WorkLLM
Vs
Glean
Glean is an enterprise search and Work AI platform built around a permissions-aware knowledge graph across 100+ connected apps, well suited to large enterprises with complex search and governance needs. WorkLLM is an AI workspace for companies that want everyday work, including prospecting, outreach, product development, research, hiring, content creation, client delivery, and more, to be run by AI agents without any technical expertise.
This page compares how WorkLLM and Glean work in practice so you can choose the right product for your company.
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WorkLLM vs Glean
What each product is best at
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Full Comparison - WorkLLM vs Glean
AI Chat
| Capability | WorkLLM | Glean |
|---|---|---|
| Multi‑LLM chat |
Access 200+ models including GPT, Claude, Gemini, Llama, Mistral, and more from a single chat interface, without separate subscriptions.
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Model Hub provides access to multiple leading LLMs, but not 200+ models, and model switching is oriented around admin configuration rather than an end-user, mid-thread comparison view.
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| Side‑by‑side model comparison |
Select up to 4 models, send the same prompt, and compare their answers together in one view.
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Does not provide a built‑in side‑by‑side comparison view for multiple different models in a single conversation.
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| Standard Q&A |
Available for everyday company workflows across sales, marketing, operations, hiring, and delivery.
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Available as part of the Glean Assistant experience.
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| Q&A with web search |
Search the web and get cited, up-to-date answers inside your workspace, grounded in sources and your organization memory.
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Search-enabled answers are available, grounded in Glean's enterprise knowledge graph across connected apps.
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| Deep research workflows |
Can do deep research using specialized models.
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Available through a dedicated Data Analysis and Research capability that coordinates multi-step research across connected company data.
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| Chat with documents |
Upload and chat with PDFs, Word files, spreadsheets, presentations, and more, grounded in projects and org memory.
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Document chat and analysis are available, grounded in your connected company tools and files across 100+ integrated systems.
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| Coding capabilities |
Coding models help with code generation and debugging in the chat interface only, not directly inside your codebase.
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Can index and search code repositories as a connected source, but is not positioned as a dedicated coding assistant.
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| Image capabilities |
Supports chatting with images as well as generating, editing, and modifying images for work content and workflows.
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Not currently positioned as a core capability.
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| Video & audio capabilities |
Chat with video and audio is supported for understanding, summarizing, and answering questions, but cannot directly modify or generate media.
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Not currently positioned as a core capability.
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| Chat with work apps |
Coming soon, with the ability to chat with work apps such as Gmail, Calendar, Drive, Slack, and more.
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Connects to 100+ enterprise apps and systems, including Slack, Salesforce, Jira, Confluence, and Google Workspace.
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| Shared threads |
Supports shared threads with sharing, co-prompting, commenting, and tagging so people can work with AI together.
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Not positioned as a multi-user thread collaboration layer; the assistant experience centers on individual search and chat sessions.
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Memory & Context
| Capability | WorkLLM | Glean |
|---|---|---|
| Personal memory | Available for individual user preferences and context. |
Available through Glean's Personal Graph, which adapts results and context to each individual user. |
| Thread memory | Maintains context within ongoing conversations. |
Maintains context within conversations. |
| Project memory | Available for shared project-specific knowledge and workflows. |
Not positioned as a dedicated project memory layer separate from the broader enterprise knowledge graph. |
| Organization memory | Built for shared company-level knowledge, including documents, links, brand guidelines, competitors, product context, and internal reference material. |
The Enterprise Graph is the core of the product, indexing documents, people, and activity across 100+ connected apps with permissions enforced. |
| Context memory | Supports structured context inside projects, such as product, customer, campaign, or business-specific context. |
Not positioned as a separate structured context layer. |
| Memory architecture | Designed around organization → project → context → thread memory. |
Built around a dual-graph model (Enterprise Graph and Personal Graph) rather than WorkLLM's organization → project → context → thread hierarchy. |
Team Collaboration
| Capability | WorkLLM | Glean |
|---|---|---|
| Multi-user AI threads | Built for teams to collaborate inside shared AI conversations. |
Not positioned as a multi-user thread collaboration layer. |
| Tagging and commenting | Built-in commenting and team discussion around AI outputs. |
Not positioned as a dedicated commenting workflow. |
| Team projects | Organize work around shared projects, customers, products, or initiatives. |
Not positioned as a team project workspace layer, though Glean Apps and Agents can be shared and reused across the organization. |
| Project contexts | Create specific context areas inside a project, such as a product, customer, campaign, or use case. |
Not positioned as a separate project-context structure. |
AI Agents
| Capability | WorkLLM | Glean |
|---|---|---|
| Ready-made agents |
Library of prebuilt agents for sales, marketing, HR, product, operations, and more so teams can start using AI on day one.
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An Agent Library provides prebuilt agents for common business functions, built on Glean's enterprise search and permissions layer.
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| Creating new agents for company tasks |
Create work agents in minutes by defining the job, inputs, outputs, and guardrails, without technical skills or prompt engineering.
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Glean Apps lets business users build custom AI apps and agents grounded in enterprise data without coding, inheriting existing permissions and governance.
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| Knowledge-based agents |
Knowledge agents answer questions using organization memory, documents, links, and FAQs so teams get consistent, up-to-date answers without pinging experts.
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A core strength; agents are grounded in the Enterprise Graph, so answers stay accurate and respect existing permissions across 100+ apps.
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| Task-based agents |
Coming soon – Task agents will run repeatable jobs like content, outreach, summaries, reports, and proposals using structured inputs and consistent outputs.
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Glean Agents can plan and execute multi-step tasks such as research, reporting, and content creation using coordinated sub-agents.
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| Workflow agents |
Coming soon – Planned workflow agents to run recurring processes across tools, such as daily summaries, handoffs, follow-ups, and status updates.
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Glean Actions let agents trigger tasks and workflows across 100+ connected systems, coordinated through Agent Orchestration.
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| Governance & ownership |
Agents have clear owner controls for private, team, or organization access, with versioning and consistent behavior across users.
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Dedicated Agent Governance tooling provides oversight, access control, and lifecycle management for agents built across the organization.
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Security & Governance
| Capability | WorkLLM | Glean |
|---|---|---|
| Tenant isolation |
Every customer gets a dedicated cloud or on-premise tenant, with data, embeddings, and access controls isolated at the infrastructure and application layer.
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Uses single-tenant connectors and a permissions-enforced architecture, with tenant isolation confirmed as part of its security program.
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| Deployment flexibility |
Supports managed cloud, private VPC, or fully on-premise deployment depending on compliance needs.
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No traditional on-premise deployment, but supports customer-controlled hosting choices and regional deployment across AMER, EMEA, and APAC.
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| Encryption at rest & in transit |
All customer data encrypted at rest and in transit using industry-standard protocols.
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AES-256 encryption at rest using a FIPS 140-2 validated crypto module, TLS 1.2+ in transit.
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| Role-based access control (RBAC) |
Granular permissions across users, assistants, agents, and integrations.
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RBAC is automatically synced with source systems, mirroring existing organizational permissions and hierarchies.
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| SSO / SAML authentication |
SAML-based SSO supported for enterprise deployments.
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Strong SSO support is built into the platform for enterprise deployments.
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| Audit logs & activity tracking |
All meaningful actions logged and available to workspace admins for compliance and investigations.
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Detailed audit logs are provided as part of the platform's compliance and security program.
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| Input & output guardrails |
Automatic redaction of sensitive data, prompt restrictions, and output policy enforcement built into the workspace.
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Glean Protect scans for sensitive content across connectors and can automatically remediate overshared content surfaced in AI responses.
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| No training on customer data |
Customer data is never used for model training; processed transiently for inference only.
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Zero-retention agreements with model providers ensure customer data is never stored or used to train models.
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| Data retention control |
Zero data retention at the LLM layer — requests to model providers are not retained.
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Regional data residency options across AMER, EMEA, and APAC let organizations control where data is stored and processed.
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| Admin dashboard & governance controls |
Central dashboard for integrations, sharing, usage visibility, and access revocation.
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A dedicated admin console provides governance and access control, backed by independently audited compliance certifications.
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Disclaimer: Information about Glean is based on publicly available documentation and product pages as of July 2026. Features and pricing may change over time.
WorkLLM vs Glean
When To Choose Which
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