Top Glean Alternatives (2026 Guide)

Here's something that's quietly draining your organization's potential: How your employees access knowledge. When critical information is scattered across dozens of systems, when finding a document takes longer than creating a new one, when institutional knowledge walks out the door with departing employees, you're not just losing time, you're losing competitive ground.

Your employees spend hours weekly searching for information that should take seconds. They're messaging colleagues asking, "Where's that document again?" sitting through avoidable meetings, and losing momentum. New hires take months to get up to speed because tribal knowledge lives in people's heads, not searchable systems. Meanwhile, your competitors are executing.

So, if you're exploring AI-powered enterprise search platforms, Glean has likely come up on your radar. Founded by former Google engineers with strong VC backing, they've built a reputation in enterprise search, especially among tech companies. 

But choosing the right platform isn't about the best origin story or biggest funding. You need to consider what actually works in your environment. 

• Integration depth: Does it connect to everything, not just Slack and Google Drive, but your 2012 ERP system, industry-specific CRM, on-premise file shares, and legacy databases?
• Governance and security: Will it respect data permissions and compliance requirements at scale, or create security headaches?
• Customization: Can you shape it to fit your workflows and terminology, or will you spend years changing ingrained habits to match the product?
• Scalability and cost: What does pricing look like in years two and three? How does it perform scaling from 200 to 5,000 users?

While most enterprise software looks impressive in demos, what matters is performance in the messy reality of enterprise IT. Organizations should evaluate thoroughly, ask uncomfortable questions, and choose platforms genuinely engineered for enterprise complexity, not just designed to win pilots.

In this guide, we're breaking down the top Glean alternatives with the lens that matters: deep integration capabilities, real customization, enterprise-grade security and governance, proven scalability, and honest total cost of ownership. 

What is Glean? 

Glean is an AI-powered enterprise search and knowledge management platform founded in 2019 by former Google search engineers. The company has raised substantial funding, including a recent Series F round that values the company at $7.2 billion. Glean positions itself as a "Work AI" platform that connects to enterprise data sources to provide intelligent search, AI-powered answers, and agentic capabilities.

What are the key features of Glean?

Glean markets itself as a comprehensive platform for enterprise search and AI assistance. The key features of the platform include:

1. Enterprise Graph & Knowledge Graph: Glean's proprietary technology that understands relationships between people, content, and context across the organization to deliver personalized, relevant results.
2. Glean Search: Hybrid search combining semantic and lexical search capabilities, trained on company-specific data to understand internal terminology, acronyms, and domain-specific language.
3. Glean Assistant: An AI-powered personal assistant that provides answers, generates content, and helps with tasks across different workflows by drawing on enterprise knowledge.
4. Glean Agents: A platform for building and orchestrating AI agents using natural language prompts, with support for third-party frameworks like LangChain and OpenAI SDK.
5. Connectors: Pre-built integrations with enterprise applications, including Google Workspace, Microsoft 365, Slack, Salesforce, Jira, ServiceNow, and more.
6. Permissions-aware search: Respects existing permissions from source applications, ensuring users only access data they're authorized to see.
7. Model Hub: Flexibility to choose from leading LLMs hosted on Amazon Bedrock, Azure OpenAI, Vertex AI, and OpenAI.
8. Glean Protect: Security and governance capabilities including prompt injection protection, data exposure detection, and compliance with SOC 2 Type II, GDPR, HIPAA, and ISO 27001.

Why look for Glean alternatives?

While Glean has established itself in the enterprise search market, it's not always the right fit for every organization. Here are key considerations based on independent analysis and market evaluation:

1. Narrower connector set compared to competitors

Glean's connector set is narrower than many of the other platforms, and the platform offers fewer capabilities for enriching indexed data with further context. For organizations with complex data ecosystems that extend beyond common SaaS applications, including legacy systems, industry-specific platforms, on-premise repositories, and proprietary databases, this limitation could create integration gaps.

If your enterprise requires comprehensive connectivity across diverse data sources or needs advanced capabilities for data enrichment and contextualization, you should carefully evaluate whether Glean's current connector breadth aligns with your requirements.

2. Scale and efficiency improvements needed

Industry analysis indicates that Glean needs to improve its capabilities in scale and efficiency to meet the needs of broader and more specialized use cases. For organizations planning large-scale deployments across thousands of users, multiple geographies, or specialized enterprise scenarios, this represents an important consideration.

Enterprises with demanding performance requirements or planning significant growth should evaluate whether Glean's current architecture will meet their scale needs both today and as their deployment expands over time.

3. Limited prebuilt applications and solutions

The platform offers a limited set of prebuilt apps and off-the-shelf solutions for customers, compared to other vendors in the market. While Glean excels in its core workplace search competencies, organizations seeking ready-made solutions across diverse enterprise functions may need to invest more in custom development.

If your strategy requires quickly deploying search solutions across multiple departments, such as IT service management, HR operations, finance workflows, or compliance processes, you should assess the development effort required versus solutions that offer more extensive prebuilt applications.

4. Workplace search specialization

Glean is recognized as a powerful workplace search solution with particular strength in workforce use cases, and is viewed as a potential component of enterprise agentic strategies. The platform evolved from its origins in employee support and knowledge management, and its core capabilities remain concentrated in these areas.

Organizations requiring a platform that extends beyond knowledge retrieval into customer service automation, operational workflows, industry-specific processes, or comprehensive agentic capabilities should evaluate whether Glean's current focus aligns with their broader enterprise AI strategy.

5. Agentic capabilities are still developing

While Glean has introduced agent-building capabilities that represent some of the platform's most advanced features for creating agentic workflows, these are newer additions to the product. Organizations planning to deploy production-grade agentic AI for complex, multi-step workflows should carefully assess the maturity of these capabilities against their specific requirements.

If your roadmap includes sophisticated automation scenarios requiring proven, battle-tested agentic functionality at enterprise scale, you should thoroughly evaluate Glean's current capabilities and roadmap in this area.

Watch the webinar: Enterprise Search Done Right: Leveraging Cognitive AI to Unlock Measurable Business Value to learn how leading enterprises are rethinking search with cognitive AI to unlock tangible business results.

Top Glean alternatives in 2026 & beyond

Below are the top 6 Glean alternatives that stand out in 2026 and beyond, along with a breakdown of where they excel, the problems they solve, and the use cases they’re best suited for. 

1. Kore.ai: Ideal for enterprises needing comprehensive search, knowledge management, and agentic automation across all departments

While most enterprise search platforms talk a good game about AI and knowledge management,  when it comes to deployment across diverse departments with real-world complexity, they fall short. Kore.ai is different, and the independent validation backs it up.

Kore.ai provides a comprehensive platform that combines enterprise search advanced by agentic RAG, knowledge management, and agentic AI capabilities across customer experience, employee experience, and business processes. 

What makes Kore.ai different?

Kore.ai has spent 11 years perfecting enterprise AI for 480+ global enterprises. Kore.ai's true multiturn conversational search experiences shine, and this notes platform's deep and rich set of tools to understand a user's intent and ability to "fine-tune the retrieval and results for optimal relevancy."

Think about what that means in practice: your employees don't just get keyword matches, they get intelligent conversations that understand context, remember what was asked before, and deliver exactly what's needed. Analysts highlight Kore.ai's "excellent set of development and evaluation tools for traditional search and for developing RAG and hybrid search pipelines." This is a search that gets smarter with every interaction.

Enterprise Search & Data AI (integrated in the Agent Platform)

Kore.ai's Search and Data AI platform provides intelligent search and knowledge retrieval across the enterprise:

• Knowledge Graph Intelligence: AI-driven knowledge graph construction automatically identifies and maps complex relationships between entities, concepts, and data points. The system dynamically adapts to user interactions, learning from engagement patterns to personalize knowledge delivery.
  
  
• Conversational Search: Natural language and intent-driven search with DialogGPT orchestration, managing multi-turn dialogues, handling complex queries with multiple intents, and asking clarifying questions when needed for context.
  
  
• Hybrid Search Architecture: Combines dense vector-based retrieval, keyword search, and lexical matching with spell correction and synonym support for high-precision, context-rich results. The platform supports semantic understanding trained on company-specific data to recognize unique acronyms, internal projects, and domain-specific terminology.
  
  
• Enterprise + Agentic RAG: Goes beyond traditional RAG with an agentic framework that autonomously applies meta-filters, boosting logic, and optimal response selection through multi-step contextual queries. AI agents analyze queries in real-time, decomposing complex requests and executing multiple searches automatically.
  
  
• 250+ Enterprise Connectors: Production-ready integrations covering major enterprise applications (Salesforce, ServiceNow, SharePoint, Confluence, Jira, Google Drive, Zendesk) plus vertical-specific integrations for banking, healthcare, and retail. All connectors feature intelligent orchestration with automatic failover, retry mechanisms, OAuth2/Basic Auth, field-level filtering, incremental sync, and role access control (RACL).
  
  
• Multi-Modal Search: Search across text, voice, and visual content with unified indexing. The platform processes documents, images, audio, and video uniformly, enabling search using natural language queries, voice commands, or image-based queries.

AI for Work

Kore.ai's AI for Work transforms workplace productivity by embedding AI assistance directly into employees' daily workflows:

• Personal AI Assistant: Provides employees with an intelligent assistant that answers questions, analyzes data, drafts content, finds experts, and helps plan work, all grounded in enterprise knowledge with proper permissions enforcement.
  
  
• 80+ Channel Deployment: Works wherever employees do, Microsoft Teams, Slack, Google Assistant, Cisco Webex, native web/mobile apps, email, SMS, and custom integrations. Seamlessly integrates into Microsoft Copilot Studio and Amazon Q.
  
  
• Role-Based Personalization: The platform understands user roles, departments, search history, and organizational context to deliver personalized results. Business rules integration ensures responses align with organizational policies and priorities.
  
  
• Pre-Built Solutions: Ready-to-deploy applications for common workplace needs, including IT support (AI for IT/ITSM), HR services (AI for HR), service automation, and agent assist for real-time support. Each solution comes pre-configured with data pipelines, optimized indexing, and tailored UI templates.
  
  
• Intelligent Results Delivery: Auto-determines optimal delivery format (AI answers, search results, or hybrid). Provides contextualized AI summaries with citations, transparent AI content labeling, and deep linking to exact pages and sections where answers originated.
  
  
• Multi-Agent Orchestration: What truly differentiates Kore.ai is its multi-agent orchestration capability. The platform enables multiple AI agents to collaborate seamlessly, share context, and execute complex workflows. According to Everest Group's 2025 assessment, "Kore.ai supports advanced multi-agent orchestration through features such as agent collaboration, short- and long-term agent memory, agent tool access, and intelligent delegation."
  
  
• Enterprise-Grade Governance: Kore.ai's governance-first approach ensures safe, compliant AI deployment at scale:
  
  
  • Multi-Level Security Controls: Security policies applied during data ingestion (RACL permissions maintained for each document), during retrieval (role-based filtering), and during answer generation (dynamic content protection ensuring only authorized information appears).
    
    
  • Comprehensive Compliance: SOC2 Type2, PCI DSS certified with ISO 27001, SOC3, GDPR, HIPAA, NIST, and SAML 2.0 compliance. FedRAMP in progress. Data residency controls across 7+ global regions.
    
    
  • Full Observability: Built-in monitoring with real-time dashboards tracking model performance, token consumption, and response quality. LLM-based evaluation using RAG metrics (relevance, faithfulness, context recall) with custom domain rubrics. Comprehensive audit logging with immutable trails.

Industry Recognition:

• Leader in Gartner Magic Quadrant for Conversational AI Platforms, 2025 (third consecutive year)
• Leader in The Forrester Wave: Cognitive Search Platforms, Q4 2025
• Leader in Everest Group's Conversational AI & AI Agents in CXM Products PEAK Matrix Assessment 2025

Proven at Enterprise Scale:

Trusted by 480+ Fortune 2000 companies delivering over $1 billion in cost savings. Proven across finance, healthcare, technology, manufacturing, telecom, and retail with deep expertise in complex workflows.

Key features of Kore.ai:

• True multiturn conversational search with superior intent understanding
• Hybrid search combining semantic, vector, and lexical methods
• Enterprise + Agentic RAG with autonomous query optimization
• 250+ enterprise-grade, plug-and-play integrations
• Multi-agent orchestration across CX, EX, and operational workflows
• Knowledge graph with AI-driven relationship discovery
• Multi-modal search across text, voice, image, and video
• 40+ channel deployment including Teams, Slack, web, and mobile
• Pre-built AI for Work solutions (IT, HR, service, productivity)
• Comprehensive AI governance, security, and audit frameworks
• Model/cloud/data agnostic architecture
• Real-time observability and LLM evaluation tools
• No-code + pro-code development options
• Agent marketplace with 300+ pre-built AI agents

Pros of Kore.ai:

• Superior conversational search with deep user journey understanding
• Enterprise-scale proven with 480+ Fortune 2000 companies
• Best-in-class AI governance and security controls
• Comprehensive RAG capabilities
• Multi-agent orchestration with agent collaboration and memory
• 250+ connectors with extensive data enrichment
• Model-agnostic, cloud-agnostic architecture, integration-agnostic, data-agnostic
• 80+ channels with seamless workplace integration
• Pre-built solutions accelerate time-to-value
• Flexible, transparent pricing models
• Superior roadmap focused on agentic future

Cons of Kore.ai:

• Comprehensive feature set requires structured implementation planning
• Documentation for some newer integrations is still catching up
• A wide product suite may require a clear onboarding plan to avoid overwhelm

Overall verdict:

Organizations looking for a unified solution that brings together enterprise search, knowledge management, employee productivity, customer experience excellence, and process automation, all underpinned by agentic AI with validated enterprise-grade performance and robust governance controls, will see Kore.ai as the most complete platform available. With its adaptable model & data agnostic foundation, sophisticated agentic capabilities for self-directed workflows, comprehensive enterprise integrations, and analyst-acknowledged market leadership, Kore.ai delivers both rapid time-to-value and sustained long-term advantage.

Read the Forrester Cognitive Search Platforms Reports Q4 2025 to explore more about the search platform capabilities of various vendors.

2. Elastic: For technical teams wanting a customizable, developer-centric search infrastructure

Elastic provides a search and analytics platform built on Elasticsearch, rooted in the open-source Apache Lucene project. After returning to an open-source model, the company has strengthened its developer community and innovation pace. Positioning itself as "The Search AI Company," Elastic delivers capabilities across search, observability, and security. For enterprise search, it provides Elasticsearch as the core engine, with Kibana for visualization and Enterprise Search for connectors and web crawling.

Elastic's strength is technical depth and customization. The platform supports hybrid search combining vector and lexical methods, with ES|QL query language enabling complex operations, including joins and analytics across structured, unstructured, and vector data.

The platform excels in relevancy refinement, scale, and efficiency, offering flexible deployment options, cloud, on-premises, or hybrid, giving organizations control over their infrastructure.

But it's built for technical teams. Elastic provides comprehensive APIs, SDKs, and extensibility for highly customized search experiences. It integrates seamlessly with development workflows and offers powerful tools for relevance tuning, index management, and performance optimization.

Key features of Elastic:

• Open-source search engine 
• Controls for relevance tuning, ranking, and query execution 
• Strong scalability and performance for large data volumes 
• Flexible deployment options, including cloud, hybrid, and self-managed 
• Developer-focused roadmap with advanced vector search and LLM controls

Pros of Elastic:

• Excellent fit for developer-led teams and open-source environments 
• Strong performance and efficiency 
• Highly customizable search behavior and ranking logic 
• An open-source approach provides a flexible infrastructure 

Cons of Elastic:

• An open-source, developer-first model can require greater customer ownership of security, AI governance, and operations 
• More of a build-your-own search platform than a packaged enterprise solution 
• Limited native intent understanding and conversational depth compared to some peers 
• Out-of-the-box apps, workflows, and agent-ready experiences are relatively limited 
• Achieving enterprise-grade experiences typically requires specialized expertise and ongoing effort

Overall verdict:

Elastic is best understood as a search infrastructure rather than a turnkey enterprise search or agentic platform. It is a strong choice for organizations that want a scalable, open-source search foundation and have the engineering maturity to design and operate custom search experiences securely. However, enterprises looking for a business-ready or agentic enterprise search platform, with faster time to value and built-in AI governance, should carefully assess Elastic’s open-source, developer-centric approach. 

3. Coveo: For organizations leveraging Adobe, Salesforce, or SAP ecosystems

Coveo is an enterprise search and AI platform founded in 2005 with extensive experience in knowledge retrieval. Positioning itself as the "AI-Relevance Platform," it has built strong capabilities within specific application ecosystems, particularly Adobe, Salesforce, ServiceNow, and SAP.

The platform has historically focused on delivering highly relevant content through search, machine learning, and behavioral analysis, serving customers including Microsoft, Dell, Adobe, and Siemens across commerce, customer service, website, and workplace use cases. 

The platform's strength lies in understanding complex user intent and needs. Rather than using a native knowledge graph, Coveo employs a different approach to entity relationships, focusing on intent recognition and query understanding to effectively interpret user searches.

Coveo offers indexable data sources with enterprise application connectors. The platform layers over existing technology infrastructure rather than requiring replacement, making it suitable for organizations wanting to enhance rather than rebuild their search capabilities.

Key features of Coveo: 

• Hybrid lexical and vector search with strong relevance and reranking models 
• Broad, customizable enterprise connectors, especially within major app ecosystems 
• Scalable, cloud-based cognitive search architecture 
• Advanced intent handling without a native knowledge graph 
• Developer-friendly tooling and implementation support 

Pros of Coveo: 

• Strong relevance and reranking across hybrid search modes 
• Broad connector coverage with deep customization options 
• Scalable cloud deployment and implementation support 

Cons of Coveo: 

• Agentic vision is not strongly differentiated in the current market
• Innovation focus remains largely on retrieval rather than orchestration or execution 
• No native knowledge graph, which may not suit all enterprise architectures 
• Conversational search experiences are typically integration-led rather than native

Overall verdict: 

Coveo is a good choice for enterprises looking for a cloud-based search platform that can improve search within platforms like Salesforce, SAP, and Adobe. However, enterprises seeking a more agentic-first platform, with native orchestration and execution capabilities, should assess whether Coveo’s retrieval-centric roadmap aligns with their longer-term AI ambitions.

4. Lucidworks: Best for commerce-focused search experiences

Lucidworks is a search platform built to manage, search, and analyze data across diverse digital sources, helping organizations build search applications that enable efficient information access. While supporting various use cases, the platform now primarily emphasizes commerce-oriented search experiences.

Lucidworks demonstrates strong relevance, accuracy, and tunability, with robust usage analytics and architecture designed for resilient global search, particularly valuable for organizations operating distributed search experiences across regions. That said, the platform needs to strengthen native governance and orchestration capabilities to meet evolving agentic requirements. This matters for enterprises wanting search to serve as a governed foundation for multi-step, action-oriented workflows rather than just information retrieval.

Lucidworks' agentic positioning is also still developing, with less clarity on its differentiation strategy in the agentic space, especially as it emphasizes commerce-specific agentic capabilities.

Key features of Lucidworks:

• Relevance tuning and hybrid search balancing keyword and vector weighting
• Usage analytics and behavioral signals enabling continuous optimization
• Global deployment architecture for distributed search at scale
• Configurable processing and query pipelines for customized retrieval
• Security features including authentication and security trimming

Pros of Lucidworks:

• Commerce-focused search capabilities
• Granular relevance tuning controls
• Built for global-scale deployments

Cons of Lucidworks:

• Needs stronger native governance and orchestration
• Agentic differentiation and strategic roadmap are still evolving
• Extending beyond native capabilities can be challenging
• Growing commerce focus may not align with teams prioritizing broader enterprise workflows

Overall verdict:

Lucidworks fits enterprises prioritizing commerce search capabilities, particularly when search quality and global deployment are critical. However, organizations seeking enterprise search as a broader agentic foundation with comprehensive native governance and orchestration should carefully assess whether Lucidworks' evolving roadmap aligns with their long-term objectives.

5. Moveworks: Ideal for employee support automation

Moveworks is an enterprise AI assistant platform built to modernize employee support. By integrating with core IT and HR business systems, it gives employees a single, intelligent entry point to get help and complete routine tasks, without navigating multiple tools or raising support tickets. 

The platform delivers capabilities such as natural-language understanding, autonomous task execution,  and contextual reasoning. What sets Moveworks apart is its ability to interpret complex employee requests and execute that process across connected systems.

That said, Moveworks is more specialized in employee support automation and has limited reach in customer-facing operations, back-office processes, or broader enterprise workflows. Even within employee support, the platform's ability to understand and extract meaning from images and other visual formats is still evolving, which may affect how fully certain IT or HR issues can be resolved end-to-end without human help.

More broadly, now that ServiceNow acquired Moveworks, enterprises may want to keep an eye on how the platform’s roadmap and packaging evolve.

Key features of Moveworks: 

• Enterprise search rooted in IT and employee service workflows 
• Multiturn conversational search 
• Knowledge-graph-based contextualization for improved relevance 
• Strong connectors for text, structured data, and common document formats 
• Low-touch deployment with infrastructure complexity handled by the platform

Pros of Moveworks: 

• Strong HR and IT support 
• Proven with large companies 
• Strong set of data connectors

Cons of Moveworks:

• Less suited for customer-facing or back-end workflows
• Visual understanding is still maturing
• Customers cannot bring their own LLMs
• Roadmap may be uncertain post-acquisition

Overall verdict:

Moveworks is a strong fit for organizations that want to modernize HR and IT employee support with a ready-to-deploy AI assistant. For organizations looking to modernize internal support and aren’t particularly looking to expand into a broader, multi-domain agentic orchestration in the near future, Moveworks can be an effective solution.

6. Google Cloud Search: Best for organizations deeply embedded in Google Cloud

Google Cloud Search enables organizations to search information across Google Workspace sources, including Gmail, Drive, Calendar, and other Google applications. Recently, Google Cloud has expanded its enterprise search focus, building on its consumer search expertise.

Strategically, Google Cloud's portfolio includes Vertex AI Search and Agentspace. Vertex AI Search offers a developer-centric foundation, while Agentspace extends this into RAG-based experiences accessible to both developers and less-technical users. The platform draws on Google's heritage in knowledge graphs, with capabilities in data enrichment and answer grounding.

However, while Google has decades of search experience, its enterprise cognitive search offerings are relatively new compared to established enterprise-focused vendors. The platform currently supports a limited range of industry-specific and horizontal enterprise use cases and needs broader coverage.

Additionally, since Agentspace launched in late 2024, connector breadth and packaged integrations are still developing, potentially increasing implementation effort for organizations with complex technology environments.

Key capabilities of Google Cloud:

• Semantic and vector-based search built on Google's search infrastructure
• Developer-centric tooling for building custom search experiences
• Agentspace for creating foundational agentic workflows
• Strong data enrichment and knowledge-graph-driven context
• Third-party data service integration for grounding search and responses

Pros of Google Cloud:

• Deep integration with the Google ecosystem
• Flexible developer tooling for custom search
• Strong data enrichment capabilities

Cons of Google Cloud:

• Enterprise search offerings are relatively new compared to established competitors
• Limited out-of-the-box connectors and pre-packaged integrations
• Fewer industry-specific and horizontal enterprise use cases are currently

Overall Verdict:

Google Cloud Search suits organizations heavily invested in the Google Cloud ecosystem seeking to build developer-led search experiences that can evolve toward agentic capabilities. However, teams wanting a more turnkey enterprise search platform, particularly with extensive out-of-the-box integrations, industry-ready use cases, mature agent orchestration, and proven enterprise search experience, should evaluate whether Google Cloud's roadmap aligns with their near- and mid-term requirements.

7. Algolia: Best for e-commerce and retail search experiences

Founded in 2017, Algolia is a cloud-based platform powering e-commerce and retail search experiences. It helps businesses deliver fast, relevant, and scalable search across websites, apps, and internal systems.

Algolia excels at delivering rapid search results and handling high query workloads. Its API-first approach makes it straightforward for developers and admins to embed search into customer-facing applications, particularly valuable in retail environments where speed and high query volumes are critical.

However, Algolia is less competitive than some peers in deep user context and intent understanding, offering fewer flexible controls for refining relevance and precision in complex enterprise scenarios. Strategically, there are concerns about Algolia's adaptation to the agentic era, with needs for a clearer functional roadmap and sharper differentiation as search evolves from pure retrieval toward action-taking, agentic workflows.

Key capabilities of Algolia:

• Strong digital commerce and knowledge-site search capabilities
• Cloud-based, API-first architecture for easy integration
• Early vector search and RAG-style retrieval innovation
• High-speed indexing and query performance at scale
• Broad content format support for common enterprise needs

Pros of Algolia:

• Fast and scalable cloud search performance
• Quick implementation
• Solid vector search foundations
• Well-suited for user-facing search applications

Cons of Algolia:

• Limited native user context and intent understanding compared to competitors
• Fewer flexible controls for fine-grained relevance tuning
• Roadmap clarity concerns from customers and analysts
• Agentic capabilities remain more retrieval-focused than execution-oriented

Overall Verdict:

Algolia suits enterprises prioritizing speed and ease of integration for cloud-based search, particularly in commerce-driven scenarios. However, organizations seeking enterprise search as a broader agentic foundation, with deeper intent awareness, workflow execution capabilities, and a clearly defined evolution path, should carefully evaluate whether Algolia's roadmap aligns with those objectives.

What is the right Glean alternative for you? 

Choosing an alternative to Glean comes down to understanding your organization's search and knowledge management needs over the next 24-36 months. Before you select a platform, clarify these fundamentals:

1. Your use case breadth: Are you solving workplace search only, or do you need a platform that extends to customer service, operational automation, and agentic workflows across the enterprise?
2. Scale requirements: Can the platform reliably handle your current user base and grow to support thousands more without performance issues?
3. Integration depth: Does the platform connect meaningfully with your complete data landscape, including legacy systems, vertical applications, and proprietary databases?
4. Data processing maturity: Can it enrich, contextualize, and understand your data beyond basic indexing?
5. Agentic roadmap: As you evolve toward agents that take action, does the platform support this progression, or will you need to replace it?
6. Governance needs: Do you have the security, compliance, and observability controls required for enterprise deployment?
7. Pricing fit: Is the cost structure transparent, predictable, and aligned with your growth trajectory?

In practice, each platform on this list has specific strengths. Elastic works well for technical teams wanting maximum customization. Coveo integrates well within specific application ecosystems. Lucidworks focuses on commerce-oriented search experiences. Google Cloud Search suits organizations invested in the Google ecosystem. Algolia serves fast-paced e-commerce and retail scenarios.

But as soon as your needs expand beyond a single use case, when you need a search that powers agents, integrates across departments, handles diverse data types, and scales reliably, the gaps between platforms become apparent.

Enterprise-scale search and knowledge management need a foundation built for comprehensiveness, proven scalability, and long-term adaptability. This is where Kore.ai stands out.

Kore.ai is designed to deliver intelligent search and knowledge management that extends into agentic AI across customer experience, employee experience, and business operations with:

• Leader recognition in the Forrester Wave for Cognitive Search Platforms, validated technical superiority
• True multiturn conversational search with deep user journey understanding
• 250+ enterprise connectors with extensive data enrichment capabilities
• Enterprise + Agentic RAG for autonomous query optimization
• Multi-agent orchestration enables agents to collaborate and share context
• Knowledge graph with AI-driven relationship discovery
• Multi-modal search across text, voice, image, and video
• Proven scalability trusted by 480+ Fortune 2000 enterprises
• Best-in-class governance with comprehensive security and compliance
• Model/cloud/data agnostic architecture, never locked in
• Transparent, flexible pricing that scales with your organization
• Superior roadmap focused on the agentic future

If you need a platform that delivers excellent search and comprehensive agentic AI capabilities today, while continuously evolving to support new use cases, departments, and automation initiatives over time, Kore.ai offers the most complete, future-ready foundation.

Ready to see how Kore.ai can transform your enterprise search and knowledge management? Schedule a Custom Demo.

Not ready yet? Explore our Resources Section to learn more about enterprise search and agentic AI.

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FAQs

Q1. Why do companies look for Glean alternatives? 

Organizations explore alternatives when they need capabilities beyond workplace search, such as broader use case coverage across customer and employee experiences, more extensive integrations, better scalability for thousands of users, comprehensive data enrichment with multi-modal processing, mature multi-agent orchestration, more prebuilt solutions, enterprise-grade governance for regulated industries, and transparent pricing models.

Q2. What are the main alternatives to Glean?

• Kore.ai - For organizations needing search, knowledge management, and agentic AI automation across CX, EX, and operations
• Elastic - For technical teams wanting maximum customization and control
• Coveo - For enhancing search within Adobe, Salesforce, or SAP ecosystems
• Lucidworks - For commerce-focused search experiences with global deployment needs
• Moveworks - For focused IT service desk and HR support automation
• Google Cloud Search - For organizations invested in the Google Cloud ecosystem
• Algolia - For fast e-commerce and retail search applications

If you need to scale across the enterprise, Kore.ai offers significantly more breadth and depth.

Q3. What should I look for in a Glean alternative? 

While assessing Glean alternatives, key factors worth considering include: vendor maturity, use case breadth, integration depth, ease of data processing, scalability, governance, Implementation tenure, and pricing flexibility

Q4. How do I pick the right Glean competitor for my organization? 

Start by asking:

• Will we need capabilities beyond workplace search within 12-24 months?
• Do we have legacy systems, vertical applications, and complex data ecosystems?
• Are we in a regulated industry requiring comprehensive compliance?
• Do we need proven scalability for thousands of users globally?
• Is transparent, predictable pricing important for budget planning?

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(Legal disclaimer: The content in this guide is intended solely for general information and does not constitute professional, legal, financial, or procurement advice. All assessments are based on publicly available materials and customer-visible product information. Any mention of competitor limitations is for comparative context, not disparagement.

As vendor products evolve rapidly, details may become outdated. Kore.ai makes no representations or warranties regarding the completeness or accuracy of competitor information, and no party should rely on this article as the sole basis for a purchasing decision.)

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