Search can't retrieve what nobody wrote down. That's the whole comparison in one line, and it's why "a cheaper Glean for small teams" is usually the wrong thing to go looking for.
Glean is an enterprise AI platform built around search, assistants and agents over the systems a company already runs: chat, email, documents, tickets, CRM. Tatara is a governed document store, a company brain that agents connect to over the Model Context Protocol (MCP). Every agent write carries a required type, title, description and tags, and is stamped with provenance. Both end with an agent giving a grounded answer. They start from opposite assumptions about where the knowledge is now.
What is each product for?
Glean is for finding knowledge that already exists and is scattered. Its value comes from federation: connect the twenty systems a large organisation runs, index them, and make the whole surface answerable. The more systems and people there are, the more that's worth.
Tatara is for writing down knowledge that exists in nobody's system, so agents have something correct to read. Its value comes from governance: a small, deliberate set of documents a team stands behind, in a shape agents can use without guessing.
These aren't two versions of the same idea. One indexes a sprawl. The other saves you from needing one.
Why do "search everything" and "write it down" solve different problems?
"Search everything" and "write it down" solve different problems because they start from different conditions. Search assumes the knowledge is already written down somewhere. Writing it down assumes it isn't yet.
A five-hundred-person company has already written down its pricing, its onboarding process and its refund policy. The trouble is that those facts live in four Slack threads, an old deck, a document nobody can find and one person's head. Federated search is exactly right for that. It surfaces what exists.
A nine-person company has a different problem: its pricing logic has never been written down at all. It lives in the founder's head and gets re-explained in every chat window. Indexing that company's systems returns very little, because there's very little there. It doesn't need a better index. It needs the twenty documents to exist.
Buying a search layer to solve an authoring problem gets you a very good search over an empty room.
What does the small-team version of the problem look like?
For a small team, the problem shows up first as repetition, then as a wrong answer.
- You paste the same background into Claude, ChatGPT and Gemini every week, and the three of them disagree because each got a different paste.
- An agent tells a customer something that was true last quarter, confidently, because it had no way to know it was missing context.
- Two people describe the ICP differently on the same call, and both are half right.
None of that is a retrieval failure. The knowledge simply isn't there. The fix is to write the durable facts once, in a form every assistant reads, which is what a company brain is.
What does a governed brain give an agent that search cannot?
A governed brain gives an agent a single current text with a known author, instead of a ranked list of candidates.
When an agent searches across systems, it gets plausible material and has to decide which piece is authoritative. To a retrieval model, a year-old deck and yesterday's decision look much the same. When an agent reads a governed brain, the pricing document is the pricing document: one artifact, currently owned, with the structure that says what it is.
That structure is enforced on every agent write:
- A free-form type, so a document declares what it is before anything reads its body.
- A title and one-line description, so the next agent can decide from the index whether to open it.
- Tags steered to a registry. An agent can coin a new one, and it lands in a review queue for a person to file or merge.
- Provenance, so an agent's output can never pass as something a person wrote and signed off.
Writes missing any of those are rejected with an instructive error, and the agent corrects and retries. Documents stay plain Markdown, so nothing is locked in a format you can't read later. That enforcement, and why provenance matters once agents start writing, is the part search layers aren't trying to solve.
When is Glean the right choice?
Glean is the right choice when your knowledge really does exist across many systems and many people, and the problem is that nobody can find it.
Choose an enterprise search platform if you have hundreds of employees, a long tail of tools accumulated over years, and real procurement, permissions and compliance requirements around who may see what. Tatara isn't a substitute for that. It doesn't index your ticketing system or crawl your email, and it won't answer "who owns this account" from your CRM. If that's the job, a federated search layer is the right tool and you should buy one.
Also choose it if the deciding constraint is organisational rather than technical. Large companies often need a vendor that has already passed their security review. That's a legitimate reason to pick a product, and not one a smaller company can argue you out of.
When is Tatara the right choice?
Tatara is the right choice when your team is small enough that its knowledge has never been written down, and agents are already doing real work on it.
| Your situation | The shape that fits |
|---|---|
| Knowledge exists, spread across many systems and people | Federated enterprise search |
| Knowledge exists mainly in a few people's heads | A place to write it down |
| The failure is "we cannot find it" | An index |
| The failure is "it was never written" | A governed brain |
| Agents mostly read | Either, if reads are cheap |
| Agents also write back | Governed writes with provenance |
For a small team, the last row matters most. An ungoverned corpus starts degrading once agents write to it, and a small team has nobody whose job is cleaning it up.
Can you use both?
Yes. At a certain size running both is the sensible arrangement, because they sit at different layers.
A governed brain is a source: a small set of documents your company stands behind. A search platform is an index over sources, including that one. Writing your positioning down carefully doesn't make it any harder to index later. The two only compete when a small team mistakes the second for the first and buys an index before it has anything worth indexing.
What should you do first?
Before choosing either, write ten documents. Then decide.
Take the ten questions your agents get wrong most often (pricing rules, who the product is for, how onboarding actually runs, what you will and won't do) and write one document for each. It takes an afternoon. Tatara's free plan holds 500 documents, fifty times that first pass, and doesn't ask for a card. Agents can connect to it over MCP the same day, read from it and make governed writes to it. The other plans are on the pricing page.
If, at the end of that afternoon, your problem still reads as "we can't find what we already wrote", you want search, and you should go and buy search. If it reads as "we finally wrote it down and every assistant now says the same thing", you've found the tool for the problem you actually had. Tatara doesn't promise a migration path from anywhere, so either way the first move is writing, not moving.
