Glean competitors: what to evaluate when enterprise search isn't the whole job

TL;DR

• Glean is strong enterprise AI search that has grown into agents and an assistant layer – but evaluating Glean competitors means asking whether your team needs an AI that finds information or one that finds, decides, and acts across every system it touches.
• The real split among enterprise AI platforms is architectural origin: knowledge-graph-first (Glean, Onyx, Coveo) versus memory-and-action-first (Computer, by DevRev). That origin shapes what the agent can do after it retrieves the answer.
• Six dimensions separate the two categories: memory persistence, action governance, cross-system reach, retrieval grounding, observability depth, and agent lifecycle management.
• If your work ends when the answer appears, a knowledge-first platform fits. If the answer is the starting point for a decision, an escalation, or a workflow that spans tools – evaluate on what happens after retrieval.

Two ways to build enterprise AI – and they produce different agents

Most "Glean competitors" listicles rank 10 tools on a feature matrix and call it a comparison. That's useful if your decision is "which enterprise search engine?" It's less useful if your question is broader: "which enterprise AI platform actually runs our work?"

The enterprise AI platform market – projected at $10.9 billion in 2026 according to Grand View Research – contains two architecturally distinct categories. They look similar in a demo and diverge fast in production.

Category 1: Knowledge-graph-first platforms. These start with enterprise search – indexing documents, messages, wikis, and tickets into a knowledge graph or retrieval layer – and expand outward into an assistant and, more recently, agents. Glean is the defining example. Others in this lane include Onyx (formerly Danswer), Coveo, GoSearch, and Atolio. The core strength is retrieval: finding what already exists across your connected apps.

Category 2: Memory-and-action-first platforms. These start with a shared memory layer across business systems and build answers, skills, and agent actions on top of it. Computer, by DevRev, is built this way. The core strength is that the agent doesn't just retrieve – it remembers context across sessions and teams, takes governed actions, and maintains an auditable trail of what it did and why.

The distinction matters because where a platform starts determines what it's best at when your requirements get harder. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027 – and the ones that survive will be the agents whose architecture handles the hard parts, not just the search.

What Glean does well

Credit where it's due. Glean has earned its position – and understanding its genuine strengths is part of evaluating whether a competitor needs to replace them or complement them.

• Broad connector coverage. Glean connects to 275+ apps out of the box – Slack, Google Workspace, Confluence, Jira, Salesforce, and more. For teams drowning in scattered information, this breadth is real value on day one.
• Enterprise search quality. Glean's core product, enterprise search, uses a knowledge graph that understands organizational context – who works on what, which docs are authoritative, what's stale. The search experience is genuinely good.
• AI assistant. Glean's AI assistant can summarize documents, draft content, and answer questions grounded in your company's data.
• Agent builder and orchestration. As of 2026, Glean has expanded into agents with a no-code agent builder and agent orchestration that connects and coordinates agents across workflows.
• Security posture. Glean's security page emphasizes data protection and enterprise-grade access controls.

These aren't minor features. If your primary problem is "our team can't find what they need across 50 tools," Glean solves it well.

Where the architecture diverges: six dimensions that matter after retrieval

The question becomes: what happens after the agent finds the answer? This is where the two categories separate, and it's the comparison most listicles skip.

Read this table vertically, not horizontally. If five of the six dimensions on the right matter to your use case, Glean's strengths in the left column – however real – won't be enough. If three of them don't matter and your primary need is finding information fast, Glean may be exactly right.

The architectural question behind "Glean competitors"

When enterprise teams search for Glean competitors, they're usually asking one of three things – and each leads to a different answer:

"We need better search." If Glean's search isn't working for you, evaluate the knowledge-graph-first Glean alternatives: Onyx (open-source, self-hosted), Coveo (e-commerce and support search), GoSearch (lightweight, fast setup), Atolio (developer-focused). These compete on Glean's home turf: retrieval quality, connector breadth, and ease of deployment.

"We need search plus action." If your team finds the right information but still spends hours acting on it – routing tickets, updating records, building reports, escalating issues across tools – you're looking for a platform where the agent's job doesn't end at the answer. Computer is built for this: the shared memory means the agent already knows the customer's history across systems, and Safe Actions mean it can actually do something about it – with governance, not guesswork.

"We're evaluating the whole category." If you're deciding where to place your enterprise AI investment for the next two years, evaluate on the six dimensions above. The market is splitting along these two architectural lines, and picking the wrong category – not the wrong vendor – is the expensive mistake.

What production looks like on each architecture

A comparison based on features reads clean. A comparison based on what happens at scale reads differently.

Consider the kinds of queries a support operation handles at volume: a customer calls about a billing issue, then calls back two days later about a related account change, then submits a ticket through a different channel about the same problem. Each interaction touches a different system. The agent needs to connect those threads, not just search for each answer independently.

That's the architectural split in practice. A knowledge-graph-first platform excels at retrieving the right document for each query in isolation. A memory-and-action-first platform connects the queries to each other because the shared memory already holds the context – then takes governed actions across the systems involved. The fintech BILL landed on the action-and-memory side of this split: after evaluating more than 15 AI providers, its team chose Computer and scaled to 70% automatic resolution across connected systems – an outcome that lives on the "act across systems," not "search better," side of the line.

For the full platform comparison across 10 enterprise AI agent tools, including where each fits by team and use case, see the AI agent tools guide.

Decision checklist: which architecture fits your team

Before you evaluate any vendor, answer five questions. They'll tell you which category to shop in.

1. Does your team's work end when they find the answer? If yes, knowledge-graph-first. If the answer triggers actions across systems, memory-and-action-first.
2. Do your agents need to remember context across sessions? If a support agent should know that this customer called twice last week, you need persistent memory – not just retrieval.
3. Can you ship agents without audit trails? If your compliance, security, or legal team would block an agent that can't prove what it decided and why, you need governed actions with traces.
4. How many systems does a single workflow touch? If the answer is one, connectors for indexing are enough. If it's three or more, you need write-back and cross-system coordination.
5. Is "it worked in the demo" sufficient proof? If you need staging, evaluation at scale, canary deployments, and the ability to roll back a bad agent version in production – evaluate on the full agent lifecycle, not just the builder. Our guide to enterprise AI agent deployment patterns walks through what that lifecycle looks like in practice.

If your evaluation is really about what happens after the answer, see how Computer builds, governs, and acts across systems in Agent Studio – or bring a multi-system workflow to a walkthrough and watch it run end to end.

Frequently asked questions

Is Glean just an enterprise search tool?

Not anymore. As of 2026, Glean positions itself as "Enterprise AI that Works" with three pillars: search, an AI assistant, and AI agents with a no-code builder and orchestration layer. It started as enterprise search and has expanded significantly – but the knowledge-graph foundation that made its search strong still shapes how its agents work.

What makes Computer, by DevRev, different from Glean?

The difference is architectural origin. Glean built outward from enterprise search – indexing documents into a knowledge graph, then adding an assistant and agents. Computer built outward from shared memory and action governance – a persistent context layer across systems, with answers and actions grounded in it. The result: Computer's agents remember across sessions, take auditable actions, and maintain the full lifecycle from build through production rollback.

Can Glean's agents take actions, not just answer questions?

Yes. Glean's public pages describe agents that automate tasks, plan, and connect across workflows, with permissions-aware governance and real-time oversight, plus deployment controls to certify agents. What differs architecturally is the granularity: Computer's Safe Actions govern each action individually – permission-scoped, reversible, and traced back to the decision behind it – so the audit trail is per-action, not just platform-level oversight. Both take actions; they govern them at different levels.

Should I replace Glean or complement it?

It depends on the gap. If your problem is "we can't find information across our tools," Glean's search may be exactly right, and the question is whether you need an additional platform for the action and governance layer. If your problem is "we find the answer but can't act on it at scale with audit trails," evaluate a memory-and-action-first platform like Computer as the primary – not a complement.

What other Glean competitors should I evaluate?

In the knowledge-graph-first category: Onyx (open-source, self-hosted RAG), Coveo (commerce and support search), GoSearch, Atolio, Dust, Cassidy AI, and Kore.ai – our roundup of Glean alternatives covers the search-focused options in depth. In the memory-and-action-first category: Computer, by DevRev. The two categories serve different needs – compare within the right category first, then decide which category fits.