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Report: Is Glean the Best Platform for Company-Wide Agent Building in 2025?

23 min read
11/22/2025
Regenerate

Overview

This report investigates whether Glean is "the best" option for company-wide AI agent building over enterprise knowledge in 2025, compared with major alternatives such as Microsoft Copilot for Microsoft 365, ChatGPT Enterprise / OpenAI enterprise offerings, and Google’s Gemini / AgentSpace for Workspace and Cloud.

The analysis is based on vendor marketing claims, independent reviews, technical deep dives, and comparative evaluations. It focuses on:

  • Breadth and depth of enterprise data integration
  • Agent-building capabilities (no-code/low-code, orchestration, workflow automation)
  • Search and retrieval quality (RAG, hybrid search, knowledge graph)
  • Security, governance, and compliance posture
  • Cost and fit for org-wide rollout

"Best" is context-dependent. Rather than a simple winner, this report highlights where Glean is a leader, where it lags, and when a different platform is a better fit.

Should we standardize on Glean or Microsoft Copilot for enterprise AI in 2025?

1. What Glean Actually Is in 2025

1.1 Core positioning

Evidence consistently describes Glean as an enterprise AI work platform whose foundation is AI-powered enterprise search and a knowledge graph, now extended with Glean Agents:

  • Glean is framed as a “Work AI platform and workplace search tool” that connects to 100+ business apps (Google Workspace, Microsoft 365, Slack, Salesforce, Notion, Confluence, Databricks, etc.) and unifies company knowledge into a single search and assistant layer.1
  • It has evolved from “Google for work” style enterprise search into a platform that powers search, assistant, and agents over an Enterprise Graph linking people, data, and processes.2
  • Glean positions its agentic capabilities as production-grade agents that don’t just talk, but actually do work, backed by an actions library spanning thousands of enterprise tasks.3

Independent comparisons still describe Glean primarily as an AI-infused enterprise search tool that creates a centralized knowledge discovery experience and answers employee questions via search and basic AI, with agents layered on top.4

1.2 Agent capabilities

From Glean’s own materials and third-party coverage:

  • Glean Agents are marketed as enterprise AI agents for multi-step workflows (e.g., onboarding flows, ticket triage, sales research, report generation) powered by the same Enterprise Graph and search infrastructure.56
  • Agents can be built using a natural-language builder / no-code interface, allowing non-technical employees to describe what they want the agent to do; the builder then turns that into a workflow.7
  • Glean emphasizes agentic reasoning (multi-step tasks, tool calling, observe–learn–improve loops) and natural language runbooks, claiming that workflows get faster and smarter over time.86
  • The platform is integrated with SDKs and MCP (Model Context Protocol) support, letting Glean act as a context and tools hub for external LLMs and agents.9

However, several independent analyses push back on how advanced this really is:

  • One review notes that Glean “excels at AI-enhanced search but has limited automation features”, contrasting it with more automation-centric competitors.10
  • Another calls Glean’s agents mainly “basic ‘if‑then’ automation” lacking true memory, inter-agent communication, and complex multi-step process handling; it describes them as simple workflows vs. deep agentic systems.11
  • A separate comparison states Glean is moving toward automation but that “full automation is still in its early stages,” with the CEO himself acknowledging enterprise AI is error prone, unpredictable, and hard to implement.12

1.3 Where Glean is genuinely strong

Across multiple sources, Glean is regarded as best-in-class or near it in a few specific dimensions:

  • Search quality and relevance: Users and reviewers frequently praise Glean’s search accuracy; one detailed comparison reports that “Glean’s search results are top‑notch” in surfacing the right information.13
  • Hybrid retrieval and knowledge graph: Glean combines vector and lexical search with a knowledge graph (and now a “personal graph”) to understand relationships between people, content, and processes, improving contextual relevance and grounding for agents.1415
  • Coverage across tools: It ships with connectors for 100+ apps, giving broad coverage across SaaS ecosystems beyond just Microsoft or Google’s native stacks.1617
  • Security and governance: Glean has a strong security posture—permissions-aware search, mirroring source system ACLs, plus documented emphasis on AI security, prompt-injection defenses, and compliance-oriented governance.1819
  • Real enterprise deployments: Case studies include large organizations like Koch Industries reportedly replacing Microsoft Copilot and ChatGPT Enterprise with Glean and indexing over 1 billion objects in seven weeks.20

These strengths matter a lot if your primary problem is “how do we build agents that actually know our company’s data” rather than just generic LLM reasoning.

When to choose Glean over ChatGPT Enterprise for enterprise knowledge?

2. Limitations and Criticisms of Glean for Company-Wide Agents

2.1 Agents maturity vs. marketing

Several independent voices challenge the idea that Glean is already a best-in-class agent platform:

  • A comparative review argues that Glean leaves enterprises wanting for meaningful automation, stating that it’s excellent at information discovery but not yet a fully mature automation layer.10
  • A separate piece notes that Glean agents “only provide basic ‘if, then’ automation” and cannot yet maintain memory or handle complex multi-step processes the way more advanced agent platforms do.11
  • Comparisons of “agent frameworks” highlight that real-world AI agents fail frequently in production and require significant observability, guardrails, and monitoring—areas where Glean is not uniquely differentiated versus specialized tools (AgentOps, Langfuse, etc.).[^hannecke-failures][^adnanmasood-observability]

In other words: Glean is much more mature as an enterprise search/knowledge platform than as a generic, autonomous agent framework. Its agents are currently strongest when they’re tightly coupled to search and known workflows, not when you need open-ended agent systems.

2.2 Cost and commercial model

On pricing, Glean is consistently characterized as a premium, enterprise-only product:

  • Glean does not publish public list pricing; you have to go through sales.21
  • Industry analyses report per‑user, per‑month pricing typically around $45–50+, with a minimum of ~100 users and minimum ACV in the ~$50–60k range.2223
  • Reviews highlight that premium enterprise pricing may be prohibitive for smaller organizations or those with limited budgets.24

For true company-wide deployment (thousands of employees), this can be substantially more expensive than:

  • Microsoft Copilot’s flat $30/user/month add-on (on top of existing M365 licensing) in many scenarios.25
  • Or ChatGPT Team/Business tiers in organizations that don’t need full Enterprise-level contracts.

2.3 Security risk trade-offs

While Glean emphasizes security and permission mirroring, there are also third-party cautions:

  • A security analysis notes that if sensitive files are broadly or publicly shared in your SaaS tools, Glean can make them easier to find, effectively amplifying existing data exposure and insider threat risks by surfacing misconfigured content.26
  • Other commentary points out that enterprise AI generally introduces increased risk across data, apps, and processes, and Glean’s own content highlights the need for rigorous accuracy verification and governance to avoid compliance incidents.2728

These are not unique to Glean, but they undercut any claim that its agent layer is “safest by default” without strong internal governance.

2.4 Breadth of use-cases

Glean’s agents are explicitly oriented toward knowledge-work and office workflows:

  • Reviews of Glean for SMBs describe its agents as designed for general office work rather than highly specialized operational or industry-specific use cases.29
  • Comparisons with more vertical- or workflow-focused tools (Ask-AI, Thunaï, Unleash, Moveworks, etc.) argue that Glean’s sweet spot is knowledge discovery and light automation, while others are more opinionated about support, GTM, or vertical workflows.3031

If you need deep domain-specific agent behavior (e.g., code agents over internal repos, manufacturing control workflows, complex financial operations), you may get more leverage from combinations of OpenAI / Azure OpenAI / Vertex AI and custom frameworks or from vertical tools.

What are the limitations of Glean Agents for complex workflows?

3. Comparison: Glean vs Microsoft Copilot for Company-Wide Agents

3.1 Positioning

  • Glean: Cross‑stack enterprise search + assistant + agents over a unified Enterprise Graph. Primary strength is connecting 100+ apps and providing high-quality semantic search and RAG over all of them.162
  • Microsoft Copilot for Microsoft 365 (plus Copilot Studio): Deeply integrated assistant and agent-building platform inside the Microsoft ecosystem (Word, Excel, Outlook, Teams, SharePoint, OneDrive, Dynamics 365, etc.). Microsoft’s semantic index and Graph connectors give first-class access to Microsoft data, with extensibility via Copilot Studio and Azure AI.3233

Glean can coexist with Copilot: Glean has dedicated Microsoft integration, including connectors for SharePoint, OneDrive, Outlook, Teams, Dynamics 365, and the ability to run on Azure and integrate with “Agent 365.”34

3.2 Strengths of Glean vs Copilot

Where Glean often wins in comparisons:

  • Cross-vendor coverage: Glean is designed to unify Microsoft 365, Google Workspace, Slack, Salesforce, Jira, Confluence, GitHub, Databricks, and many others into a single knowledge layer.1316 Copilot is best if you’re all‑in on Microsoft; external sources require extra configuration and are more limited.
  • Search-first design: Glean’s entire stack is optimized around semantic enterprise search and RAG; reviewers repeatedly cite its search quality as a differentiator.35
  • Agent builder for non‑technical users: Glean offers a natural-language agent builder targeting business users; Copilot Studio is powerful but more oriented toward pro/low-code builders and the Power Platform ecosystem.733

3.3 Strengths of Copilot vs Glean

Where Copilot tends to be a better fit:

  • Microsoft-first orgs: If your collaboration, documents, and workflows are already 90%+ in Microsoft 365, Copilot has unmatched in-app integration (inline assistance in Word/Excel/PowerPoint/Outlook/Teams) and is often the default agent layer.36
  • Licensing and rollout: Copilot pricing is a flat $30/user/month add-on (with prerequisite M365 licenses). For large orgs already paying for E3/E5/Business plans, this can be operationally simpler and sometimes cheaper than Glean’s custom, higher per-user pricing.2537
  • Development ecosystem: Copilot Studio + Azure AI + Graph connectors provide a broad platform for building agents and plugins that can run across M365 and custom apps; this is attractive if your dev teams are already on Azure.

3.4 Takeaway vs. “best” claim

For Microsoft-centric enterprises, Copilot + Copilot Studio is usually the first-line agent platform, while Glean can be the cross-app knowledge and search backbone, and a second agent layer when you need multi‑SaaS, permission-aware search.

Claiming Glean is the best for company-wide agents is not well-supported here; it’s a leading choice when heterogeneous SaaS search is the core problem, but not a clear winner over Copilot for Microsoft-heavy environments.

Will Glean replace Microsoft Copilot in Microsoft‑centric enterprises?

4. Comparison: Glean vs ChatGPT Enterprise / OpenAI Enterprise

4.1 Different layers in the stack

  • Glean: An application/platform that sits on top of your SaaS data, with its own search index, RAG stack, knowledge graph, and agent layer.
  • ChatGPT Enterprise / OpenAI: Primarily LLM and AI capability provider (chat UI, APIs, RAG capabilities, agents) that you can embed into your own apps. ChatGPT Enterprise brings native RAG, persistent memory, huge context windows, and enterprise-grade security to a generic chat/productivity interface.38

In practice, many enterprises use Glean + OpenAI together: Glean as the retrieval and permissions layer; OpenAI as the reasoning/generation engine.

4.2 Where Glean is stronger

  • Enterprise data integration: Glean ships with 100+ prebuilt connectors and a mature permissions-aware graph; building the same breadth of connectors and ACL mirroring yourself on top of pure OpenAI APIs is non-trivial.1618
  • Out-of-the-box search experience: Glean is plug‑and‑play for enterprise search; ChatGPT Enterprise is not a turnkey enterprise search product, though its RAG and file tooling can be used to build one.4[^workativ-enterprise-search]
  • Compliance posture specific to enterprise search: Glean is positioned with TX-RAMP Level 2 and ISO 27001 for its enterprise search offering.39

4.3 Where OpenAI Enterprise is stronger

  • Raw model capability and context windows: ChatGPT Enterprise exposes OpenAI’s latest models, very large context windows, and native RAG, plus agents and tools that are not constrained to enterprise search scenarios.38
  • Breadth of use cases beyond knowledge work: OpenAI’s stack underpins code assistants, creative generation, complex analytics, custom agents—much broader than Glean’s enterprise knowledge focus.40
  • Flexible deployment patterns: Through Azure AI Foundry and enterprise agreements, OpenAI models can be hosted in environments with strong regulatory and residency guarantees, and deeply embedded into custom line-of-business systems.41

4.4 Cost and contracts

  • Glean: Per‑user, per‑month with high minimums—often cited as ~$45–50/user/month and minimum ACV ~$60k.2223
  • ChatGPT Enterprise: Typically volume-based enterprise contracts; public benchmarks show example organizations paying from the tens of thousands to millions annually, but with flexibility in API vs seat-based models.4243

Neither is obviously “cheaper” at full enterprise scale; OpenAI is more usage/volume-flexible, while Glean is more license/seat oriented.

4.5 Takeaway vs. “best” claim

If your priority is company-wide agents that truly understand and respect enterprise permissions across 100+ SaaS tools, Glean is compelling. If your priority is maximum model power, custom agent architectures, and broad use-cases (code, creative, analytics), then OpenAI Enterprise with a custom retrieval/indexing stack is more flexible.

Glean is not obviously superior to OpenAI’s enterprise offerings for all-agent scenarios; it’s strongest when you explicitly want “enterprise search + work assistant + scoped agents” as a product rather than a toolkit.

Should we build on OpenAI ourselves or buy Glean for enterprise AI?

5. Comparison: Glean vs Google Gemini / Agentspace / Gemini Enterprise

5.1 Google’s agent stack

Google’s story spans Gemini for Workspace and Google AgentSpace (now part of Gemini Enterprise):

  • Gemini for Google Workspace adds assistants into Gmail, Docs, Sheets, Meet, etc., similar to Copilot in M365.
  • AgentSpace / Gemini Enterprise is an “AI agent hub” built on Google Cloud with pre-built agents, custom agent designer, and multimodal Gemini models, aimed at orchestrating enterprise workflows and cross-vendor agent collaboration.4445

AgentSpace is built around connectors and actions plus custom agents that use Gemini-based reasoning.4647

5.2 Glean vs AgentSpace capabilities

Independent comparisons portray the two as complementary, not direct substitutes:

  • Glean “excels at information discovery but leaves enterprises wanting for meaningful automation”; AgentSpace promises sophisticated agent workflows but lacks Glean’s robust data integration and search capabilities.10
  • AgentSpace focuses on autonomous workflows with multiple collaborating agents (e.g., sourcing, scheduling, reference-checking agents) orchestrated across enterprise data sources.[^credal-vs-agentspace-usecases][^premiercloud-agents]
  • Glean focuses on indexed search + permission-aware RAG + simple-to-moderate agents embedded in knowledge workflows.

5.3 Ecosystem and security

  • AgentSpace / Gemini Enterprise runs natively on Google Cloud, inheriting Google’s compliance portfolio (SOC 2, HIPAA-eligible services, ISO standards, etc.) and strong encryption, IAM, and key management (including Cloud HSM).[^^gcp-soc2]48
  • Glean uses Google Cloud (BigQuery, Google AI) under the hood but operates as a distinct SaaS, offering SOC 2, ISO 27001, TX‑RAMP Level 2 for its Work AI platform.4939
  • Agentspace is primarily optimized for Google’s own stack; third-party connectors can require more configuration and often rely on external vendors for maintenance.5051

5.4 Pricing

  • Glean: Custom quotes, per‑user/month, high minimum ACV (~$50–60k) and minimum seat counts (~100 users). Often reported as ~$45–50 per user per month.22[^unleash-glean-price]
  • Gemini Enterprise / AgentSpace: Pricing is still evolving and tied into broader Google Cloud and Workspace licensing. Public commentary describes tiered enterprise editions and usage-based charges over GCP, often more granular but requiring careful forecasting of cloud spend.5253

5.5 Takeaway vs. “best” claim

If you are a Google-first organization (Workspace, BigQuery, Vertex AI, GKE), Gemini + AgentSpace may be the natural agent fabric, with Glean filling a niche around cross-SaaS search and knowledge discovery.

If your environment is multi-stack and you want a vendor-neutral “Switzerland” of enterprise data, Glean’s Enterprise Graph and connectors are a major differentiator.54

Again, nothing in the evidence shows Glean categorically outperforming Google’s agent stack for all org-wide agent scenarios.

Should Google Workspace shops standardize on Glean or Gemini Enterprise?

6. Accuracy, Risk, and Real-World Adoption

6.1 AI accuracy and failure rates

Glean’s own thought leadership emphasizes that enterprise AI value is bottlenecked by accuracy:

  • Their materials stress that ensuring AI accuracy with rigorous verification and quality control is critical, citing broader industry data where a majority of AI projects fail to reach production or fail to deliver business outcomes.2855
  • External analyses show high failure rates for general-purpose AI agents (e.g., 90%+ of office tasks failing in some benchmarks), underlining that agent reliability is an ecosystem and process problem, not something any single vendor has fully solved.56

Glean does not escape this reality: its agents ride atop the same class of LLMs and RAG techniques used by others. Its advantage is better grounding and permissions-aware retrieval, not perfect reasoning.

6.2 Where Glean is winning today

Signals of traction and recognition:

  • Glean is recognized by Gartner as an emerging leader in generative AI knowledge management, signaling strong performance in relevance, scale, and business impact.57
  • It is cited as a top AI app by enterprise AI spend, alongside OpenAI API, Microsoft Copilot, and ChatGPT.58
  • Investors and analysts highlight Glean as a category leader in enterprise search and knowledge, with strong product-market fit in mid-market and large tech companies.5960

But these refer primarily to search and Work AI, not necessarily to Glean being the dominant platform for sophisticated, cross-domain autonomous agents.

7. Compliance Considerations

Your organization has a stated requirement that vendors must meet certain compliance standards (though the provided list is not sufficiently specific to map to named frameworks like SOC 2, ISO 27001, HIPAA, etc.). Within the limits of available information:

  • Glean publicly asserts and is reported as:
    • ISO 27001 certified and TX‑RAMP Level 2 certified, with strong data governance and permissions-aware security for AI.3918
    • Operating on Google Cloud with encryption at rest and in transit and support for customer-controlled KMS where applicable.49
  • Microsoft Copilot inherits Microsoft 365’s extensive compliance portfolio (including SOC, ISO, HIPAA-eligible services) and enterprise-grade security controls.61
  • OpenAI / ChatGPT Enterprise provide enterprise-grade security, compliance, and privacy features, including encryption, data isolation, and no training on customer data by default; specific certifications depend on deployment (e.g., via Azure OpenAI).41
  • Gemini Enterprise / AgentSpace inherit Google Cloud’s compliance portfolio (SOC 2, HIPAA-eligible services, ISO standards, etc.).6263

Because the specific compliance label “Hi” is not a recognized standard, no vendor can be definitively marked as compliant or non-compliant against that term alone. If “Hi” is a placeholder for an internal framework, you would need to:

  1. Map that internal framework to external standards (e.g., SOC 2, ISO 27001, TX‑RAMP, HIPAA, GDPR).
  2. Compare those requirements with each vendor’s trust / security / compliance documentation.

In absence of that mapping, there is no clear evidence that Glean violates or fails your named requirement, but you also cannot treat it as certified-compliant to “Hi” without an internal control assessment.

⚠️ Compliance Alert (conditional): Based on public information, Glean appears to meet mainstream enterprise security/compliance expectations (e.g., ISO 27001, TX‑RAMP Level 2). However, because your requirement is expressed as an undefined standard (“Hi”), no vendor—including Glean—can be confirmed as fully compliant without mapping “Hi” to concrete external frameworks and conducting a detailed vendor security review.

8. Bottom Line: Is Glean “the Best” for Company-Wide Agent Building in 2025?

8.1 What the evidence supports

The research supports these points with high confidence:

  1. Glean is a top-tier enterprise search and Work AI platform with a strong knowledge graph, hybrid RAG, and high-quality, permissions-aware indexing across 100+ apps.
  2. Glean’s agents are real and maturing, but much of its strength today is still in search + assistant, not in fully general-purpose, autonomous agents across all possible enterprise workloads.
  3. For multi-SaaS environments, where your main challenge is “let everyone in the company talk to and act on our knowledge safely”, Glean is one of the leading commercial options, and in that slice it may be “best” for some organizations.
  4. In Microsoft- or Google-first environments, Copilot (M365) or Gemini/AgentSpace are at least as strong as agent fabrics, and often cheaper and more deeply integrated in-office tools.
  5. OpenAI / ChatGPT Enterprise are stronger as general-purpose agent and LLM infrastructure than as turnkey enterprise search/knowledge platforms. Many serious deployments pair OpenAI with a search/knowledge layer like Glean or build their own retrieval stack.

8.2 When Glean could be your best choice

Glean is plausibly your best platform for company-wide agents if:

  • Your data and workflows are spread across many SaaS tools (Microsoft + Google + Slack + Salesforce + GitHub + Jira + Confluence + Databricks, etc.).
  • Your primary need is permission-aware, high-accuracy enterprise search and Q&A, with agents tightly coupled to that knowledge.
  • You can afford a premium, high-ACV, per-seat product and want a productized solution rather than building a large custom stack on raw LLM APIs.

8.3 When Glean is not the best choice

Glean is likely not the best fit as your primary agent platform if:

  • You are heavily Microsoft or Google native and mostly need in‑suite assistance (Copilot or Gemini will be more native, often cheaper, and more ergonomically integrated).
  • You need deeply specialized or domain-specific autonomous agents (e.g., complex devops, manufacturing, trading systems), where building directly on OpenAI / Azure AI / Vertex AI with custom frameworks or using specialized platforms (Unleash, Moveworks, etc.) may deliver more control.
  • You are cost-sensitive or have fewer than ~100 knowledge workers; Glean’s minimums and per-user pricing may not make sense.

8.4 Practical decision guidance

To decide if Glean is “best” for your context, you should:

  1. Map your estate: quantify how much of your work happens in Microsoft 365, Google Workspace, and which SaaS tools.
  2. Rank priorities: is your #1 need search + knowledge access, or agentic workflow automation, or broad LLM capabilities (code, analytics, creative)?
  3. Run focused pilots:
    • Pilot Glean in 1–2 departments with heavy cross-tool work (e.g., engineering + support), measuring search success rate, time-to-answer, and agent-driven automation.
    • In parallel, pilot Copilot, Gemini, or ChatGPT Enterprise in similar teams with targeted workflows.
  4. Evaluate TCO and governance: compare per-user cost, data residency, compliance fit, and internal ops effort to manage each platform long-term.

Only after those steps can you credibly say Glean is “the best” for your company-wide agent strategy in 2025. The open web evidence alone does not justify a blanket statement that Glean is universally the best agent platform for all enterprises this year.

How should we evaluate enterprise AI agent platforms for a 2025 rollout?

Footnotes

  1. Glean described as a Work AI platform and workplace search tool that connects to tools like Google Drive, Slack, and Notion. ↩

  2. Glean’s Enterprise Graph maps relationships between data, people, and processes for contextual search and AI. ↩ ↩2

  3. Coverage of Glean enabling creation of production-grade agents with an extensive integrations and actions library. ↩

  4. Unleash comparison describing Glean as an AI-infused enterprise search tool focused on centralized knowledge discovery. ↩ ↩2

  5. Glean blog on enterprise AI agents and how they automate multi-step workflows. ↩

  6. phData article on creating a “company brain” with Glean, highlighting agentic systems orchestrating multi-step tasks. ↩ ↩2

  7. Reporting on Glean’s natural-language agent builder and agent-building UX. ↩ ↩2

  8. Glean’s own blog describing enterprise AI agents and agentic reasoning capabilities. ↩

  9. Article noting Glean’s SDKs and MCP (Model Context Protocol) support. ↩

  10. Credal comparison: “Glean excels at AI-enhanced search but has limited automation features,” contrasted with Agentspace. ↩ ↩2 ↩3

  11. Credal article critiquing Glean agents as basic if-then automations without memory or multi-step orchestration. ↩ ↩2

  12. Capacity blog citing Glean’s automation as still early-stage and CEO comments on enterprise AI being error-prone and hard to implement. ↩

  13. LinkedIn comparison highlighting user praise that “Glean’s search results are top-notch.” ↩ ↩2

  14. Glean article explaining its hybrid vector + lexical search with knowledge graph framework for more accurate, contextual results. ↩

  15. Glean blog describing expansion of its knowledge graph with a personal graph to better understand work context. ↩

  16. Glean content and independent lists citing 100+ connectors across business apps. ↩ ↩2 ↩3 ↩4

  17. Glean blog listing connectors across 100+ apps for generative AI tools. ↩

  18. Glean perspective on security and permissions-aware AI, including preventing prompt injection and data leakage. ↩ ↩2 ↩3

  19. NimbleGravity blog explaining Glean’s strict permission mirroring. ↩

  20. Futurum report on Koch Industries implementing Glean as a replacement for Copilot and ChatGPT Enterprise, indexing over 1B objects in seven weeks. ↩

  21. ClickUp blog noting Glean doesn’t publish public pricing. ↩

  22. GoSearch article explaining Glean enterprise search pricing, per-user and minimum ACV. ↩ ↩2 ↩3

  23. eesel blog giving Glean pricing estimates and minimum contracts. ↩ ↩2

  24. Siit.io review highlighting Glean’s extensive integrations and premium pricing. ↩

  25. Microsoft and third-party pricing explanations of Copilot at $30/user/month add-on. ↩ ↩2

  26. DoControl blog outlining security risks when Glean indexes overshared files. ↩

  27. Glean perspectives and third-party analyses on AI risk categories. ↩

  28. Glean perspective on ensuring AI accuracy and common pitfalls. ↩ ↩2

  29. eesel comparison describing Glean agents as primarily aimed at general office work. ↩

  30. Ask-AI article positioning itself as a more workflow-centric alternative to Glean. ↩

  31. Thunaï blog highlighting Glean’s focus on finding information vs deeper task automation. ↩

  32. Microsoft docs on semantic index for Copilot. ↩

  33. Microsoft and partner content describing Copilot Studio and its extensibility. ↩ ↩2

  34. Glean press/blog on integrating with Microsoft Agent 365, Azure, Dynamics, and expanded connectors. ↩

  35. Glean blog on semantic search and productivity. ↩

  36. Tidalwave Research newsletter explaining Copilot’s deep embedding into Microsoft 365 apps. ↩

  37. eesel blog on Copilot pricing and prerequisites. ↩

  38. Credal comparison describing ChatGPT Enterprise as a beefed-up chat interface with unlimited access to latest models and native RAG. ↩ ↩2

  39. Reworked article and Glean/third-party sources citing TX‑RAMP Level 2 and ISO 27001 for Glean. ↩ ↩2 ↩3

  40. OpenAI business page listing enterprise use-cases. ↩

  41. Xenoss comparison of OpenAI vs Anthropic vs Gemini enterprise platforms. ↩ ↩2

  42. Exploding Topics article showing ChatGPT Enterprise pricing examples by org size. ↩

  43. Redress Compliance post on OpenAI pricing models. ↩

  44. Unleash comparison introducing Google AgentSpace as an AI agent hub. ↩

  45. Google Cloud blog on bringing AI agents to enterprises with AgentSpace. ↩

  46. Google Cloud and partner posts describing AgentSpace connectors and actions. ↩

  47. Google and partner blogs explaining AI agents and custom agents in AgentSpace. ↩

  48. Google docs on encryption and KMS. ↩

  49. Google Cloud customer story on Glean using BigQuery and Google AI. ↩ ↩2

  50. Credal article noting Agentspace is primarily compatible with Google’s stack with third-party connectors via external vendors. ↩

  51. Cloudbabble article on Agentspace connectors and configuration. ↩

  52. Gemini Enterprise editions documentation. ↩

  53. Sparkco blog on AI agent platform pricing in 2025. ↩

  54. Geodesic Capital commentary calling Glean a “Switzerland” of enterprise data. ↩

  55. Glean and industry references on high AI project failure rates. ↩

  56. Gaper blog on high failure rates of AI agents with GPT-4o and Llama. ↩

  57. Glean blog referencing Gartner Innovation Guide recognition for gen AI knowledge management. ↩

  58. Zylo analysis listing Glean among top AI apps by spend. ↩

  59. Sacra research on Glean’s product-market fit as enterprise search. ↩

  60. Adams Street Partners explanation of why they invested in Glean. ↩

  61. VarsityTech comparison of AI security features, naming Watson, Azure AI, and ChatGPT for different sensitivity levels. ↩

  62. Google Cloud SOC 2 compliance documentation. ↩

  63. Google Cloud HIPAA compliance documentation. ↩

Explore Further

Report: Is Glean the Best Platform for Company-Wide Agent Building in 2025? | VendorTruth