Glean Alternatives in 2026:
An Honest Look for Growing Teams
Most "Glean alternatives" articles exist to rank. They list eight tools, assign star ratings, and call it research. This one is different — because the teams searching for Glean alternatives are often solving a fundamentally different problem than the one Glean was built for.
How the main options stack up
Before the analysis — here's how the tools compare on what actually matters. If you're short on time, focus on the "File-level AI permissions" row. That's where the real split is.
| Glean | Guru | Notion AI | Copilot 365 | Provenyx | |
|---|---|---|---|---|---|
| Primary use case | Enterprise search | Knowledge base | AI in Notion | AI in M365 | AI access control |
| File-level AI permissions | ✗ | ✗ | ✗ | Partial | ✓ |
| Works with existing storage | Connects to it | Connects to it | Notion only | OneDrive only | BYOS |
| Claude, ChatGPT, Gemini | ✗ | ✗ | ✗ | ✗ | ✓ |
| Full AI audit log | ✓ | ✗ | ✗ | Partial | ✓ |
| Setup complexity | High — IT-led | Medium | Low | High — IT-led | Low |
| Realistic for SMB | ✗ | ✓ | ✓ | ✗ | ✓ |
| Pricing | $100k+/year | ~$10/user/mo | Notion plan | ~$30/user/mo | Free early access |
What Glean actually does
Glean is a workplace search platform. You connect it to Slack, Google Drive, Jira, Confluence, Salesforce, and 100+ other apps, and it builds a unified search index across all of them. On top of that index, it runs an AI layer that can answer questions in natural language — "what did we decide about the Q3 pricing change?" pulls from Slack threads, Confluence pages, and Jira tickets simultaneously.
That is genuinely impressive engineering. For a 2,000-person company where institutional knowledge is scattered across a dozen systems, it solves a real problem.
What Glean does not do — and this is where the "alternatives" framing usually breaks down — is control which AI models your team uses, or govern what those models can access at the file level. Glean is its own AI system. If you want your team using Claude for drafting, ChatGPT for analysis, or Gemini for summarization, Glean isn't in that conversation. It's a parallel track.
It also requires meaningful IT involvement to deploy, a procurement cycle that typically takes months, and a contract size that puts it out of reach for most companies under ~300 employees. Those aren't criticisms — they're design choices that reflect Glean's actual target customer.
Why teams actually search for Glean alternatives
After looking at the search patterns behind this query, there are roughly three distinct situations driving it — and they lead to very different tools.
The most common. A Director of Engineering or Head of Ops gets shown a Glean demo, it looks compelling, the quote comes back at $80–120k/year, and suddenly they're looking for something that solves a similar problem at a human price point.
This is a different problem entirely. The team isn't looking for enterprise search — they've already figured out that AI is useful. The question is: how do we let the marketing team use Claude on client briefs without feeding it confidential financial data? How do we give the sales team AI access to product docs without exposing the unpublished roadmap? This pattern is particularly common in SaaS teams and agencies. Glean doesn't address this. Neither do most tools in standard "alternatives" lists.
20 minutes — we'll show you how the control layer works.
Some teams want a structured home for company knowledge — playbooks, onboarding docs, SOPs — with AI built in. That's closer to Guru or Notion AI territory.
Most "Glean alternative" articles treat all three situations identically. They don't. The right tool depends entirely on which problem you're actually in.
across disconnected apps
without moving your data
One by one, honestly
Guru
Guru is a wiki-style knowledge management platform. You publish verified articles, tag them by team or topic, and Guru's AI can surface answers from that structured content. It integrates with Slack and Chrome so knowledge appears in context.
What makes Guru genuinely good is the verification workflow. Someone owns each article. It expires and gets flagged for review. For customer-facing teams — support, sales, success — where outdated information causes real damage, that structure is valuable.
What Guru doesn't do is work with your existing Google Drive or SharePoint files. You're building a parallel knowledge base — a meaningful content migration burden. Your "source of truth" is now split between Guru and wherever your team actually creates work.
There's also no concept of file-level AI permissions. If you need to say "the AI can see client briefs in the /active-clients folder but not the /contracts folder," that's not a problem Guru is designed to solve.
Notion AI
Notion AI is embedded directly in Notion's editor. You can ask it to summarize a doc, draft a section, translate something, or search across your workspace. It's genuinely useful if Notion is already your primary workspace.
The ceiling becomes apparent quickly when you're not an all-Notion shop — which most growing companies aren't. If your engineering team lives in Linear and GitHub, your sales team in Salesforce, and your design work in Figma, Notion AI only knows what's in Notion.
More importantly: Notion AI has no separate permissions model for AI queries. Any member with access to a page can have the AI query that page. No audit log of what the AI was asked. No way to choose which underlying model processes your content — it's Notion's model, not Claude or GPT-4.
Microsoft Copilot 365
Microsoft's AI play is comprehensive in a way that no startup can match. Copilot is embedded in Word, Excel, Teams, Outlook, and across the Microsoft Graph. If your company runs on Microsoft and you have the budget and IT resources to implement it properly, Copilot delivers genuine value.
The "if" is load-bearing. Microsoft 365 Copilot starts at roughly $30/user/month on top of existing M365 licenses. For a 50-person company, that's an $18k/year decision requiring IT governance, permission audits in SharePoint, and ongoing administration. Implementation projects for 50–200 person companies typically take two to four months.
The permissions model is Microsoft-specific. Copilot respects SharePoint and OneDrive permissions — which is good — but doesn't extend to non-Microsoft storage, and doesn't give you model flexibility. You're using Microsoft's models, not choosing between Claude, GPT-4, and Gemini.
Provenyx
We built Provenyx, so take this section with appropriate skepticism. I'll try to be as honest about our limitations as I was about the others.
The problem we're solving is specific. Your team already uses AI. Someone on the marketing team pastes client strategy docs into ChatGPT. The sales team feeds competitor analysis into Claude. The engineering team asks Gemini to review code. This is happening whether you've sanctioned it or not — and when it happens without a control layer, confidential data leaves your systems with no record of what was shared, with whom, or what the AI returned.
Provenyx sits between your team's AI tools and your file storage. You define which folders map to which projects or clients. You set file-level rules: the AI assistant for the account team can see active client folders but not the contracts or financial reports three directories up. Every query is logged — what was asked, which files the AI accessed, what it returned. Your data stays in your storage. We don't move it, copy it, or index it.
SharePoint · S3
query routing
ChatGPT · Gemini
What we don't do today: we're not a search engine across 30 connected apps. We're not replacing Glean for a 1,000-person company with enterprise search needs. We don't have SSO/SAML yet.
Which problem are you actually solving?
Before choosing any tool, it's worth being precise about what's broken.
The first teams shape it.
Provenyx is in active development. We're not a finished enterprise product — we're a team that has built and run Pics.io, a digital asset management platform used by thousands of creative teams, and we're applying that experience to a problem we kept seeing: companies have file storage, they have AI tools, and nothing governs the connection between them.
- Free access during the build period — no card required
- Direct input into the roadmap — talking to a small number of teams
- Priority onboarding when paid tiers launch
- Locked-in pricing when we go commercial
Getting the question right
Glean is a well-built product that solves enterprise search for large organizations. If that's your problem and you have the budget, it's worth a serious evaluation.
For the majority of growing teams searching for Glean alternatives, the real question is more specific: are you trying to find information better, or are you trying to govern how AI accesses information you already have?
Those are different problems. The tools that solve them are different. Getting this distinction right at the evaluation stage saves you from a six-month implementation that doesn't fix the actual pain.
