Before diving into the GetGuru vs Glean comparison, consider Qatalog. Here's why it's the better choice for mid-market companies:
✅ Implementation in days, not months
✅ Free trial and $15/mo per user
✅ No need to maintain complex and expensive index infrastructure
✅ Superior security with access permissions that mirror your existing setup
✅ Seamless integrations with popular business tools and data warehouses
✅ Scalable as your business grows
Not convinced? Let's dive into the detailed comparison below.
What is the main difference between Glean and GetGuru?
The main difference between Glean and GetGuru is that Glean helps you find existing information across all your tools, while Guru helps you create and store new documentation in one place.
This is why some companies use both Guru to create their "source of truth" documentation and Glean to search across all tools, including their Guru content.
Is there a better solution than Guru and Glean?
If GetGuru or Glean Search don't solve your specific problems with valuable data, Qatalog’s no-ndex RAG addresses the limitations of traditional knowledge management tools. It’s ideal for real-time business analytics, sensitive data applications in regulated industries, complex data relationships management, and critical decision-making support.
The problem with traditional approaches
GetGuru's wiki-first limitations
-
Requires manual content creation and maintenance
-
Limited to stored knowledge, missing real-time data
-
Basic AI capabilities without deep data understanding
-
Risk of outdated information
-
Heavy maintenance burden on teams
Glean's index-heavy drawbacks
-
Creates vulnerable copies of sensitive data
-
Complex setup requiring weeks of indexing
-
Performance degrades as index grows
-
Permission drift between updates
-
AI hallucinations from mixed content
What if Glean or Guru aren’t enough?
Modern organizations choose Qatalog’s real-time AI over Glean and Guru for several reasons.
1. Superior architecture
- Direct connection to source systems
- No data copying or indexing required
- Real-time access to live data
- Maintains complete data context
2. Enterprise-grade security
- No additional attack surface
- Native permission inheritance
- Real-time compliance enforcement
- Single source of truth
3. Reliable AI performance
- Advanced RAG architecture
- No hallucinations from mixed content
- Preserves data relationships
- Consistent response quality
Related: Easy guide to what is RAG in search
4. Fast implementation
- Hours instead of weeks
- No complex setup required
- Immediate value delivery
- Seamless integration
Implementation and setup
Glean Search typically requires a longer implementation period of several weeks to months due to its comprehensive indexing process. You'll need dedicated IT resources to integrate with various data sources, configure permissions, and monitor the initial indexing phase. A successful Glean deployment might require:
-
A technical project manager
-
An IT admin for integrations
-
Security team involvement for compliance review
The indexing process involves creating secure copies of your organization's data across connected systems, which can take 2-4 weeks depending on data volume and complexity.
GetGuru, in contrast, offers a faster implementation pathway of days to weeks since it doesn't require extensive system integration or data indexing. The main setup effort focuses on migrating existing documentation and establishing your knowledge base structure. You'll primarily need:
-
A knowledge management lead
-
Content owners from relevant departments
Guru provides import tools for common formats (like Confluence, Google Docs) to streamline migration, though you'll need to plan time for organizing and structuring this content. While technically simpler to set up than Glean, Guru requires more upfront content organization and ongoing maintenance resources to keep information current.
Cost and ROI
In short, Glean pricing is an enterprise-focused, data-volume model versus Guru's more straightforward per-seat pricing.
Glean follows an enterprise pricing model that isn't publicly disclosed, requiring direct sales engagement. Their pricing is typically based on company size and data volume, with annual contracts being standard. Hidden costs may be significant—you'll need to factor in storage costs for indexed data, implementation services, and potential infrastructure upgrades to handle the indexing load. As your data volume grows, costs can increase substantially due to additional storage and processing requirements for the search index. Based on market feedback, organizations should expect annual commitments with average ocntracts in the five-figure range for enterprise deployments.
Guru offers more transparent pricing with a per-user subscription model, starting at $15-$18 per user per month depending on the plan. Implementation costs are generally lower since there's no complex indexing infrastructure needed. Guru offers enterprise plans as well as monthly options for smaller teams. The main consideration is maintaining an active user license for everyone who needs access to the knowledge base.
Security and compliance
In short, Glean's index-based approach requires creating and securing copies of data, while Guru's AWS-based storage model provides native isolation and security controls.
Glean creates security challenges by maintaining indexed copies of your company data, effectively creating a "shadow" database that requires its own security measures. While Glean emphasizes enterprise-grade search security, the fundamental architecture of storing indexed copies introduces additional attack surfaces and compliance considerations. The indexed data is stored in Glean's cloud infrastructure, and while they maintain SOC 2 compliance, organizations in regulated industries may face extra scrutiny due to this data duplication. A particular concern is "permission drift" — where access rights in the index may become out of sync with source systems between updates, potentially exposing sensitive information to unauthorized users.
Guru stores all customer content in logically isolated AWS databases, with each organization's data separated by unique team IDs to ensure multi-tenant isolation. Their access control capabilities include role-based access control (RBAC), granular permissions for content access, and SSO integration for enterprise identity management. Guru's AWS infrastructure provides strong isolation guarantees for sensitive data, while their permission system allows for restricted cards and collections with precisely controlled access.
Both platforms maintain standard enterprise compliance certifications.
Integrations
The core difference between Guru vs Glean lies in their integration philosophy: Glean tries to search across all your tools, while Guru focuses on embedding their knowledge base into your existing workflows.
Glean integrates with a broad spectrum of enterprise tools, creating searchable indexes across your tech stack. The frequency of index updates varies by data source, which may lead to inconsistencies in search results that don't reflect recent changes. Glean attempts to mirror source system permissions, but the complexity of maintaining synchronized access controls across indexed copies may lead to permission drift and potential security gaps.
Guru approaches integrations differently, focusing on embedding their knowledge base within your workflow. Their browser extension works across 150+ web applications, enabling users to access and create Guru content without switching contexts. They offer native integrations with popular tools. Permissions are more straightforward since Guru maintains its own access control system but they can inherit some permissions through SSO integration. A key advantage is their ability to embed knowledge cards directly into other applications, making information accessible where work happens.
Maintenance and ongoing management
The key difference in maintenance type is that Glean requires technical monitoring of index health, while GetGuru needs active content curation.
Glean enterprise search faces inherent challenges with index freshness that can impact reliability. When the index becomes outdated (which can happen between update cycles or due to syncing issues), users might see stale search results, missing recent content, or worse, accessing outdated information for critical decisions. The system requires regular monitoring to ensure proper indexing and sync status across all connected sources. While much of this is automated, organizations need to budget extra hours per month for a technical admin to:
-
Monitor index health
-
Troubleshoot sync issues
-
Manage access permissions that may drift between updates
Guru requires more proactive content maintenance but offers better control over information accuracy. The platform includes features to help automate maintenance, like:
-
Verification reminders
-
Content expiration dates
-
AI suggestions for outdated content
However, organizations need to establish clear ownership and review cycles for knowledge base content. A mid-sized company should budget time for a knowledge manager to coordinate content reviews, archive outdated information, and properly document new knowledge. Guru's AI Training Center helps identify knowledge gaps and improve content quality, but the human oversight needed for content accuracy remains higher than Glean's more automated approach.
User experience
When comparing Glean vs Guru user experience, you may notice Glean offers a familiar search-first approach but with varying accuracy, while Guru provides contextual knowledge delivery with more consistent but curated results.
Glean operates as a universal search layer, where users can find information through a simple search interface similar to Google. Users primarily interact through a web portal or browser extension, typing queries to search across connected tools. The learning curve is relatively low since most users are familiar with search interfaces, but they need to learn Glean's search operators and filters for better results. Glean integrates into existing workflows through its browser extension and Slack integration, allowing users to search without switching contexts. While Glean uses AI to understand context, results may be outdated due to indexing delays, and the system may sometimes struggle with complex queries or industry-specific terminology.
In contrast, GetGuru takes a more structured approach to knowledge access through their browser extension, which proactively suggests relevant information based on the page or tool you're using. Users can access, create, and share knowledge cards directly within their workflow. The learning curve is slightly steeper as users need to understand Guru's card-based system and knowledge organization, but the contextual suggestions often mean users find information without actively searching. Result accuracy tends to be higher since the content is manually curated and verified, though this depends on how well teams maintain their knowledge base. Guru's AI assists with content creation and maintenance, helping ensure information stays relevant.
AI capabilities
While Glean offers broader AI-powered search but with higher hallucination risks, GetGuru provides basic AI features with better reliability.
Glean uses AI to process search queries and synthesize answers from indexed content, similar to an internal ChatGPT but with company data. Glean AI attempts to understand the context by considering the user's role, recent searches, and commonly accessed content. However, since Glean works with indexed copies of data that may mix different content types and contexts, their AI can sometimes produce inconsistent or hallucinated responses, especially when dealing with technical or domain-specific information.
Guru takes a more controlled approach to AI knowledge management system with their "Assist" feature. It focuses on content enhancement rather than open-ended question answering. Their AI helps with writing and maintaining knowledge cards through features like summarization, tone adjustment, and translation into multiple languages. Since Guru's AI works with structured, verified content within their knowledge base, they have better control over hallucinations.
Scale and growth
In short, Glean faces performance and maintenance challenges with growing data volumes, while Guru's main scaling hurdle is knowledge organization rather than technical limitations.
Glean's scalability challenges may become more apparent as data volumes grow. While they serve enterprise customers, performance can degrade as the search index expands, affecting search speed and result accuracy. Large organizations may report longer indexing times and increased latency when searching across massive data sets. The system requires additional computing resources and storage as your data grows, which can impact costs and maintenance needs. Performance particularly becomes a concern when indexing across multiple large data sources (like extensive SharePoint libraries or years of Slack history).
Guru scales more predictably since it uses a structured AWS database architecture with dedicated customer instances. Their card-based system handles large content volumes efficiently, and performance remains consistent with extensive knowledge bases if the content is logically organized. The primary scaling consideration isn't organizational — companies need stronger governance and organization structures to maintain knowledge base clarity as content grows.