Memora vs Glean: Which Fits Your Team? [2026]
Memora vs Glean compared: company-wide enterprise search versus decision context for engineering and product teams, with pricing, connectors and when to choose each.
The short answer
Glean and Memora both connect workplace tools, respect source-system permissions and answer questions with AI. They're aimed at different problems.
Glean is enterprise-wide search and an AI assistant. Its documentation lists 275+ connectors, an MCP server, and deployment either as Glean-hosted SaaS or in the customer's own GCP or AWS account. It suits large organizations that want one search layer over many departments and systems. Pricing isn't published; Glean asks prospects to book a demo.
Memora focuses on the work of engineering and product teams. It connects Slack, GitHub, GitLab, Jira, Linear, Confluence, Notion, Google Drive, Gmail, Microsoft Teams, Zendesk, Figma and Zoho, plus meeting transcripts from Google Meet, Zoom and Teams. It links the conversations, pull requests, tickets and documents that belong to the same decision. It also offers AI pull-request reviews and an MCP server for Claude Code and Cursor. There's a free plan, and paid plans start at $39 a month.
Where the two differ
| Glean | Memora | |
|---|---|---|
| Main audience | Every department in a large organization | Engineering and product teams |
| Connectors | 275+ listed | 13 workplace tools plus meeting transcripts |
| Permission-aware answers | Yes | Yes |
| Answers cite sources | Yes | Yes |
| Links related work into one decision trail | Not its main focus | Core feature |
| AI pull-request reviews | Not a core feature | Included in every plan |
| MCP server | Yes | Yes |
| Deployment | SaaS, or customer-hosted in your GCP/AWS account | Cloud (AWS, USA/EU); dedicated infrastructure on Enterprise |
| Pricing | Not published | Free; Starter $39/mo; Pro $99/mo; Enterprise custom |
Glean facts are from Glean's public documentation, accessed 10 October 2026: connectors, deployment, pricing.
A typical question, answered two ways
Take a question an engineer might ask: "Why does checkout retry failed payments three times?"
A broad enterprise search tool will usually find the relevant documents and summarize them. That works well when the answer is written down in one place.
Often it isn't. The reasoning might be split between a Slack incident thread, the pull request that added the retry, a Jira ticket about duplicate charges and a short note in an architecture document. Memora connects those pieces as related work, so the answer can explain the decision and link to each part of the trail. You can open any of them to check the details.
When Glean is the better choice
- You need one search tool for the whole company: sales, HR, legal and finance as well as engineering.
- You rely on systems outside Memora's integration list.
- You need to run the product in your own cloud account, or you need a vendor with third-party compliance certifications. Memora doesn't hold SOC 2 or ISO 27001 certification today.
When Memora is the better choice
- Your most expensive questions are about engineering and product decisions, not finding a file.
- You want answers that combine code, tickets, discussions and meeting notes.
- You'd like AI pull-request reviews that take past decisions into account.
- You want to start on a free plan with published pricing, without a sales process.
Can you use both?
Yes. Some teams use a company-wide search tool for general knowledge and Memora for engineering context. Memora's MCP server can also bring that context into the AI coding tools your engineers already use.
Next steps
See how source-linked answers work in the interactive demo, compare plans and limits, or read how we handle data on the security page. For a wider comparison, see our guide to Glean alternatives.
Common questions.
How does Memora differ from Glean?
Does Memora require manual tagging like traditional search tools?
Can Memora connect to existing workplace tools?
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See how Memora works with your own tools.
Connect Slack, GitHub, Jira or your docs and ask a question about your own work.