Glean Alternatives in 2026: 7 Enterprise AI Search Platforms Compared

Glean built the reference product for enterprise AI search: one permissions-aware index across a company’s entire SaaS stack, with answers instead of links. It also built the reference price tag — unpublished, per-seat, and reportedly north of $50,000 a year before anyone types a query. The search for a Glean alternative usually starts right there, and then turns up four or five genuinely different architectures hiding under the same “AI search” label.

This guide compares the seven alternatives that come up most in 2026 evaluations — Microsoft 365 Copilot, Guru, Onyx, GoSearch, Coveo, Notion AI, and Fluree — with every competitor claim sourced to the vendor’s own surfaces and dated. It’s part of the same comparison series as our enterprise AI search guide and the stack-wide view at flur.ee/compare.

Why Teams Look for Glean Alternatives

Glean indexes 100+ enterprise systems deeply — 275+ apps counting its MCP-based integrations (MCP, the Model Context Protocol, is the open standard AI agents use to connect to tools) — inherits permissions from each of them, and answers questions with citations across all of it. The reasons buyers look elsewhere cluster into four groups:

• Cost. Glean publishes no price list — glean.com has no pricing page, and every deal is custom-quoted. Third-party estimates consistently describe per-seat licensing with annual minimums around $50–60k, a mandatory support fee, and renewal increases — estimates Glean neither confirms nor denies, which is itself the problem: you can’t know what it costs without a sales cycle.
• Search stops at the answer. The recurring reviewer critique — in Gartner Peer Insights’ words, Glean is “great at finding information but stops short of actually doing anything with it.” Glean Agents (launched 2025) is the company’s answer; buyers evaluating today are weighing a mature index against a young agent platform.
• Control. The code is proprietary, security detail sits behind an NDA’d trust portal, and even the “your own cloud” deployment runs Glean-managed software. There is no self-hosted or air-gapped option, and model choice — though broad — is limited to Glean’s supported catalog.
• Relevance consistency and rollout weight. G2 reviewers describe results that “can feel a bit broad” and want more control over ranking; Gartner Peer Insights reviewers call setup “a significant undertaking” that “puts real strain on internal resources.”

Where Glean still wins — and who should stay. The connector library (275+ apps, 100+ deeply indexed) is the largest first-party catalog in the category, its permission inheritance is the most production-hardened, and the numbers back the product: $200M+ ARR, 27 billion documents indexed, and 4.4–4.5 stars across 100+ Gartner Peer Insights reviews. Model choice spans 15+ LLMs with bring-your-own keys, and the 2025 agent platform ships real scheduling, triggers, and write-action guardrails. If you run 30+ SaaS tools, leakage is unacceptable, and the budget clears six figures comfortably, Glean remains the reference standard.

What to Look for in a Glean Alternative

Seven criteria decide most of these evaluations. They define the comparison table below and the fields inside every profile.

• Connectors and source coverage — which systems it reads out of the box, and whether the ones that matter to you are first-class indexed sources or an afterthought behind an API.
• Permission inheritance — whether access controls are enforced from the source system at query time, automatically. The architectures differ more than the marketing does: live query-time checks, synced ACLs, and role-based layers all get called “permission-aware.”
• Answers and citations — a synthesized answer with links to the documents behind it, or a ranked list you still have to read; and what the system does when it doesn’t know.
• Agents and actions — whether the tool only answers, or can also run the task: triggered by events or schedules, with logs and audit trails.
• Deployment and data control — cloud, self-hosted, on-premise, air-gapped; and whether the code is open to inspection.
• Model flexibility — whether you choose the LLM, and whether you can switch it later.
• Pricing model and time to value — per seat, usage-based, or self-hosted infrastructure; published numbers or a sales call; days to a working deployment or quarters.

Glean Alternatives Compared

Glean first as the baseline, then the seven alternatives. Short explanations rather than checkmarks — in enterprise software, a checkmark is just a claim someone can dispute.

All claims verified against vendor documentation, August 2026. Vendors move fast — check the linked sources for current terms.

The Best Glean Alternatives in 2026

What these seven share: each removes at least one of the four pressures above — cost opacity, answer-only ceilings, control, or rollout weight. Where they diverge is architecture: a tenant you already run, a curated knowledge layer, an open-source index, a relevance engine, a workspace, or a knowledge graph. Same fields, same order, for every one.

Microsoft 365 Copilot

Microsoft 365 Copilot — being renamed simply “Microsoft Copilot” through 2026 — is the default Glean alternative for any organization whose knowledge already lives in SharePoint, Teams, Outlook, and OneDrive. It’s not a separate search destination: it’s AI threaded through the apps your company already pays for.

How it differs from Glean: it rides an index you already own. Everything in Microsoft Graph is covered natively, permissions and all — and everything outside Microsoft’s walls is where the work begins.

Connectors and source coverage: Native coverage of the entire M365 estate; beyond it, a gallery of 100+ Copilot connectors (Box, Confluence, Salesforce, ServiceNow) in synced and MCP-based federated flavors — much of it partner-built and separately licensed, with custom connectors requiring developer registration and upkeep.

Permission inheritance: Automatic and genuinely enforced at query time inside the tenant — Copilot “only surfaces organizational data to which individual users have at least view permissions”, honoring sensitivity labels. Microsoft’s own caveat: it’s only as good as your SharePoint permission hygiene, which is why the license bundles oversharing-remediation tooling.

Answers and citations: Grounded answers with clickable citations to source items, including connector content. Documented behavior when it doesn’t know: not publicly documented beyond “use your judgment.”

Agents and actions: Copilot Studio agent building is included with the license; autonomous triggers and premium connectors meter Copilot Credits at $0.01 each (or $200 per 25,000), and agent governance at scale is another SKU (Agent 365).

Deployment and data control: SaaS inside your existing tenant, EU Data Boundary support, GCC High for FedRAMP High workloads. No self-hosting, closed source.

Model flexibility: Partial — Microsoft routes among its hosted OpenAI models (GPT-5 family), with opt-in Anthropic models in specific surfaces. No bring-your-own model.

Time to value: Assign licenses in the admin center — no new infrastructure. The real timeline is permission-hygiene cleanup, not deployment.

Pricing: $30/user/month billed yearly, on top of a qualifying Microsoft 365 plan; a free Copilot Chat tier exists for all M365 commercial users; agent usage meters credits on top.

Key features:

• AI inside Word, Excel, PowerPoint, Teams — not a separate destination
• Automatic permission enforcement across the M365 estate
• Copilot Studio agent building included with the seat
• FedRAMP High path via GCC High for public sector

Best for:

• Microsoft-centric organizations wanting AI on infrastructure they already run
• Regulated and public-sector buyers needing GCC High and EU Data Boundary
• Teams that want in-app assistance plus a free org-wide chat tier

Key trade-off: Transparent pricing and automatic permissions — but only inside Microsoft’s walls. Cross-stack coverage means partner connectors, developer maintenance, and relevance that reviewers rate behind purpose-built indexes; and the $30 seat is the entry fee, not the total, once base licenses, agent credits, and governance SKUs stack up.

Verified: August 2026

Guru

Guru attacks the problem from the opposite end: instead of indexing everything and ranking it, Guru maintains a curated, verified knowledge layer — subject-matter experts confirm accuracy on a cadence, and the AI answers only from that governed foundation.

How it differs from Glean: trust over breadth. Glean tells you what exists; Guru tells you what’s true, because a human verified it.

Connectors and source coverage: 100+ integrations including Slack, Teams, Salesforce, Confluence, and Zendesk, plus an API, an MCP server, and no-code paths for custom sources — feeding a unified, permission-aware index.

Permission inheritance: Enforced through role-based access controls within Guru’s governed layer — “employees only see information they’re already authorized to access” — with DLP masking for sensitive data. Note the architecture: this is Guru’s access model, not live query-time checks against each source system’s ACLs.

Answers and citations: Cited answers down to the exact section of the source (“slide 8 of a deck”), with an answer-details view showing reasoning — and uncertain answers route to SME review, surfacing documentation gaps as to-dos.

Agents and actions: Knowledge Agents (launched September 2025) answer, research, run scheduled tasks, and maintain content quality per department. Cross-system write actions are not a documented capability — these are knowledge agents, not workflow agents.

Deployment and data control: Cloud SaaS only; closed source. Strong AI-data posture: zero-day retention with LLM providers, no training on your data.

Model flexibility: Not publicly documented — Guru doesn’t publish which LLMs it uses or offer a model picker.

Time to value: Sales-led. Guru’s current packaging bundles solution engineers and knowledge-architecture services into the deal — a program, not a self-serve tool.

Pricing: As of August 2026, Guru no longer publishes seat prices — pricing is custom “platform and expertise” packaging via sales. (Third-party trackers cite ~$25/seat/month historically; not vendor-confirmed.)

Key features:

• Human verification workflow — SMEs confirm accuracy on a schedule
• Citations to the exact section of the source
• Department-scoped knowledge agents with scheduled tasks
• MCP connectivity to Claude, ChatGPT, and Copilot

Best for:

• Support and sales teams that need one verified answer, not ten results
• Knowledge and enablement leaders who want governance and content health built in
• Companies standardizing AI answers inside Slack, Teams, and the browser

Key trade-off: The verified layer is the product’s spine — and someone has to maintain it. Guru is a knowledge-management program with AI on top, not a crawl-everything engine; agents do knowledge work rather than acting in other systems; and with pricing now sales-led, the transparent self-serve entry it once had is gone.

Verified: August 2026

Onyx

Onyx (formerly Danswer) is the open-source answer: an MIT-licensed enterprise search and agent platform with 31.7k GitHub stars that you can run on your own infrastructure — up to and including fully air-gapped with local models.

How it differs from Glean: control. Everything Glean structurally can’t offer — code inspection, self-hosting, air-gap, bring-your-own-LLM — is Onyx’s reason to exist.

Connectors and source coverage: 40–50+ indexed connectors (Slack, Confluence, Google Drive, SharePoint, GitHub, Salesforce) plus MCP-based sources, with an open codebase where teams write their own.

Permission inheritance: Real, with an important asterisk: Onyx syncs ACLs from source systems so users only see what they’re authorized to view — but permission-sync connectors, SSO, and RBAC are Enterprise Edition features, not part of the free MIT core. And synced ACLs mean a revoked permission applies at the next sync, not instantly.

Answers and citations: Grounded answers with inline citations and a sources sidebar, over hybrid search with knowledge-graph augmentation. Refusal behavior depends on the LLM you configure.

Agents and actions: Custom agents with actions via MCP and OpenAPI — plus web search, deep research, and code execution. Genuinely more than answers.

Deployment and data control: The category’s strongest story: cloud, Docker/Kubernetes self-hosting, or fully air-gapped with local LLMs — Onyx cites a 37,000-user air-gapped university deployment. MIT core; proprietary EE directories.

Model flexibility: Total — any provider, or self-hosted models via Ollama, LiteLLM, vLLM. The strongest BYO-model story on this page.

Time to value: “Self-host in minutes” for a demo; a production rollout (Kubernetes, ACL sync, model serving) is an engineering project you own.

Pricing: Free MIT Community Edition self-hosted; Cloud Business at $20/user/month billed annually; Enterprise (SSO, permission sync, on-prem support) custom. Seed-stage company — $10M raised March 2025, led by Khosla Ventures.

Key features:

• MIT-licensed core you can inspect, extend, and self-host
• Air-gapped deployment with fully local models
• Any LLM — hosted or local — plug-and-play
• Agents with MCP and OpenAPI actions

Best for:

• Security-first and regulated organizations needing self-hosted, VPC, or air-gapped deployment
• Engineering-led teams that want to own and extend their search stack
• Model-agnostic buyers who refuse LLM lock-in

Key trade-off: The free thing and the enterprise thing are not the same thing. The MIT core ships real search and chat — but SSO, permission syncing, and RBAC live in the paid Enterprise Edition, so “free open-source Glean alternative” is only true where everyone may see everything. And you own the operational load and the polish gap of a seed-stage project versus mature SaaS — that’s the price of control.

Verified: August 2026

GoSearch

GoSearch is the value play: Glean-style search plus agents and workflow automation on a transparent $0/$20 ladder, from the team behind GoLinks.

How it differs from Glean: the widest feature surface per dollar — 100+ connectors, multi-LLM choice, no-code agents, and scheduled workflows, priced like a team tool instead of an enterprise program.

Connectors and source coverage: 100+ connectors in three architectures — indexed, federated (real-time, data stays in the source), and MCP — with the MCP path as the custom-source route.

Permission inheritance: Claimed plainly — “users only see data they’re authorized to access” — and federated/MCP connectors inherently check at query time since nothing is copied. Per-connector sync mechanics: not publicly documented at the depth security teams will want.

Answers and citations: Synthesized answers with cited sources across apps; “fully auditable AI” per the agents page. Behavior when unsure: not publicly documented.

Agents and actions: No-code agents that act — update PRs, sync payroll, update CRMs — plus GoSearch Workflows (February 2026): drag-and-drop, scheduled, multi-agent orchestration.

Deployment and data control: Single-tenant SaaS with bring-your-own-cloud on Enterprise; closed source, no on-prem or air-gap.

Model flexibility: Choose OpenAI, Anthropic, or Gemini models per agent; bring-your-own LLM on Enterprise.

Time to value: Genuinely self-serve — free tier with no credit card, connectors “set up in minutes.”

Pricing: Free at $0; Pro at $20/user/month; Enterprise custom — with the enterprise substance (SAML/SCIM, audit logs, API, shared connectors, BYO LLM/cloud) all in the custom tier.

Key features:

• 100+ connectors across indexed, federated, and MCP architectures
• Multi-LLM choice per agent
• No-code agents plus scheduled workflow automation
• Transparent free and $20 tiers

Best for:

• Mid-market teams that want Glean-style search plus agents at self-serve prices
• Ops-minded buyers who want automation, not just answers
• Multi-model shops avoiding platform lock-in

Key trade-off: The least battle-hardened at enterprise scale: a product line from a short-link startup rather than a dedicated search company, with the security features enterprises require gated to the custom-priced tier and less published depth on permission mechanics than its claims imply. The free-to-$20 ladder is real value; the enterprise conversation is still a sales conversation.

Verified: August 2026

Coveo

Coveo is the relevance engineer’s platform: a public company (TSX: CVO) whose search powers customer service, commerce, and workplace experiences — with the most rigorously documented permission machinery in this comparison.

How it differs from Glean: Coveo optimizes the quality of retrieval — tunable ranking, analytics, machine-learning relevance — where Glean optimizes coverage. And increasingly, Coveo grounds other platforms’ agents rather than replacing them.

Connectors and source coverage: 28 documented connectors (Salesforce, ServiceNow, SharePoint, Confluence, Zendesk among them) plus Push/REST/GraphQL APIs carrying the long tail — a narrower first-party catalog than the index-everything vendors, by design.

Permission inheritance: The reference documentation for how this should work: early-binding security extracts item permissions at crawl time, resolves each query to a single identity across systems, and enforces at query time — including inside generative answers.

Answers and citations: Relevance Generative Answering does two-stage retrieval then grounded generation with clickable citations — and its model card documents abstention testing: the model is evaluated on refusing to answer when the retrieved content doesn’t contain the answer. That’s rarer than it should be.

Agents and actions: The strategy is to be the grounding layer: Coveo for Agentforce, custom agent actions via its passage-retrieval API, and a hosted MCP server (GA February 2026) feeding ChatGPT Enterprise and Claude.

Deployment and data control: Cloud SaaS on AWS across US, Canada, EU, and Australia regions; HIPAA edition and BYOK as add-ons; closed source, no self-hosting.

Model flexibility: Coveo-managed — a third-party LLM it hosts and operates; customer model choice is not publicly documented.

Time to value: Months, not days — third-party review data puts average implementation near four months. This is a platform you engineer, not a tool you switch on.

Pricing: Sales-gated and consumption-priced — query-metered units, with generative queries metered separately and compliance features as add-ons.

Key features:

• Early-binding, identity-resolved permission enforcement
• Abstention-tested generative answering with citations
• Relevance tuning, analytics, and ML ranking
• MCP server and agent-grounding APIs

Best for:

• Customer service organizations on Salesforce or ServiceNow needing grounded self-service and case deflection
• Commerce teams running search, recommendations, and personalization on one engine
• Regulated enterprises needing region choice, HIPAA options, and BYOK

Key trade-off: Enterprise-grade grounding, paid for in procurement cycles, integration months, and query-metered costs — with no published prices and no customer LLM choice. Workplace search is one of Coveo’s three businesses, not its center of gravity; buyers wanting a turnkey employee search tool are buying more platform than they need.

Verified: August 2026

Notion AI

Notion AI is the consolidation play: if your docs, wikis, and projects already live in Notion, AI search across the workspace plus a dozen common tools comes included in the seat you already pay for.

How it differs from Glean: it’s workspace-first. Notion doesn’t index your enterprise; it makes the workspace you work in searchable, conversational, and — since Notion 3.0 — agentic.

Connectors and source coverage: A dozen named connectors — Slack, Teams, Google Drive, SharePoint/OneDrive, Jira, GitHub, Linear, Gmail, Outlook — with connectors generally reaching back about a year of content and initial syncs of up to 72 hours. No documented custom-connector framework for search.

Permission inheritance: Asserted plainly — users can’t get responses from resources they lack access to — though without the deep ACL machinery documentation that dedicated engines publish.

Answers and citations: Enterprise Search “always cites its sources”. When unsure: “double-check all answers for accuracy” is the documented posture.

Agents and actions: Real and growing fast — Notion 3.0 agents (September 2025) do multi-step work in the workspace, and Custom Agents (February 2026) run on schedules and act in external tools via MCP, with logged, reversible runs.

Deployment and data control: Cloud SaaS; US/EU data residency on Enterprise (free of charge); zero data retention with LLM providers on Enterprise; closed source.

Model flexibility: User-level choice among GPT, Claude, and Gemini models — unusual at this price point.

Time to value: Self-serve — admin consent, connect apps, and indexing completes within 72 hours.

Pricing: The transparency benchmark: AI included in Business at $20/member/month (annual), Enterprise custom; Custom Agents meter credits at $10 per 1,000 from May 2026.

Key features:

• AI search, meeting notes, and research included in the Business seat
• User-level model choice: GPT, Claude, or Gemini
• Agents that act in the workspace and, via MCP, beyond it
• EU data residency at no extra charge

Best for:

• Teams already living in Notion who want search over the workspace plus common tools
• SMB and mid-market buyers who want published pricing and self-serve rollout
• Teams that want agents doing the work where the work already lives

Key trade-off: Workspace-first cuts both ways: a dozen connectors with a roughly one-year lookback is a fraction of dedicated enterprise-search coverage, and permission enforcement — while asserted — isn’t documented at the auditable depth regulated buyers need. When Notion is the system of record, it’s the obvious value; when knowledge sprawls across a hundred repositories, you’ll outgrow it.

Verified: August 2026

Fluree

Fluree is the one entry on this list from a different category — and that’s the honest frame for it. It’s not a workplace-search suite; it’s a knowledge graph platform whose search is one surface of a governed semantic graph. You connect structured and unstructured sources, Fluree resolves them into entities and relationships with policy attached to the data itself, and people and AI agents query the same governed graph — with cited answers.

How it differs from Glean: Glean indexes your apps; Fluree builds a knowledge model of your business. That difference shows up exactly where search tools strain: multi-part questions that span silos, governance you have to prove, and AI agents that need the same policy enforcement humans get.

Connectors and source coverage: 300+ connectors spanning SaaS apps, files, documents, and lakehouse tables (Snowflake, Databricks, S3, Salesforce, ServiceNow among them) — feeding a semantic graph rather than a search index.

Permission inheritance: Architecturally different from every synced-ACL or role-layer approach above: policy lives with the data and is evaluated at the entity, relationship, and property level as part of retrieval. Every caller — human or agent, through any interface — sees only its governed slice; nothing sensitive leaks into embeddings or shared indexes, and every access is logged.

Answers and citations: Direct answers with the sources, records, and relationships behind them attached. Retrieval is hybrid in one governed pass — graph traversal, BM25 full-text, and HNSW vector search against the same engine and schema.

Agents and actions: Fluree publishes an MCP endpoint any compatible client can consume — Claude, ChatGPT, Bedrock, or your own agents — so AI tools work from the same governed knowledge, under the same policies, as your people. Agents inherit data-layer permissions automatically, get persistent memory via Fluree Memory, and receive token-efficient Agent JSON output.

Deployment and data control: Serverless hosted platform with a source-available core database you can run yourself; single-tenant and private deployment on Enterprise.

Model flexibility: The MCP-first design inverts the question: external agents bring whatever model their client runs. Internal answer-generation model selection: not publicly documented.

Time to value: $0 to start, serverless with zero idle cost — connect sources and query. Building out the full semantic model of your business is incremental from there.

Pricing: Published and usage-based — free to start, with fuel covering tokens, storage, and compute; transparent tiers, Enterprise custom.

Key features:

• Policy enforced in the data — entity, relationship, and property level
• One governed graph for people and MCP-connected AI agents
• Hybrid graph + keyword + vector retrieval in a single pass
• Cited, verifiable answers over structured and unstructured sources

Best for:

• Organizations where “prove what the AI saw” is a compliance requirement, not a preference
• Teams standing up agentic AI that needs governed access to enterprise context
• Data-centric buyers who want the knowledge layer, not just the search box

Key trade-off: Fluree is not a turnkey workplace-search suite: it doesn’t match Glean’s depth of per-app indexing across hundreds of SaaS tools, and its payoff — a governed semantic graph — asks you to care about your knowledge model, not just your search results. That investment compounds where search tools plateau: the same graph that answers your people powers every agent you deploy next, under the same policies. This is the use case Fluree runs in production at a global financial services leader — analysts getting direct, cited answers across hundreds of thousands of documents and datasets.

Verified: August 2026

Why Teams Choose Fluree Over Glean

The buyers who land on Fluree usually arrive via the governance and agent doors rather than the search door. (For the full head-to-head, including the honest “choose Glean when” routing, see Fluree vs Glean.) Four differentiators, each something Glean can’t claim:

• Policy lives in the data, not the platform. Glean inherits permissions into its index; Fluree evaluates policy inside the data itself — at the entity, relationship, and property level — during every retrieval. There’s no second permission model to maintain, and nothing sensitive enters a shared index or embedding store to begin with.
• One governed graph for every AI tool you run. Through Fluree’s MCP endpoint, Claude, ChatGPT, Bedrock, and your own agents consume the same governed knowledge under the same policies as your employees — instead of each AI tool becoming its own permission project.
• Answers grounded in a knowledge graph, not chunk similarity. Multi-part questions traverse typed relationships across silos in one pass — the failure mode of similarity-ranked retrieval. Fluree’s April 2024 study, GraphRAG for GenAI Accuracy, documents the accuracy gap semantic grounding closes.
• Pricing you can see. Published, usage-based tiers with a free start — against unpublished per-seat quotes with reported five-figure minimums. You can find out what Fluree costs without talking to us.

If those sound like your requirements — or like where your AI roadmap is heading — start with a technical conversation. The same graph that answers questions today is the substrate your agents will need tomorrow. For the wider picture of where Fluree sits against the whole data stack, see flur.ee/compare.