Glean vs Notion AI vs Mem: enterprise knowledge search compared in 2026

Three companies, three answers to the same question: where did that decision get made, and what was the context?

Glean indexes your entire SaaS stack and returns LLM-generated answers grounded in source documents. Notion AI lives inside your Notion workspace and now reaches across connected tools like Slack and Drive. Mem started as an AI-native notes app for individuals and has mostly stayed there while the others moved upmarket.

If you’re evaluating one against the others in 2026, the choice is rarely about model quality. They all sit on top of frontier LLMs now. The decision comes down to what they index, who owns the data layer, and how much setup you tolerate before answers appear.

Three different bets on where knowledge lives

Glean assumes your company’s knowledge is scattered across dozens of SaaS tools and that no single platform will ever consolidate it. Its product is the connector layer plus a unified permissions model that respects each source app’s ACLs. You ask “what’s the status of the Q3 retention experiment?” and Glean searches Slack threads, Notion docs, Linear tickets, Salesforce accounts, and email - returning a generated answer with citations. The enterprise focus is real: their target customers are 1,000-plus employee companies with serious IT teams.

Notion AI assumes your knowledge already lives (or should live) in Notion. Until 2024 that was a hard sell against Glean for any company that also used Slack, Drive, and Jira. The 2024-2025 connector rollout - Slack, Google Drive, Microsoft Teams, GitHub, Linear - closed most of that gap. Notion AI now answers questions that span Notion plus those connected sources, with the caveat that Notion remains the primary home and external sources are read-only inputs.

Mem stayed in the personal knowledge space. It’s still a smart notes app with semantic search and AI summarization across your own captures, not your company’s Confluence. If you’re a founder, researcher, or solo operator who wants AI on top of your personal notebook, that’s the use case. If you’re trying to find what your colleague decided in last Tuesday’s incident review, Mem isn’t the tool.

How they handle the connector problem

This is where evaluations usually get stuck. The demo always works. The real question is how the tool behaves on month three when your Confluence has 40,000 pages, half of them stale, and your Slack has 80 channels with sensitive content.

Glean’s entire reason to exist is this problem. Permissions are inherited from the source system in real time, so a query never returns a doc the asking user couldn’t open natively. Connectors run on a schedule with delta sync, and the index supports incremental updates. The trade-off is setup time - expect weeks of IT involvement for the initial deployment and a quarterly review of which connectors are live and healthy.

Notion AI’s connector approach is lighter and faster to enable. An admin authorizes the integration, picks the scope, and queries can include those sources within a day. Permission inheritance works but is less granular than Glean’s. The implicit assumption is that you’re using Notion AI to extend Notion, not to replace a dedicated enterprise search layer. For teams under 200 people this is usually fine. Above that, you start hitting the limits.

Mem doesn’t have this problem because it doesn’t try to solve it. Your data is what you put into Mem, plus a few integrations like Apple Notes and email forwarding. There’s no Confluence connector. No Salesforce. No permissions model beyond “your account.”

Pricing reality and the hidden costs

Public pricing tells you almost nothing about what you’ll actually spend.

Glean lists no public per-seat price. Quotes typically start in the high-five-figures annually for a small deployment and scale into six figures for mid-market. The hidden cost is the integration work - even with their professional services, expect to commit one engineer or IT admin for the first month and recurring time after that to keep connectors healthy.

Notion AI is $10 per user per month on top of your Notion subscription. For a 50-person team on Notion Business, that’s roughly $4,500 per year added to a ~$9,000 per year Notion bill. Hidden costs are lower because you probably already have Notion deployed; connector setup is a same-week project rather than a quarter-long program.

Mem charges a flat per-user monthly fee in the low-teens dollar range at the time of writing. There’s no enterprise tier with separate negotiation. You sign up, you pay, you use it.

What to actually test before committing

Don’t trust a vendor demo. Run your own queries against your own data for at least two weeks. The signals that matter:

Hit rate on questions you can verify. Pick 20 questions you already know the answer to - decisions from old meetings, status of past projects, who owns a given service. Run them through the tool. Anything below 70 percent accuracy on questions where the source material clearly exists in the index is a red flag.

Latency and timeout behavior. Generated answers should arrive in under 10 seconds for typical queries. If you’re regularly hitting 20-plus seconds or seeing timeout errors during a trial, production will be worse, not better.

Citation quality. Every answer should link to source documents. Open three citations at random per query and check that the source actually says what the answer claims. Hallucination rates vary by tool and by question type, and the only way to know yours is to look.

Permission edge cases. Have a low-permission user run queries about high-permission content. If the tool ever surfaces a snippet from a doc that user couldn’t open natively, you have a leak.

The honest summary: pick Glean if you’re a 1,000-person company with budget and an IT team. Pick Notion AI if you already use Notion and want a fast upgrade that covers most of your team’s questions. Pick Mem if you’re a single person who wants a smarter notebook. Treating these three as direct competitors makes the decision harder than it needs to be - they’re solving overlapping but meaningfully different problems.