Companies of 20 to 500 people keep running into the same wall: they want an AI tool that actually knows their business, but every answer to "how do I do that" points at Glean, and Glean is priced and built for a company ten times their size. That's the itch behind almost every glean alternative search.
Glean is a genuinely good product. It just isn't aimed at you. This piece is an honest look at what it does well, where the gap actually sits, and what to check before you commit to something else.
What Glean gets right
Glean built its name on federated enterprise search: one search box across Slack, Google Drive, Confluence, Jira and dozens of other connectors, with results ranked by relevance to your role. It layers a knowledge graph on top, so the platform understands that a document, a person and a project are related, not just that they share a keyword.
That's real engineering, and the market has rewarded it. Glean's annual recurring revenue reportedly doubled to $200 million by the end of 2025 and had climbed past $300 million by May 2026, on a $7.2 billion valuation. That is not a company chasing a niche; it is a company that has found a large one, in large enterprises with the security teams and budgets to match.
Where the gap actually is
Ask a 40-person company what they need and it is rarely "search across 80 connectors." It's closer to: give our support agent and our internal tools an accurate, current, access-controlled answer about our own business, without shipping every internal document to a vendor's cloud.
Three things tend to matter more than connector count at this size:
- Self-hosting. A company that size often cannot, or will not, sign off on sending its entire company memory to a third party's servers, no matter how good the SOC 2 report is.
- Retrieval-time entitlement, not a bolt-on. If a contractor and a founder ask the same question, they should not get the same answer just because the underlying search index doesn't distinguish them.
- Setup cost. Glean's onboarding assumes a dedicated admin and a procurement process. A 20-person team doesn't have either.
"Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust."
OWASP Top 10 for Large Language Model Applications, on excessive agency
A worked example
Picture a 60-person operations consultancy. Their CRM lives in Postgres, contracts sit in Drive, and Slack carries most of the day-to-day decisions. A junior analyst asks their AI assistant what a client's renewal terms are. Without entitlement enforced at retrieval, the assistant happily surfaces payroll notes from the same client's onboarding thread, because nothing in the retrieval layer knew the analyst wasn't supposed to see them. With entitlement scored before synthesis, that memory object scores zero for this asker and is dropped from the ranked set before an answer is even drafted. That is the difference between "the AI is smart" and "the AI is trustworthy."
Comparing the shape of the problem
| Dimension | Glean | Typical self-hosted alternative | What actually matters |
|---|---|---|---|
| Company size fit | 500+ employees, dedicated IT | 20-500 employees, no dedicated IT | Setup time and who maintains it |
| Data residency | Vendor cloud (with enterprise controls) | Your own infrastructure | Whether it can leave your network at all |
| Entitlement enforcement | At retrieval, mature | Varies widely, often bolted on | Where in the pipeline access is checked |
| Freshness handling | Re-indexes on a schedule | Often static after ingest | Whether a stale answer is ever served as current |
| Pricing model | Enterprise contract, custom | Usage-based or free self-hosted | Whether cost scales with headcount or with usage |
Table 1: sample comparison points worth checking against any glean alternative, not vendor marketing.
Common pitfalls when switching
- Assuming "self-hosted" means "secure." Self-hosting only answers where the data lives. It says nothing about whether the tool checks who is allowed to see it.
- Skipping the freshness question. A tool that indexes once and never re-checks a fact against its source will confidently repeat something that stopped being true weeks ago.
- Underestimating agent access. If your AI tools are going to be called by other software, not just typed into by a human, check that the product exposes an MCP server or a documented API, not only a chat window.
- Comparing headline price, not the entitlement tier. Several tools put fine-grained permissions behind their highest paid plan, which defeats the point of switching for a smaller team.
What to ask any vendor before you switch
A demo will always look clean. The questions below are harder to dodge than "does it work with our tools," and the answers tell you more about the architecture than a sales deck ever will.
- "Where exactly is entitlement checked?" Push past "we support permissions." Ask whether an unentitled record is filtered before an answer is drafted, or redacted from an answer that has already been written. The first is a retrieval-time guarantee; the second is a best-effort patch that depends on the model behaving.
- "What happens when a source record changes?" Some tools re-index nightly. Some re-index never, after the initial ingest. Very few re-check a specific fact at the moment someone asks about it, which is the only version of "current" that actually holds up when a contract or a headcount number changes mid-week.
- "Can our own software call this, not just our people?" A growing share of the actual demand for a context layer comes from other software: a support bot, an internal Slack app, an automation pipeline. If the answer is "we have a chat UI," that rules out most agent-facing use cases outright.
- "What does self-hosting cost us in maintenance, not just licence fees?" A Docker image you have to patch yourself is a real ongoing cost. Ask what the update cadence looks like and who is expected to run it.
None of this is unique to evaluating a glean alternative specifically. It's the same checklist worth running against any tool that is about to sit between your team and your company's own data.
Frequently asked people-also-ask questions
Is enterprise search software overkill for a 40-person team?
Usually, yes, if "enterprise search software" means Glean's full connector catalogue and knowledge graph. What a smaller team needs is narrower: a condensed, current, permission-aware answer from the handful of systems that actually matter, which is a different and cheaper problem to solve.
How do I check if a glean alternative actually enforces permissions properly?
Ask the vendor directly where entitlement is checked in the pipeline. If the honest answer is "after the answer is generated, we redact anything sensitive," that is a weaker guarantee than a retrieval step that never lets an unentitled record reach synthesis in the first place.
Where this leaves you
If you are a large company with a dedicated security review process and money to match, Glean is a reasonable default and its growth numbers reflect that it earns its price for that buyer. If you are a 20-500 person team, the honest answer is to look at the specific mechanisms above rather than the brand name: self-hosting, retrieval-time entitlement, and freshness handling, in that order.
Contextely was built directly for that gap: self-hostable with Docker, retrieval-time entitlement enforced inside the scoring function rather than after the fact, and memory objects that re-check themselves against their source when they go stale. See how it stacks up against the closest agent memory alternatives if that is the comparison you are actually trying to make, or read the security model directly rather than taking a vendor's word for it. If you want the numbers before anything else, the pricing page has no hidden entitlement tier.