one fits.
If you are evaluating Glean, you are trying to solve a real problem: your company’s knowledge is spread across tools and hard to reach. Suda solves the same problem from a different starting point. Glean is enterprise search built for people. Suda is a context graph built for AI agents. This piece lays out the difference honestly so you can pick the right one.
What Glean is good at
Glean indexes your company’s tools and gives people a strong search experience and an assistant on top of it. If the primary job is “help employees find things across our tools,” it does that well and it is a mature product. No need to pretend otherwise.
Where the two diverge
The divergence is who the answer is for and how it is assembled.
- Glean retrieves and ranks results, then generates an answer over them. The model of the world underneath is search: documents, relevance, ranking.
- Suda builds a context graph: it models how facts connect, which one is current, and what supersedes what, then hands an agent exactly the context it needs. The model underneath is a graph, not a ranked list.
That difference shows up when an AI agent, not a person, is asking:
| Glean (enterprise search) | Suda (context graph) | |
|---|---|---|
| Built primarily for | People searching | AI agents querying |
| Underlying model | Search and ranking | Graph of connected facts |
| Stale and conflicting facts | Surfaced in results | Retired and resolved automatically |
| How agents connect | Assistant and APIs | Native over MCP |
| Context sent to a model | Retrieved passages | Only what is needed (~85% less) |
| Personal brain | Enterprise focus | Company brain and personal brain |
When to pick which
- Pick Glean if the main goal is human search across enterprise tools and you want a broad, established assistant for employees.
- Pick Suda if the main goal is giving AI agents correct, current, connected context, especially if those agents run in your own stack and speak MCP, and if you care about sending the model less text to cut cost.
Some teams run a search tool for people and Suda for their agents. They are not the same job.
Trying Suda
Suda connects to more than 700 sources, including Notion, Slack, and Linear, builds the graph, keeps it current, and serves it to any agent over MCP with permissioned access. Setup is one command:
npx suda connectFor the architecture behind the comparison, read context graph vs RAG.