Best Enterprise AI Search Tools

Glean is the best managed default for a company-wide employee-search rollout.

Its package combines broad connectors, permission-aware indexing, a knowledge graph, generated answers, and an opinionated user experience.

We would choose differently for a Microsoft-centered company, a developer platform, an external search product, or a self-managed stack.

We found no current controlled benchmark that tests these products on the same corpus and permission model. This is a fit ranking from first-party product, pricing, and availability documentation. We have not run a private corpus through nine of them.

Name the constraint before the demos. A shortlist assembled without one converges on whichever product demos best.

Nine products sit on the table, and only one is a default

Table 1 Download CSV

Product	Best fit	Pricing shape	Main limit to test
Glean	Managed employee search across many SaaS systems	Quote	Contract cost, connector depth, and admin control
Microsoft 365 Copilot	Microsoft 365 organizations	$30/user/month, annual, qualifying plan required	Coverage outside Microsoft Graph
Google Cloud Agent Search	Teams building search into apps	Queries, generation, and storage	Engineering and evaluation burden
Perplexity Enterprise	Web research plus connected company sources	Pro $40/user/month, Max $325/user/month	Internal-source governance and answer controls
Elastic	Search teams that want low-level control	Usage-based serverless or self-managed	Relevance engineering and operations
Onyx	Open-source and self-hosted requirements	Free OSS core; paid cloud and enterprise tiers	Connector coverage and who operates it
Coveo	Customer service, commerce, and knowledge search	Quote, entitlements vary by solution	Packaging complexity and query entitlements
Guru	Search plus verified knowledge workflows	Plans start at $25/seat/month	Knowledge-maintenance process fit
SearchBlox SearchAI	Fixed-cost cloud or on-prem search	Quote, fixed-cost positioning	Smaller ecosystem and implementation evidence

Amazon Q Business is absent from the recommended shortlist for new buyers. AWS documentation says the product stopped accepting new customers after July 31, 2026. Existing customers can continue. New AWS-centric evaluations should start with Amazon Quick rather than assume an expiring entry path.

Only the first row is a default. Every other row is a specific constraint that outranks packaging: an estate concentrated in one vendor, a product surface that needs its own interface, a compliance boundary, or a team that wants to own relevance.

We are writing about a first purchase. If an index already exists and the question is whether to move, the Glean alternatives comparison handles migration cost and per-product failure modes, and our architecture explainer covers how the retrieval pipeline works underneath all nine.

Glean wins packaging, not every estate

Glean packages the pieces that usually become a program: connectors, real-time indexing, inherited permissions, semantic and keyword retrieval, a company knowledge graph, generated answers, and an employee-facing search experience. Its connector catalog lists more than 275 integrations.

Breadth reduces integration work, which is the main reason to shortlist it. Search engines can be technically strong and still fail because the organization cannot keep connectors, identities, permissions, and content freshness aligned.

Commercial and operational visibility is the limit. Public pages do not give a simple seat price, and a connector count does not establish depth. In a trial, test the systems carrying the messiest permissions rather than only Google Drive and Slack. Ask how deleted documents, group changes, shared links, and source outages propagate.

Glean wins when the organization wants one managed employee-search product and values implementation speed over infrastructure control.

Procurement should ask for a connector-level implementation plan, not a logo sheet. For each source, record supported objects, permission model, incremental update behavior, deletion handling, attachment extraction, historical backfill, expected indexing delay, and who owns failures. One deep connector to a critical system can be worth more than fifty shallow ones.

Microsoft Graph is a different product question

Microsoft 365 Copilot is the natural first test when SharePoint, Teams, Outlook, OneDrive, Word, and Entra already define the work graph. Microsoft lists the enterprise Copilot license at $30 per user per month on an annual commitment, with a qualifying Microsoft 365 plan required. Copilot Chat is included for eligible Entra users. Agents can add metered charges.

Identity and application context are the advantage. Users ask questions where they already work, and administrators operate inside an existing security boundary.

Evaluation should focus outside that boundary. If product knowledge lives in Salesforce, Jira, Zendesk, engineering databases, or acquired-company tenants, measure the connector and permission behavior rather than assuming the Microsoft estate covers the company.

Price it honestly too. That $30 sits on top of a qualifying plan, so a comparison against a Glean quote is not licence-to-licence, and the true increment depends on entitlements the company already owns.

Builder platforms cost you engineering

Google Cloud Agent Search pricing is unusually explicit. Standard search costs $1.50 per 1,000 queries. Enterprise search, including core generative answers, costs $4 per 1,000 queries. Advanced generative features add another $4 per 1,000. The service includes 10,000 queries monthly, and indexed storage is $5 per GB per month after the first 10 GB.

Treat this as infrastructure for a product team rather than a finished employee-search rollout. Control over data stores, schemas, ranking, interfaces, and application integration is the value. Owning relevance evaluation, permission mapping, user experience, and ongoing tuning is the cost.

Choose it when search is a product capability and engineering control is worth more than a turnkey portal. Published per-query rates also make forecasting unusually easy here, which is worth something during a budget review where every other vendor answers with a quote.

Elastic provides primitives for hybrid retrieval, vector search, ranking, observability, and custom interfaces. Its serverless search pricing meters ingest, search, and machine-learning compute separately. Current product guidance also matters: Elastic says the old standalone Enterprise Search, App Search, and Workplace Search packages are in maintenance mode and recommends Elasticsearch-native tools for new work.

Onyx approaches the same requirement from the opposite direction. Where Elastic gives you components to assemble, Onyx ships an open-source employee-search application with connectors, permission-aware retrieval, and cited answers already wired together, and it self-hosts.

Pick between them by asking what you want to own. Teams with search engineers and a specific relevance problem get more from Elastic. Teams that want a working internal search inside their own boundary, without building the application layer, should start with Onyx.

Both trade a licence line for an operations line. Upgrades, scaling, storage, and connector maintenance become staff time, and that cost is real even though it never appears on the quote you were comparing.

Specialists fail when bought as Glean substitutes

Perplexity Enterprise Pro lists $40 per seat monthly or $400 annually. Enterprise Max lists $325 monthly or $3,250 annually. Its product begins with answer-oriented web research and adds enterprise controls and connected sources.

Analysts, sales, strategy, and research teams moving between public and internal information fit it well. It is less obviously the company system of record for internal search than a platform built around enterprise indexing from the start.

Test citations that mix public and private evidence. Confirm whether the answer makes that boundary visible, how administrators control connectors, and what happens when internal evidence disagrees with a current public source. Buying it as a company-wide index is the predictable way to be disappointed by an otherwise strong product.

Coveo is strongest when search connects directly to customer service, commerce, websites, Salesforce, ServiceNow, or knowledge experiences. Its platform combines a unified index, machine-learned ranking, recommendations, and generated answers. Entitlements vary by platform and solution plan, so procurement should map query volume, indexed items, connectors, and user counts before comparing quotes.

Guru combines permission-aware search with knowledge verification. That second part matters when the problem is not only finding a document but deciding whether the answer is still approved. Guru advertises plans starting at $25 per seat monthly and custom enterprise terms.

Coveo fits search-led digital experiences. Guru fits teams willing to maintain a verified knowledge layer. Neither should be reduced to a generic Glean substitute, and both fail in the same way when bought as one: capable machinery aimed at users who are not the ones complaining.

SearchBlox SearchAI offers managed and self-managed deployment, hybrid retrieval, RAG, knowledge graphs, agents, and fixed-cost positioning rather than token or seat metering. Its Inception partnership report says Mercury is available as a model endpoint inside SearchAI.

That report is not an independent latency benchmark, and we have not reproduced it. It does establish the integration and the intended architecture, with SearchBlox handling indexing and retrieval while Mercury handles generated text.

Buyers considering on-premises or private-cloud deployment should test this route alongside Elastic, Onyx, and custom stacks. Fixed-cost pricing is genuinely attractive for finance teams tired of metered surprises, so the thing to verify is what the fixed cost stops covering as volume grows.

Permission leakage is the release blocker

Use 100 to 300 real questions spanning easy lookup, synthesis, stale documents, conflicting sources, acronyms, and questions with no answer. For each query, record the expected source and which test users may see it.

Score at least these outcomes:

• retrieval recall for the expected evidence,
• citation support for every generated claim,
• permission leakage, with any leak treated as a release blocker,
• freshness after edits, deletions, and access changes,
• abstention when the corpus does not support an answer,
• administrator time to diagnose a miss,
• median and tail response latency, and
• total monthly cost at expected users, queries, and storage.

One test belongs in every trial and is usually skipped: ask the product a question whose answer exists only in a document the test user cannot see. Systems that abstain have understood the permission model. Systems that answer from a neighbouring public document have not, and you have just found the failure that matters most, for free, before signing anything.

Do not let one polished demo choose the product. Phrasing an answer is not the hard part of enterprise search. Returning the right evidence to the right person after a source system changed at 4:17 p.m. is the hard part, and no demo is scheduled for 4:17 p.m.

Run the trial long enough to observe those changes. Four weeks can include group membership updates, policy edits, connector outages, source migrations, and an ordinary volume cycle. Require administrators to diagnose several planted failures without vendor guidance, then record how long each took and what evidence was available to them. That number predicts your support burden better than any feature comparison.

Price the evaluated configuration rather than the entry plan. Include implementation services, premium connectors, storage growth, query or generation overages, agent capacity, support tier, sandbox environments, retention, and the internal staff needed to operate the system. Divide the result by supported answers and completed workflows, not licensed seats alone.

Put the evidence and commercial assumptions in a durable decision memo. Successor teams should be able to see which corpus was tested, which permissions were exercised, what the quote included, where the product failed, and which future change would justify reopening the choice.

Buy the tool that survives that change.