Enterprise search that goes beyond: Glean alternatives to look out for in 2026

*Updated September 2026*

Every Glean alternatives comparison you’ve read follows the same script. Connectors counted. UI screenshots compared. Deployment timelines estimated. None of them ask the question that actually decides your long-term ROI. Does the tool retrieve documents, or does it understand relationships and act on them?

It’s an architectural fork in enterprise AI search, and it shapes everything downstream. Retrieval-first tools like Glean index your content and surface the closest match. Memory-first platforms maintain a live, permission-aware knowledge graph – and use it to ground answers, trigger workflows, and resolve issues without human escalation.

This guide compares six of the best Glean alternatives across that architectural divide. You’ll see where Glean excels, where it falls short, and why the retrieval-vs-memory distinction matters more than any feature checklist. The Enterprise-Bench results make the gap measurable – and the cost implications are hard to ignore.

TL;DR – five things to know before you switch

• Architecture matters more than features. Retrieval-first tools surface documents. Memory-first platforms like Computer, by DevRev ground answers in entity relationships – customers, tickets, products, decisions – and act on them.
• The accuracy gap is benchmarked. On Enterprise-Bench, the memory-first approach hit 94.3% accuracy versus 63.6% for retrieval-only. Same foundation model. 4.4x fewer tokens per correct answer.
• Architecture bends the cost curve. The retrieval-vs-memory choice sets your long-run economics: on Enterprise-Bench, a 256x jump in data pushed retrieval-only token consumption up 29% while Computer’s held roughly flat. That divergence compounds every quarter you add data.
• Glean is strong enterprise search software. The comparison isn’t “Glean is bad.” It’s whether your needs have grown past document retrieval into resolution and permission-aware grounding.
• Six Glean alternatives compared here: Computer, by DevRev, Microsoft Copilot, Notion AI, Gemini Enterprise, Moveworks, and Onyx. Evaluated on architecture, accuracy, actions, and total cost of ownership.

What is an enterprise search tool?

An enterprise search tool is software that indexes and retrieves information across an organization’s applications, documents, and databases. It’s the workplace search layer that lets your team find answers without knowing which system holds them.

The category has evolved fast. Early enterprise search software matched keywords. Modern AI enterprise search platforms use semantic understanding to surface relevant results even when the query doesn’t match the exact phrasing. But the latest shift goes further – from enterprise search beyond keyword matching toward systems that maintain persistent context.

That shift matters because search alone doesn’t close the loop. Finding a relevant document is a starting point. Acting on it – updating a ticket, triggering a workflow, resolving a customer issue – requires something traditional search tools weren’t built for. This is where the distinction between retrieval-first and memory-first architecture becomes practical.

For teams managing knowledge at enterprise scale, the question isn’t whether you need search. It’s whether search is enough. As every app needs GenAI search, the bar for what “enterprise AI search” means keeps rising. Enterprise knowledge management software now needs to ground answers in relationships, not just retrieve documents.

The buying decision has moved: enterprise search is table stakes. What matters now is what happens after the result appears – and whether your tool can act on it.

Why look for Glean alternatives

Glean built a strong enterprise search product with a broad connector ecosystem. But as organizations push toward AI-driven resolution – not just AI-assisted search – specific architectural limitations surface. Here are six reasons teams evaluate Glean alternatives.

Glean’s agents retrieve but can’t resolve

Glean’s agents excel at finding relevant documents and summarizing them. What they can’t do is act on what they find. If a customer reports a billing discrepancy, a retrieval-first agent surfaces the invoice.

A resolution-first agent surfaces the invoice, cross-references the subscription record, and initiates the correction. That gap between “here’s the document” and “here’s the fix” drives the Glean vs Computer evaluation for many teams.

Limited agentic capabilities beyond search

Retrieval-first tools typically extend into action through MCP (Model Context Protocol) integrations – bolt-on connections that let the assistant trigger actions in external tools. As a category, that pattern tends to add latency, another integration layer, and another failure surface. Understanding how knowledge graphs replace MCP interfaces clarifies why native action capabilities reduce both complexity and cost.

Cross-system intelligence that stays shallow

Glean indexes documents from your connected systems. It doesn’t model the relationships between them. A support ticket, the customer who filed it, the product version they’re running, the engineering decision behind the bug – Glean sees these as separate documents. A memory-first architecture sees them as connected entities. That changes the quality of every answer an enterprise AI assistant delivers.

Time to value stretches when search is read-only

Deploying Glean for document search can be fast. Deploying it for autonomous resolution takes longer – because resolution requires action capabilities that Glean’s architecture doesn’t natively support. Teams end up layering additional tools (workflow automation, ticketing integrations, custom API bridges) to close the action gap, extending the timeline.

Permission gaps at the field level

Document-level permissions, inherited from the source systems, are the norm for retrieval-first tools. But enterprise resolution often requires field-level access control. The agent should see the customer’s plan tier but not their payment method. It should access the ticket’s priority but not the internal escalation notes. When permissions apply after retrieval rather than before the model processes data, the risk surface grows.

Separate licenses for search, assistant, and actions

Retrieval-first suites often separate search, the assistant, and agentic capabilities into distinct tiers. Most enterprise buyers need all three. That stacked licensing creates a total cost of ownership that isn’t visible in the initial search-only evaluation. Compare this to Glean competitors that bundle search, analytics, and action in a single license.

Strategic takeaway: None of these limitations make Glean a bad product. They’re architectural boundaries. If your team’s needs have grown past document retrieval into cross-system resolution, the architecture – not the feature list – is what to evaluate.

Feature comparison: Glean alternatives at a glance (2026)

Read the table this way: the first two columns look similar across tools. The last four reveal the architectural divergence. Pay attention to how each tool grounds its answers and whether it can act on them.

Beyond feature checklists: retrieval vs memory-first architecture

Most Glean alternatives comparisons stop at features – connectors, UI, deployment speed. The question that actually determines whether accuracy holds and costs stay flat is architectural. Does the tool retrieve documents, or does it understand relationships?

This architectural difference is measurable – and, crucially, it decides how your economics behave as data piles up. According to Enterprise-Bench results, holding the foundation model constant, memory-first grounding answered enterprise tasks correctly 94.3% of the time to retrieval-only’s 63.6%, at 4.4x fewer tokens per correct answer.

Then the telling part: grow the evaluation dataset 256x and Computer’s token consumption barely moves, while the retrieval baseline climbs 29%. Retrieval gets more expensive the more you feed it; memory does not.

In short: retrieval finds a passage. Memory understands how that passage connects to the customer, the open ticket, and the product decision – then acts on it. Understanding knowledge graphs vs RAG for enterprise AI gives you the technical depth behind this comparison.

Why it matters: the retrieval-vs-memory distinction is a practical one. It determines whether accuracy degrades or holds as your data scales – and whether your cost curve bends in your favor or against it.

Glean vs Computer: what’s the real difference?

The short version: Glean is enterprise search, and Computer is enterprise resolution. Glean indexes your content and returns the closest-matching documents, fast, across a broad connector ecosystem.

Computer maintains a live, permission-enforced knowledge graph – Computer Memory – and uses it to ground answers in entity relationships and then act on them: updating tickets, triggering workflows, and closing the loop.

Ask Glean a question and you get the right document. Ask Computer and, where it can, you get the resolved outcome. That is the difference behind the “glean vs devrev” comparison buyers keep running.

Top Glean alternatives compared

Computer, by DevRev

Computer, by DevRev takes a different approach to enterprise search. Instead of indexing documents and retrieving the closest match, Computer maintains a persistent knowledge graph – Computer Memory. It models live relationships between customers, tickets, products, and team decisions.

Think of it less as smarter search and more as a self-wiring AI memory layer that grounds every answer in the actual state of your business. When a customer asks about a delayed shipment, Computer doesn’t surface a FAQ. It cross-references the order, the logistics status, the engineering ticket blocking fulfillment, and the product team’s resolution timeline – then acts.

The practical result: Computer resolves 70% of queries at BILL across 200,000 customer interactions. Not routed. Not deflected. Resolved. AI agents built for enterprise workflows handle the resolution autonomously, escalating only when the situation genuinely requires a human.

According to Enterprise-Bench, Computer hit 94.3% accuracy versus 63.6% for retrieval-only approaches on the same model. That gap compounds at scale – and Computer’s token costs stay flat as data grows.

Best for: Organizations evaluating Glean alternatives that need autonomous resolution across support, product, and engineering.

Microsoft Copilot

Microsoft Copilot brings AI directly into the M365 suite – Word, Excel, Teams, Outlook, and SharePoint. For teams already embedded in the Microsoft stack, the integration depth is unmatched. Copilot draws from the Microsoft Graph to summarize meetings, draft documents, and answer questions grounded in your M365 data.

The limitation is scope. Copilot’s grounding is deep within Microsoft’s ecosystem but shallow outside it. Cross-platform intelligence – connecting Salesforce data to Jira tickets to Slack conversations – isn’t what Copilot was built for. And its action model stays within M365 boundaries.

For a detailed comparison of enterprise AI assistants beyond the Microsoft stack, see Microsoft Copilot alternatives for enterprise AI.

Best for: M365-native teams that need AI-assisted productivity within Microsoft’s ecosystem.

Notion AI

Notion AI extends Notion’s workspace with AI-powered search, writing assistance, and Q&A across your Notion pages, databases, and wikis. It’s fast, intuitive, and works within the tool your team already uses.

The constraint is that Notion AI’s knowledge boundary is Notion itself. It can’t pull from your CRM, ticketing system, code repository, or communication tools. For teams that use Notion as their single knowledge base, it’s useful. For enterprise environments with dozens of interconnected systems, the single-tool scope becomes a ceiling.

Best for: Small-to-mid teams using Notion as their primary knowledge workspace.

Gemini Enterprise

Gemini Enterprise brings Google’s multimodal AI into Gmail, Docs, Sheets, Drive, and Meet. It can also ground responses in Google Search results – an uncommon capability among enterprise AI assistants. For Google Workspace-first organizations, the native integration and web grounding add genuine value.

Like Copilot, the limitation is ecosystem boundaries. Gemini’s enterprise capabilities stay within Google’s suite. Cross-platform action capabilities are minimal. And for teams that need enterprise-grade permission controls at the field level, the Google Workspace permission model may not be granular enough.

Best for: Google Workspace organizations that want AI assistance grounded in both their own data and the web.

Moveworks

Moveworks – acquired by ServiceNow in December 2025 – built its reputation on IT service automation. It resolves IT tickets, provisions access, answers HR questions, and handles common employee requests across ITSM platforms. Its AI is trained specifically for IT workflows, and it’s good at them.

The scope is the trade-off. Moveworks focuses on IT and employee experience. It isn’t built for customer-facing resolution, product intelligence, or cross-functional workflows spanning support, engineering, and product. If your evaluation is IT-centric, Moveworks belongs on the shortlist. If it’s broader, the specialization becomes a constraint.

Best for: IT teams with high ticket volumes and ITSM-focused automation needs.

Onyx (open source)

Onyx – formerly Danswer – is an open-source Glean alternative that connects to common data sources: Slack, Confluence, Google Drive, and GitHub. It runs on your own infrastructure. For teams that need full control over their search stack, it’s a strong starting point.

The trade-offs are production-grade features. Onyx lacks native enterprise permissions, a built-in action model, and the persistent knowledge graph architecture that grounds answers in entity relationships. It’s a capable search tool – but search is where it stops.

Best for: Technical teams that want self-hosted, customizable enterprise search with full infrastructure control.

Why choose Computer over Glean?

Glean is a capable enterprise search product. The comparison isn’t about quality gaps in what Glean does – it’s about architectural differences in what the two platforms are designed to do. Here are five reasons teams choose Computer.

Computer Memory grounds answers in relationships, not documents

Glean indexes documents and retrieves the nearest match. Computer maintains a persistent knowledge graph – Computer Memory – that models the live relationships between customers, support tickets, product features, engineering decisions, and business outcomes. Every answer is grounded in these entity relationships, not isolated document fragments.

Permissions are enforced at the memory layer

Computer enforces permissions before the model processes data – at the memory layer, not after retrieval. The AI agent never sees information the person asking isn’t authorized to access. For regulated industries and enterprises with complex data governance requirements, that ordering is a prerequisite, not a nice-to-have feature.

Outcomes, not just answers

Glean finds answers. Computer resolves issues. The distinction is native CRUD access – Computer’s agents don’t just read data. They update tickets, trigger workflows, sync records across systems, and close the loop. Resolution is the default, not a bolt-on.

#### Measured on Enterprise-Bench

On identical foundation models and data, Computer’s memory-first architecture delivered 94.3% accuracy to a retrieval-only approach’s 63.6%, at 4.4 times fewer tokens per correct answer. The cost story is the one to sit with, though: scale the evaluation dataset 256x and Computer’s token usage holds roughly flat where the retrieval baseline rises 29%.

For enterprise teams pricing total cost of ownership, that is the whole point. Retrieval-first tools get costlier as your data grows; a memory-first architecture bends the cost curve in your favor instead.

One platform, not three licenses

Search, the assistant, and agentic capabilities often sit in separate pricing tiers across retrieval-first suites. Computer bundles search, analytics, agent actions, and workflow automation into a single platform license. For buyers who need the full stack – and enterprise evaluations almost always land there – the total cost difference is meaningful.

Accuracy holds as data grows

Retrieval-first architectures face a structural challenge at scale: more data creates more noise to filter. Computer’s knowledge graph structure narrows the working set as data grows – relationships constrain what’s relevant, so accuracy holds rather than degrades. The 256x scaling result on Enterprise-Bench isn’t a lab curiosity. It’s a preview of what happens to your accuracy and cost curve over the next two years.

Strategic takeaway: The choice between Glean and Computer isn’t about features. It’s about whether your enterprise search tool stops at finding answers – or continues through to resolving them at a cost that improves with scale.

How Uniphore uses Computer for enterprise resolution

Uniphore, the conversational AI company, deployed Computer to unify customer intelligence across support, product, and engineering. Before Computer, their teams operated in disconnected systems – support tickets in one tool, product feedback in another, engineering decisions in a third.

With Computer Memory connecting these systems into a single knowledge graph, Uniphore’s support agents see the full context of every customer interaction. Related engineering work, product decisions, and account history surface without switching tools. Resolution shifted from “find the right document and escalate” to “resolve in context.”

This isn’t a search improvement. It’s an operational shift – from using enterprise search to look things up, to using enterprise memory to close the loop. For Uniphore, that shift turned search from a productivity tool into a resolution engine.

Final verdict: how to choose the right Glean alternative

Choosing among Glean alternatives depends on where your needs sit today – and where they’ll be in 18 months.

Stay with Glean if your primary need is broad document search across a large connector ecosystem. If action requirements are limited and you’re comfortable with separate licensing tiers, Glean remains a solid choice.

Evaluate Computer if your team needs autonomous resolution – not just search – across support, product, and engineering. If you need AI that respects field-level access before it acts on data, and a cost curve that improves with scale, the architectural fit is Computer.

Among the remaining Glean alternatives, consider Microsoft Copilot if your stack is M365-native and cross-platform needs are minimal.

Choose Notion AI if Notion is your primary workspace and scope is contained.

Look at Gemini if you’re Google Workspace-first.

Evaluate Moveworks for IT-specific automation.

Try Onyx if you want self-hosted, open-source search with full infrastructure control.

The deeper question for anyone evaluating Glean alternatives isn’t which tool has the best feature list. It’s whether your enterprise search tool should retrieve documents or understand relationships and act on them. That architectural decision determines accuracy, cost, and resolution outcomes for years.

Explore how Computer compares to Glean to see how the memory-first approach works in practice.