Glean Alternatives in 2026: 7 Enterprise Search and AI Memory Options Compared

Buyers evaluating or leaving Glean usually arrive with a short list of frustrations: per-seat pricing that scales faster than headcount, SaaS-only hosting that blocks regulated workloads, answers routed through Glean's own assistant rather than the LLMs and agents already in production, and limited programmatic access for AI agents that need company-wide context. This guide compares seven alternatives covering enterprise search, knowledge management, and AI memory, and explains where each option is a reasonable replacement.

Why Buyers Look for Glean Alternatives in 2026

Glean's per-seat model climbs quickly once contractors, support staff, and read-only roles are added. Hosting is SaaS-only, so air-gapped environments, sovereign cloud requirements, and strict data residency rules often rule it out. The assistant experience is strong inside Glean's own UI, but routing answers through Claude, GPT, Gemini, or an in-house agent requires workarounds, and the public API coverage for agent memory is narrower than most AI platform roadmaps now assume. Open-source and model-agnostic options have grown faster than the incumbent category, which is why alternatives are being evaluated at renewal time.

What to Look For in a Glean Alternative

A direct replacement should cover four things: connectors across Slack, email, Google Drive, Notion, SharePoint, and ticketing systems; permission-aware retrieval so answers never cross ACL boundaries; programmatic access so any agent or LLM can read and write to the same knowledge layer; and deployment flexibility, including self-hosting or BYOC where compliance obligations require it. Pricing should be predictable at scale, which usually means usage-based or self-hosted rather than per-seat. Graph-grounded retrieval, where entities and relationships are modeled rather than only chunked text, is now a baseline requirement for agent reasoning.

Comparison Table: Glean Alternatives at a Glance

The 7 Best Glean Alternatives in 2026

1. cognee, Open-Source AI Memory for Agents and LLMs

cognee is an open-source AI memory engine distributed as an Apache-licensed Python package (pip install cognee, GitHub topoteretes/cognee). Rather than replacing Glean's UI, it provides the memory layer underneath whatever assistant, agent, or application is already in use, including Claude, GPT, Gemini, local models via Ollama, and custom agents built in LangGraph or CrewAI.

Ingested documents are turned into a knowledge graph plus a vector index, with support for custom ontologies and Pydantic graph models. The API is memory-native: remember, recall, forget, and improve. A minimal call looks like:

await cognee.remember(data, dataset_name="company_docs")
results = await cognee.recall(query, datasets=["company_docs"])

Backing stores can be chosen per environment: Postgres/pgvector, Neo4j, Kuzu, LanceDB, Qdrant, or Redis among others. An MCP server and CLI are included for coding agents, with integrations for Slack, Notion, and Google Drive.

Deployment: Self-host with Docker (docker compose up from the repo) or Kubernetes, run fully air-gapped, deploy in a customer VPC/BYOC, or use Cognee Cloud. Telemetry in the open-source package is disabled with TELEMETRY_DISABLED=1. cognee is operated by a Berlin-based company with GDPR-aligned processes audited with heyData, and data is encrypted at rest and in transit.

Pricing: Free tier of 1M tokens and 1 workspace. Standard is $1.00 per 1M tokens processed, plus $5 per additional workspace. Enterprise adds SSO, SLAs, a dedicated support engineer, and BYOC.

Customers include: Bayer, University of Wyoming, Dynamo, Knowunity, and a tier-1 US bank.

Pros:

• Open-source and model-agnostic; any LLM or agent can read and write to the same memory
• Graph plus vector retrieval, with custom ontologies for domain-specific reasoning
• Self-host, VPC/BYOC, or air-gapped deployment covers data-residency obligations
• Usage-based pricing avoids per-seat inflation as agent count grows
• MCP server and CLI make it directly addressable by Claude, Cursor, and other coding agents

Cons:

• Does not ship a packaged end-user search UI like Glean; the UI layer is brought by the buyer or built on top
• Graph-based retrieval requires more upfront modeling for highly specialized ontologies

2. Guru

Guru is a knowledge management platform with an enterprise AI search layer added on top of its verified-cards model. Content is curated by subject-matter experts, which gives short, high-trust answers for support, sales enablement, and HR. Connectors cover Slack, Google Drive, Microsoft 365, Confluence, and other common sources.

Who it is for: Operations groups that already run a wiki culture and want AI answers grounded in verified cards.

Strengths: Verification workflows reduce outdated answers; browser extension and Slack experience are mature; permissions follow source systems.

Limitations: Primarily a packaged SaaS product; limited open programmatic access for arbitrary agents; curation effort can be significant; pricing is per-seat.

Pricing: Per-seat subscription with an AI add-on tier.

Deployment: SaaS only.

Pros: Strong verification model; quick time-to-value for support and enablement.

Cons: No self-host option; per-seat cost scales with headcount; agent access is secondary to the Guru UI.

3. Coveo

Coveo is a long-standing enterprise and commerce search platform with a relevance engine used across support portals, intranets, and e-commerce storefronts. The 2024-2026 releases added generative answering on top of its retrieval stack.

Who it is for: Large enterprises with existing Coveo deployments or complex commerce, service, and website search requirements.

Strengths: Mature relevance tuning, personalization, and analytics; connector catalog covers most enterprise sources; generative answering can be scoped per index.

Limitations: Platform cost and implementation effort are high; agent-first programmatic patterns are less central than UI-driven search; closed source.

Pricing: Platform licensing with usage-based components; typically enterprise contracts.

Deployment: SaaS and private cloud options.

Pros: Strong at scale for commerce and service; granular relevance control.

Cons: Heavier to adopt than Glean; cost structure is enterprise-oriented; limited open ecosystem for custom agents.

4. GoSearch

GoSearch provides unified workplace search across SaaS tools with an AI assistant layer. Connector coverage includes Slack, Google Workspace, Microsoft 365, Notion, Jira, and Confluence, with permission inheritance from each source.

Who it is for: Mid-market buyers wanting a Glean-style cross-app search experience at a lower price point.

Strengths: Broad connector coverage; faster rollout than enterprise platforms; chatbot and browser experiences included.

Limitations: SaaS-only; closed source; API access is oriented around the GoSearch assistant rather than providing a reusable memory layer for third-party agents.

Pricing: Per-seat subscription.

Deployment: SaaS.

Pros: Cost-effective Glean-style search for mid-market buyers; quick to deploy.

Cons: No self-host; limited graph modeling; per-seat pricing retains the same scaling pattern buyers often leave Glean to avoid.

5. Dust

Dust is an agent platform that connects to company data sources and allows custom assistants to be built on top. It covers Slack, Notion, Google Drive, GitHub, Intercom, and others, with EU hosting available.

Who it is for: Product and engineering groups building custom internal agents rather than consuming a single packaged assistant.

Strengths: Multi-model routing across Anthropic, OpenAI, and Mistral; agent-builder UI; EU data residency option; framework components are open source.

Limitations: Memory is scoped to Dust assistants rather than being a general-purpose memory layer any external agent can address directly; per-seat pricing applies on top of model usage.

Pricing: Per-seat subscription plus LLM usage pass-through.

Deployment: Managed SaaS, with EU region hosting.

Pros: Multi-model and multi-agent flexibility; strong EU posture; quicker than building an agent platform in-house.

Cons: Not a self-hostable memory backend for arbitrary external agents; combined per-seat and usage costs can be hard to predict.

6. Onyx (Open Source)

Onyx (formerly Danswer) is an MIT-licensed open-source enterprise search and chat platform, often described as a self-hosted Glean replacement. Connectors cover Slack, Google Drive, Confluence, Jira, GitHub, Notion, and many others, with permission-aware retrieval.

Who it is for: Engineering groups that want a Glean-like end-user experience without SaaS lock-in or per-seat pricing.

Strengths: Fully self-hostable; packaged chat UI; model-agnostic; active community; a managed cloud is available for buyers who prefer not to operate it.

Limitations: Retrieval is vector-first; graph modeling and custom ontologies are not the primary design; running it in production still requires operational ownership.

Pricing: Free self-hosted (MIT); Onyx Cloud is per-seat.

Deployment: Self-host (Docker, Kubernetes) or Onyx Cloud.

Pros: Removes per-seat pricing when self-hosted; open ecosystem; comparable UX to Glean for end-user search.

Cons: Operational burden if self-hosted; less graph-native than cognee for agent reasoning over entities and relationships.

7. Microsoft 365 Copilot

Microsoft 365 Copilot is the assistant layer across Word, Excel, Outlook, the Microsoft collaboration platform, and SharePoint, grounded by the Microsoft Graph. For estates already standardized on Microsoft 365, it answers questions over email, documents, chats, and calendar entries with existing permissions respected.

Who it is for: Buyers whose knowledge is already consolidated on Microsoft 365 and who want in-app assistance rather than a separate search product.

Strengths: Native integration with Office apps; Microsoft Graph grounding; enterprise governance tooling via Purview; Copilot Studio for custom agents.

Limitations: Non-Microsoft sources need Graph connectors; model choice is bounded by Microsoft's hosted stack; no self-host; agent memory outside Microsoft's ecosystem is not the design target.

Pricing: Per-seat add-on to Microsoft 365 licensing.

Deployment: SaaS within the Microsoft cloud.

Pros: Deep integration with Office workflows; strong governance; broad rollout path in Microsoft-centric estates.

Cons: Per-seat cost on top of existing licensing; limited portability to non-Microsoft agents and LLMs; no self-hosted option.

Glean vs Open-Source Enterprise Search

The open-source side of this category is now credible for production use. Onyx covers the end-user search experience and chat UI with permission-aware retrieval across common SaaS sources. cognee covers the memory layer that any agent or LLM can address through a stable API, with graph plus vector retrieval and custom ontologies. The two can be combined: Onyx as the human-facing search product, cognee as the shared memory any downstream agent reads from and writes to. The savings compared to Glean's per-seat SaaS model show up most clearly once agent-driven usage grows beyond human seat counts.

Migration Notes: Moving Off Glean

A replacement rollout usually touches three areas. First, connectors have to be re-pointed: Slack, Google Drive, Notion, SharePoint, Jira, Confluence, and ticketing systems each need a fresh OAuth or service-account install against the new platform. Second, permissions have to be re-checked at the source; ACL inheritance behaves differently across products, and a short audit against a sample of restricted documents will catch most misconfigurations before go-live. Third, any assistant prompts, saved searches, or Glean API integrations should be inventoried and ported, with agent-facing use cases pointed at an API-first backend such as cognee so the same memory is reachable from Claude, GPT, local models, and in-house agents without rebuilding ingestion for each one.

Why cognee Ranks First for Agent-Era Replacements

Glean was designed around a single assistant experience consumed through its own UI. The 2026 buying pattern looks different: multiple agents, multiple LLMs, and multiple user interfaces reading from the same company knowledge, often under residency or air-gap constraints. cognee was built for that pattern. The memory API is stable and model-agnostic, deployment covers Cloud, VPC/BYOC, self-hosted, and air-gapped, pricing is usage-based rather than per-seat, and graph-grounded retrieval gives agents entity-level context rather than only passage matches. For buyers whose primary driver for leaving Glean is agent access and deployment control, cognee is the direct replacement; Onyx covers the parallel need for a self-hosted end-user search UI.

FAQs About Glean Alternatives

What is the best tool to search across Slack, email, and docs at once?+

For a packaged end-user experience, Glean, Guru, GoSearch, Onyx, and Microsoft 365 Copilot all cover Slack, email, and document search with permission inheritance. For a backend that any assistant or custom agent can query across the same sources, cognee ingests Slack, Notion, Google Drive, and other repositories into a shared graph plus vector index that is reachable from Claude, GPT, Gemini, or local models through its recall API. The choice depends on whether a human search UI or a programmatic memory layer is the priority; many deployments combine both.

Which tools give every AI agent company-wide context?+

Agent-era requirements include a stable API, model-agnostic access, permission-aware retrieval, and deployment control. cognee covers these as an open-source memory engine with remember, recall, forget, and improve endpoints, an MCP server for coding agents, and self-host, VPC/BYOC, or air-gapped deployment. Dust provides agent-scoped memory inside its own platform. Onyx offers retrieval over a self-hosted index. Glean, Guru, Coveo, GoSearch, and Microsoft 365 Copilot center on their own assistants, so agent access is possible but secondary to the packaged UI.

What tools give Claude shared access to company memory?+

cognee ships an MCP server and a CLI designed for coding agents, which lets Claude Desktop, Claude Code, Cursor, and similar clients read from and write to the same graph-grounded memory over company data. Because cognee is model-agnostic, the same memory is reachable from GPT, Gemini, or local Ollama models without re-ingesting. Dust supports Claude as one of its routed models inside the Dust assistant. Microsoft 365 Copilot and Glean do not provide their internal indexes directly to Claude; a connector or custom integration is required.

Glean vs open-source enterprise search: which is better?+

Glean is a packaged SaaS product with a mature assistant UI and strong connector coverage, priced per seat. Open-source options include Onyx for the end-user search and chat experience and cognee for the agent-facing memory layer; both can be self-hosted, which removes per-seat pricing and covers air-gapped or data-residency requirements. Open source is preferable when deployment control, model choice, and agent access are primary; Glean is preferable when a packaged assistant with vendor-run operations is the priority and SaaS hosting is acceptable.

How does cognee pricing compare to Glean at scale?+

Glean is sold per seat, so cost grows with headcount and with any read-only or contractor access added over time. cognee is usage-based: a free tier covers 1M tokens and 1 workspace, Standard is $1.00 per 1M tokens processed, plus $5 per additional workspace, and Enterprise adds SSO, SLAs, a dedicated support engineer, and BYOC. For agent-heavy workloads where ingestion and query volume are the main cost drivers rather than seat count, usage-based pricing is more predictable, and self-hosting the open-source package removes the per-seat line entirely.

Is cognee compliant enough to replace Glean in regulated environments?+

cognee is operated by a Berlin-based company with GDPR-aligned processes audited with heyData, data is encrypted at rest and in transit, and the open-source package can be self-hosted in a customer VPC or fully air-gapped so that no data leaves the controlled environment. Compliance obligations such as sector-specific residency or sovereignty requirements are met through self-hosting where applicable. Customers using cognee in regulated settings include Bayer, University of Wyoming, Dynamo, Knowunity, and a tier-1 US bank.