Enterprise search tools: the best enterprise search software compared for 2026

Enterprise search tools connect to the systems a company already runs, build one index across them, and let anyone ask a question in plain language and get an answer back. The modern ones return a written answer with citations rather than a page of blue links, and they check the asker's permissions before including a document in the answer.

That last part is what separates an enterprise search tool from a consumer search engine. Public search has no notion of who you are. Inside a company, the same query has a different correct answer depending on whether you are in finance, engineering or a contractor account. A tool that ignores this is a data leak with a search box on it.

There is no single best enterprise search software, because the right answer depends on how many apps you need covered and how much you want to build. If your knowledge is spread across Drive, Slack, Notion, Confluence and Jira, a cross-app assistant is the fit. If everything is already in Microsoft 365, Copilot has an advantage you should not ignore. If you are building a search product for customers, you want infrastructure like Coveo or Lucidworks, not a workplace assistant.

The three groups map cleanly onto the table above:

• Cross-app assistants. InSearch, Glean, GoSearch, Dropbox Dash, Onyx. They index your whole SaaS stack and answer from all of it. Best when your knowledge is genuinely scattered.
• Platform-native search. Microsoft 365 Copilot, Gemini Enterprise, Amazon Q Business, Atlassian Rovo. Excellent inside their own ecosystem, noticeably weaker outside it. Best when 90 percent of your content lives in one vendor's cloud.
• Search infrastructure. Coveo, Lucidworks, Sinequa, Elastic. Powerful, tunable, and a project rather than a purchase. Best when you have a search team.

Two of the biggest names are no longer options for new buyers. Amazon Kendra went into maintenance mode on June 30, 2026 and stopped taking new customers on July 30. Amazon Q Business closed to new customers on July 31. Both remain supported for existing deployments, and we wrote up where Kendra customers should land if you are in that position.

Start from your content, not from a feature grid. List the five systems that hold the answers people actually hunt for, then eliminate every tool that cannot index all five on day one. That one step usually cuts a sixteen-vendor list to four, and it is a much better filter than counting connectors, because a vendor claiming 200 connectors and a vendor claiming 40 are often equally useful to you if the five you need are covered.

After that, the questions worth asking in a demo are narrow and specific:

1. Show me a wrong answer. Ask something your content genuinely does not cover. A good tool says it does not know. A bad one invents something confident.
2. Show me the citation. Click through from the answer to the source paragraph. If the citation is a document-level link and you cannot see which passage the claim came from, verification costs as much as the search did.
3. Log in as a contractor. Run the same query as a restricted account, live in the demo. Permission handling either works at query time or it does not, and this takes 30 seconds to prove.
4. What happens when a permission changes? Ask how long a revoked user keeps seeing a document. Tools that sync identity on a schedule have a real revocation window, and that window is a compliance answer you will need.
5. What is the second-year price? Seat cost is often the smaller half. Index capacity, credit packs and overage meters are where the bill grows.

The checklist version of this, with scoring, is in our guide to evaluating enterprise search software.

Published per-user prices in this market run from about $15 to $49 per user per month, with most cross-app tools clustered between $20 and $35. That range covers only the seven vendors that publish anything. The other eight quote per deal, and reported figures for those sit far higher, often with seat minimums attached.

The trap is that seats are frequently not the whole bill. Amazon Q Business charges $3 or $20 per user and then bills index capacity by the hour on top. Copilot Studio sells prepaid credit packs at $200 for 25,000 credits, and a single SharePoint agent interaction consumes 12 of them. Gemini Enterprise bills a seat subscription plus consumption above your quota. When you model cost, model it at your real document volume and query rate for year two, not at the seat count in the quote.

Our own pricing is on the pricing page: $20, $32 and $49 per user per month, billed annually, with no usage meter behind it. We publish it for the same reason the column exists in the table above. A vendor that will not name a price before a discovery call is telling you something about how the negotiation will go.

The good ones do, but the mechanism differs and the difference matters. Permission-aware search enforces each source system's own access controls at query time, so a document is filtered out of your answer because you cannot open it in Drive or Confluence right now, not because someone remembered to tag it. The weaker pattern is a periodic identity sync, where a revoked permission stays effective inside search until the next sync runs.

Ask every vendor two things: whether permissions are checked at query time or at index time, and how long a revocation takes to propagate. Gemini Enterprise, for example, documents an identity sync schedulable from every 30 minutes to every 7 days, and that upper bound is your worst-case exposure window. Amazon states plainly that without IAM Identity Center, all Amazon Quick users automatically receive access to connected Q Business indexes.

InSearch inherits and enforces each connected source's access controls on every query, which is the whole design of permission-aware AI search. We go through the threat model in detail on secure enterprise AI search.

Enterprise search finds and ranks documents. An AI knowledge assistant reads them and writes you an answer. In 2026 almost every serious enterprise search tool does both, so the labels have largely merged, and the distinction that survives is whether the product hands you sources to read or a conclusion to check.

The reason to care is verification. An assistant that writes a paragraph without citations is asking you to trust it on a question where being wrong has consequences, like a PTO policy or a security requirement. An assistant with clickable inline citations gives you the conclusion and the receipt at the same time, which is why we treat cited AI answers as non-negotiable rather than a feature. If you want the retrieval side explained, RAG versus enterprise search covers how grounding works underneath.