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    Fluree vs Glean

    Glean is the reference product for enterprise workplace search: one permissions-aware index across hundreds of SaaS apps. Fluree is a governed knowledge graph: entities, relationships, and policy modeled in the data itself, answering people and AI agents alike. Both return cited answers instead of links — they get there through fundamentally different architectures.

    This page compares them honestly, capability by capability — including where Glean is the better choice.

    Every Glean claim on this page is sourced and was last verified August 2026.

    The Short Answer

    Which should you choose?

    Neither tool is better at everything — they're built for different jobs. The honest routing, up front.

    Choose Fluree when…

    • AI agents are part of the plan, and they need the same governed access as your people
    • Compliance requires proving what a system could see when it answered
    • Your questions span structured data and documents — entities and relationships, not just passages
    • You want policy enforced in the data, with nothing sensitive entering shared indexes or embeddings
    • You want published pricing and a free start, not a sales cycle to learn the cost

    Choose Glean when…

    • Pure workplace search across a sprawling SaaS estate is the whole requirement
    • You need the deepest per-app indexing available — 275+ apps, 100+ deeply indexed
    • Years-hardened inherited permission enforcement at massive scale is the deciding factor
    • A six-figure enterprise budget fits how you buy software
    • You want a mature, no-code agent platform bundled with the search product
    Side by Side

    Fluree vs Glean, capability by capability

    Short explanations rather than checkmarks — both products evolve quickly, and a checkmark is just a claim someone can dispute.

    Capability

    Competitor

    Glean

    Fluree

    Fluree

    What it builds

    A search index over your apps — documents, messages, permissions
    A semantic knowledge graph of your business — entities, relationships, meaning

    Permissions

    Source ACLs inherited and synced into the index, updated in real time
    Policy evaluated in the data at query time — entity, relationship, property level

    Answers

    Cited answers from multi-stage RAG over indexed content
    Cited answers from graph traversal + keyword + vector in one governed pass

    AI agents

    Glean Agents platform (GA 2025) with triggers and write-action guardrails
    MCP endpoint — any agent works from the same governed graph as people

    Model choice

    15+ hosted LLMs with bring-your-own keys
    MCP-first: each agent client brings its own model

    Data foundation

    Index rebuilt from sources; 27B+ documents under management
    Governed graph with provenance — structured and unstructured resolved together

    Deployment

    Single-tenant SaaS — Glean-managed even in your cloud; closed source
    Serverless hosted platform + source-available core you can run yourself

    Connectors

    275+ apps, 100+ deeply indexed — the category's largest first-party catalog
    300+ connectors incl. lakehouse tables (Snowflake, Databricks, S3)

    Pricing

    Unpublished, per-seat, custom-quoted; third-party estimates cite ~$50k+ minimums
    Published, usage-based — $0 to start, zero idle cost

    Short explanations rather than checkmarks — both products evolve quickly. Verified August 2026.

    Why Fluree

    What Fluree offers that Glean can't

    Four differences that are architectural, not incremental — each something Glean's index-based design genuinely doesn't claim.

    Policy lives in the data, not the platform

    Glean inherits permissions into its index; Fluree evaluates policy inside the data itself during every retrieval — at the entity, relationship, and property level. 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 (MCP — the Model Context Protocol — is the open standard AI tools use to connect to data), 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

    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.

    Identity, resolved — not just found

    Search finds every document that mentions “Acme Corp”; it can’t tell you that Salesforce’s Acme Corp and Zendesk’s ACME Inc. are the same customer. Fluree resolves entities across systems into governed golden records — so answers draw on one identity, not a pile of mentions.

    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.

    Fair Play

    Where Glean wins

    A comparison you can trust has to say this part out loud.

    Coverage and maturity: 275+ app integrations with 100+ deeply indexed is the largest first-party connector catalog in the category, and its inherited-permission enforcement is the most production-hardened, refined across 27 billion indexed documents and a $200M+ ARR customer base. Model choice spans 15+ LLMs with bring-your-own keys, and the 2025 agent platform ships real scheduling, triggers, and write-action guardrails.

    If the requirement is pure workplace search across a huge, heterogeneous SaaS estate — and the budget clears six figures comfortably — Glean remains the reference standard, and this page would rather tell you that than win an argument. The full market picture, including Copilot, Guru, Onyx, GoSearch, Coveo, and Notion AI, is in our Glean alternatives comparison.

    FAQ

    Frequently asked questions

    The questions buyers actually ask in this evaluation.

    For a specific class of buyer, yes: organizations that want governed, cited answers over enterprise data — especially where AI agents need the same policy enforcement as people. Fluree approaches the problem as a knowledge graph rather than a search index, which is why it appears in Glean evaluations even though the architectures differ. For pure workplace search breadth, Glean's per-app indexing is deeper.

    Glean syncs access controls from each source system into its index and enforces them in real time — a mature, production-hardened model. Fluree enforces policy inside the data itself: authorization is evaluated at the entity, relationship, and property level as part of every retrieval, for any caller through any interface. The practical difference appears with AI agents and derived data — in Fluree, nothing sensitive enters embeddings or shared indexes in the first place, and every access is logged.

    Yes — that's the design center. Fluree publishes an MCP endpoint that any compatible client can consume (Claude, ChatGPT, Bedrock, or custom agents), and agents automatically inherit the same data-layer permissions as human users. There's no separate agent permission model to build or maintain.

    Glean publishes no price list — every deal is custom-quoted per seat, and third-party estimates consistently describe annual minimums around $50,000 with support fees and renewal increases. Fluree publishes its pricing: the hosted platform is free to start with usage-based fuel covering tokens, storage, and compute, and the core database is source-available and free to run.

    No. They do different jobs: Glean indexes your SaaS apps for workplace search; Fluree builds a governed knowledge graph that answers people and powers agents. Some organizations run a search suite for document findability while Fluree serves as the governed knowledge layer for analytics, AI agents, and cross-silo questions — the architectures are complementary where budgets allow.