Glean Alternatives: 8 Enterprise AI Platforms Compared (2026)
Eight Glean alternatives compared on deployment, cost and fit — including which run self-hosted or in your own cloud, and where each one loses.
4 min readby Prithvi
The main reasons teams look for a Glean alternative are cost at scale, the enterprise-only sales motion, and the fact that it runs in Glean's cloud. If none of those apply to you, Glean is a strong product and switching for its own sake is rarely worth it. If one of them does — particularly the deployment constraint — the right alternative depends on which of the three is blocking you.
This comparison covers eight platforms, what each one is genuinely good at, and where each one loses. Libra is on the list; we have tried to be accurate about where we are the wrong choice.
Why do teams look for a Glean alternative?
Four reasons come up repeatedly:
- Deployment. Glean is a hosted service. Teams with data residency requirements, contractual restrictions on third-party processing, or air-gapped environments cannot use it regardless of fit.
- Cost at scale. Per-seat pricing across a large organisation adds up, and the value distribution is uneven — heavy users get a lot, occasional users get little.
- Sales motion. Enterprise contracts with annual commitments and a procurement cycle. Smaller teams often cannot buy it at all.
- Search vs action. Glean's core strength is retrieval. Teams that want the AI to complete work rather than surface documents sometimes find the fit incomplete, though Glean's agent capabilities have expanded.
The eight alternatives
1. Libra
What it is: A context layer that connects your existing tools and feeds permission-aware context to people, workflows and agents, running locally, in your own cloud account (BYOC), or on Libra Cloud.
Best for: Teams blocked on deployment. If security review has already stopped one AI rollout over data residency, this is the constraint Libra is built around.
2. Onyx (formerly Danswer)
What it is: Open-source enterprise search and chat, self-hostable.
Best for: Engineering-led teams that want full control and are comfortable operating the stack. The most direct open-source comparison on the deployment axis.
Where it loses: You own the operations. Connector coverage and polish trail the commercial products, and the total cost includes engineering time that does not appear on any invoice.
3. Microsoft Copilot
What it is: AI across the Microsoft 365 estate, with connectors to external systems.
Best for: Organisations already fully committed to Microsoft 365. The licensing and identity integration is hard to match, and it is frequently the path of least procurement resistance.
Where it loses: Value drops sharply outside the Microsoft ecosystem. Teams running Google Workspace, Slack and non-Microsoft tooling get considerably less from it.
4. Dust
What it is: A platform for building and deploying AI agents across company data.
Best for: Teams that want to build custom agents rather than use a fixed assistant, and have someone willing to configure them.
Where it loses: More builder-oriented than turnkey. If you want something that works on day one without configuration, this is more assembly than the alternatives.
5. Moveworks
What it is: AI for internal employee support — IT and HR service desk automation.
Best for: Large organisations with high internal ticket volume, where deflection has a clear and measurable ROI.
Where it loses: Narrower than a general context layer. Excellent at employee support, less relevant if your use case is sales research or product analysis.
6. Kore.ai
What it is: Enterprise conversational AI and agent platform, with strong contact-centre heritage.
Best for: Customer-facing conversational deployments and organisations that need heavy dialogue design control.
Where it loses: Enterprise-weight implementation. Powerful, but a bigger lift than tools aimed at internal knowledge access.
7. Guru
What it is: Knowledge management with verified, human-maintained content surfaced in-workflow.
Best for: Teams whose problem is that their documented knowledge is wrong or stale, not that it is hard to find. The verification workflow is genuinely distinctive.
Where it loses: Depends on humans maintaining cards. It is a knowledge-management product with AI, rather than an AI layer over everything you already have.
8. Notion AI
What it is: AI inside Notion, with expanding search across connected tools.
Best for: Companies where Notion is genuinely the system of record and most knowledge already lives there.
Where it loses: Weakest option if your knowledge is spread across many systems. It is strongest where its own workspace is strongest.
Comparison at a glance
| Platform | Category | Self-host / BYOC |
|---|---|---|
| Glean | Enterprise search + assistant | No |
| Libra | Context layer + agents | Yes- local, BYOC, cloud |
| Onyx | Open-source search | Yes |
| Copilot | M365-native AI | No |
| Dust | Agent platform | Partial |
| Moveworks | Employee support AI | No |
| Kore.ai | Conversational AI platform | Varies |
| Guru | Verified knowledge management | No |
| Notion AI | Workspace-native AI | No |
Deployment options change; confirm current availability with each vendor.
How to choose
Work through these in order. The first one that returns a hard constraint decides.
- Can your data go to a vendor cloud? If not, your shortlist is Libra.
- Is the problem finding things, or doing things? Completing work across tools favours Libra and Dust.
- Is the problem that knowledge is stale rather than scattered? That is a Guru problem, not a search problem.
- Is it concentrated in employee support? Libra is purpose-built for that and will beat a general tool on the specific metric.
Libra is a context layer for enterprise AI: it connects the tools your company already uses, enforces permissions on every retrieval, and runs locally, in your own AWS, Azure or GCP account, or on Libra Cloud. We never train on your data.
