Best Enterprise Search Software in 2026 (Compared)

Enterprise search has moved past keyword matching. The current generation of tools uses vector embeddings, retrieval-augmented generation (RAG), and knowledge graphs to return direct answers instead of ranked document lists. For most teams, the practical question is no longer whether to adopt AI search but which platform fits their stack, budget, and data governance requirements.

This guide compares seven enterprise search platforms across features, integrations, pricing transparency, and deployment model. The comparison includes both established players and newer entrants that have shipped meaningful updates in the last 12 months.

Comparison table

Glean

Glean is an AI-powered enterprise search platform built by former Google search engineers. It connects to 100+ enterprise applications and uses a proprietary knowledge graph to understand relationships between people, content, and activity across the organization.

What sets Glean apart

Glean’s knowledge graph is its core differentiator. Rather than treating each document in isolation, Glean maps how documents, people, and projects relate to each other. When a user searches for “Q3 revenue forecast,” Glean considers who created the document, which team they belong to, what project it’s associated with, and how recently it was updated. This produces results that are personalized to the searcher’s role and context.

Glean also offers a generative AI assistant that can summarize documents, answer questions about company data, and draft content grounded in internal knowledge. The assistant uses RAG to pull from indexed company data, which reduces hallucination compared to using a general-purpose LLM.

Other notable features:

• Glean Apps: a no-code builder for creating custom AI agents that automate workflows (data collection, content summarization, report generation)
• LLM flexibility: supports multiple model providers, so organizations are not locked to a single vendor
• Permission-aware results: respects source-system access controls, so users only see documents they are authorized to view

Glean integrations

Glean connects to a broad set of enterprise tools out of the box:

• Communication: Slack, Microsoft Teams, Gmail, Google Calendar
• Storage: Google Drive, OneDrive, Dropbox, Box, SharePoint
• Project management: Jira, Asana, Monday.com, Notion, Confluence
• Sales & marketing: Salesforce, HubSpot, Gong
• HR: Workday, BambooHR, Lattice
• Development: GitHub, GitLab, Azure DevOps, Bitbucket

Total connector count exceeds 100 apps.

Glean pricing

Glean does not publish pricing on its website. Based on buyer reports from early 2026, typical costs look like this:

• Enterprise Search license: ~$45-50/user/month
• Work AI add-on (generative assistant, Glean Apps): ~$15/user/month additional
• Minimum contract: approximately $50,000-$60,000/year (around 100 seats)
• Large deployments: contracts often exceed $200,000/year

Glean requires annual contracts. There is no free tier or self-serve sign-up.

Guru

Guru combines AI-powered enterprise search with a knowledge management wiki and a company intranet in a single platform. Its core value proposition is that search results are only as good as the knowledge behind them, so Guru ties search directly to content verification workflows.

What sets Guru apart

Guru’s verification system requires subject-matter experts to periodically review and re-verify knowledge base articles. This means search results are backed by content that someone has recently confirmed to be accurate. For organizations where outdated information creates real risk (compliance, customer-facing support, onboarding), this is a significant feature.

Other notable features:

• Chrome extension: surfaces Guru answers inline on any website without switching tabs
• AI Content Assist: a writing assistant that drafts content based on existing knowledge base articles
• Duplicate detection: automatically flags redundant content to keep the knowledge base clean
• Proactive Slack suggestions: monitors Slack channels and suggests relevant Guru articles when it detects unanswered questions

Guru integrations

Guru offers native integrations organized by category:

• Communication: Slack, Microsoft Teams
• HR: Gusto, Deel, Personio, BambooHR
• Sales: Gong, HubSpot, Salesforce
• Project management: Asana, ClickUp, Adobe Workfront
• Customer service: Intercom, Zendesk
• Storage: Box, Google Drive, Dropbox
• No-code automation: Zapier, Workato
• API: available for custom integrations

Guru pricing

• Free trial: 30 days of the full platform
• All-in-one plan: $15/user/month (includes AI search, intranet, wiki, all core features)
• Enterprise plan: custom pricing for advanced security, SSO, and larger deployments

At $15/user/month, Guru is one of the more affordable options. The per-seat model means costs scale linearly with team size, which can be a concern for larger organizations (a 500-person team pays $7,500/month).

Quickchat AI

Quickchat AI is a no-code platform for building conversational AI agents. While it’s primarily known for customer-facing chat, its knowledge base and RAG capabilities make it a viable option for internal enterprise search, particularly for organizations that want their employees to get answers through a conversational interface rather than a traditional search results page.

What sets Quickchat AI apart

Quickchat AI takes a different approach from the other tools on this list. Instead of indexing dozens of enterprise apps and presenting search results, it lets you build an AI agent trained on your specific knowledge base that answers questions in natural language. You upload documents, PDFs, website content, or connect data sources, and the agent can answer questions about that content in real time.

This works well for scenarios where:

• employees need answers from a specific documentation set (product docs, HR policies, compliance guides)
• you want the search experience to feel like asking a colleague rather than scrolling through results
• you need the same AI agent to also handle external customer queries

Notable features:

• Deployment in minutes: import documents or paste a URL and the AI agent is ready
• Conversation style customization: control reply length, personality, and tone. Support for over 100 languages with custom translations
• Multi-channel deployment: the same AI agent can be deployed to your website, Slack, WhatsApp, Discord, and other channels simultaneously
• Human handoff: when the AI agent cannot answer, it escalates to a human with full conversation context
• Message Sources: every response shows which knowledge base article was used, so users can verify answers

Quickchat AI integrations

• Communication: Slack, WhatsApp, Telegram, Discord, Messenger
• Customer service: Intercom, Zendesk
• CRM: HubSpot (with AI Actions for automated contact/deal creation)
• Project management: Jira (ticket search)
• Scheduling: Cal.com
• E-commerce: Shopify
• Custom: API and webhooks for any backend system

Quickchat AI pricing

• Free plan: $0/month (200 AI messages, 1 user)
• Basic: $29/month (2,000 AI messages, 1 user)
• Essential: $83/month (unlimited knowledge base, multi-channel integrations)
• Professional: $316/month (human handoff, custom translations, no Quickchat branding)
• Business: $566/month (API access, Intercom/HubSpot/Zendesk integrations, 25 users)
• Custom: $0.60/resolution (dedicated servers, SLA, priority support)

All plans include a 7-day free trial. Unlike most competitors, pricing is flat (not per-seat), which makes it significantly cheaper for larger teams. A 50-person team using Quickchat AI’s Professional plan pays $316/month total, compared to $750/month for Guru or $2,500+/month for Glean.

Coveo

Coveo is an established enterprise search platform (founded 2005) that specializes in embedding AI-powered search and recommendations inside the applications organizations already use, particularly Salesforce, SAP Commerce, Adobe Experience Manager, and ServiceNow.

What sets Coveo apart

Coveo’s strength is in-platform relevance tuning. Rather than being a standalone search page, Coveo augments the search that already exists inside your CRM, commerce platform, or service desk. Its ML models learn from user behavior (what people click, what they ignore, what leads to case deflection) and continuously adjust result rankings.

For organizations already running Salesforce or SAP, this means employees and customers get better search results without leaving the tools they already work in.

Other notable features:

• Relevance Generative Answering (RGA): generates direct answers from indexed content, grounded in your data
• Recommendations engine: suggests related content, products, or articles based on user behavior
• Analytics: detailed dashboards showing search effectiveness, content gaps, and user behavior
• Headless architecture: APIs let you embed Coveo search into custom applications

Coveo integrations

Coveo connects to 60+ sources:

• CRM: Salesforce (deep native integration), Dynamics 365
• Commerce: SAP Commerce, Adobe Commerce, Shopify
• Content: SharePoint, Confluence, Sitecore, Drupal
• Service: ServiceNow, Zendesk
• File storage: Google Drive, Box, Dropbox
• Custom: Push API for any data source

Coveo pricing

Coveo does not publish pricing publicly. It operates on annual enterprise contracts with pricing based on the number of queries, users, and connected sources. Based on market reports, contracts typically start in the five-figure range annually and scale into six figures for larger deployments.

Elastic

Elastic (the company behind Elasticsearch) offers an open-source search platform that combines full-text search, vector search, and geospatial search in a single engine. It’s the most technically flexible option on this list but also requires the most engineering effort to deploy and maintain.

What sets Elastic apart

Elastic gives you complete control over the search stack. You define the index mappings, write the queries, tune the relevance, and manage the infrastructure. In 2025-2026, Elastic added Elasticsearch Relevance Engine (ESRE) with native vector search, reciprocal rank fusion (combining keyword and vector results), and integrations with external LLMs for RAG.

This makes Elastic suitable for organizations that:

• have dedicated DevOps or search engineering teams
• need to handle structured and unstructured data in the same index
• want to build custom search experiences (e-commerce, documentation portals, log analysis)
• require on-premises or hybrid deployment for compliance reasons

Other notable features:

• Hybrid search: combine BM25 keyword matching with kNN vector search in a single query
• Machine learning: built-in models for anomaly detection, classification, and NER
• Observability stack: search, logging, APM, and security in one platform
• Scale: handles petabytes of data and thousands of queries per second

Elastic pricing

• Open source (self-managed): free
• Elastic Cloud: starts at ~$95/month for a basic deployment, scaling based on compute and storage
• Enterprise: custom pricing for dedicated support, advanced security, and cross-cluster replication

Elastic is the only option on this list with a fully free self-hosted tier, which makes it attractive for teams that can manage infrastructure but don’t have budget for SaaS licensing.

Dashworks

Dashworks is an AI knowledge assistant that answers workplace questions by connecting to your company’s tools and documents. It positions itself as the fastest-to-deploy option on this list, with a no-indexing approach that queries source systems in real time rather than maintaining a separate search index.

What sets Dashworks apart

Dashworks’ no-indexing architecture means it doesn’t copy your data into a separate database. When a user asks a question, Dashworks queries the connected applications in real time through their APIs and uses an LLM to synthesize an answer. This approach simplifies data governance (no duplicate data stores) and means new content is available immediately without waiting for re-indexing.

Other notable features:

• Slack Autopilot: automatically answers questions in Slack channels, reducing interruptions for team members who would otherwise answer them manually
• Pre-built workflow templates: ready-made prompts for common tasks (PR reviews, ticket resolution, social media drafts)
• Customizable branding: white-label the interface to match your organization

Dashworks integrations

• Communication: Slack, Microsoft Teams
• Storage: Google Drive, OneDrive, Notion, Confluence
• Project management: Asana, Jira
• CRM: HubSpot, Salesforce
• Customer service: Zendesk
• Development: GitHub
• HR: Workday

Dashworks pricing

• Team plan: $9.99/user/month (unlimited usage, core integrations, Slack bot, workflows)
• Enterprise plan: custom pricing (AI customization, advanced analytics, HRIS integrations, SSO/SCIM)
• Free trial: 7 days, no credit card required

At $9.99/user/month, Dashworks is the cheapest per-seat option on this list. However, the per-seat model still adds up at scale.

Azure AI Search

Azure AI Search (formerly Azure Cognitive Search) is Microsoft’s cloud search service. It’s the natural choice for organizations already invested in the Microsoft ecosystem (Azure, Microsoft 365, Dynamics 365).

What sets Azure AI Search apart

Azure AI Search integrates tightly with other Azure services, including Azure OpenAI Service, which makes it straightforward to build RAG applications on top of your indexed data. It supports vector search, semantic ranking, and hybrid queries out of the box, and you can enrich documents during indexing with built-in AI skills (OCR, entity recognition, key phrase extraction, translation).

For Microsoft-native organizations, the main advantage is that data never leaves the Azure environment, which simplifies compliance and data residency requirements.

Other notable features:

• AI enrichment pipeline: built-in skills for image OCR, entity extraction, PII detection, and language detection during document indexing
• Semantic ranker: re-ranks results using a deep learning model trained by Microsoft, improving relevance beyond keyword matching
• Integrated vectorizer: generates embeddings at indexing and query time, so you can build vector search without managing a separate embedding model
• Knowledge mining: extract structured data from unstructured documents (contracts, invoices, reports)

Azure AI Search pricing

Azure AI Search pricing is consumption-based and depends on the tier, number of indexes, and storage:

• Free tier: 50 MB storage, 3 indexes (suitable for prototyping only)
• Basic: ~$75/month (2 GB storage, 15 indexes)
• Standard S1: ~$250/month (25 GB, 50 indexes)
• Semantic ranker add-on: ~$250/month at Standard tier
• Note: if your use case involves end-user search (not just application search), you also need Microsoft 365 licenses for your users, which adds $20-34/user/month

Azure AI Search is cost-effective for application search (powering a documentation portal or product catalog) but becomes expensive for organization-wide employee search when factoring in per-user licensing.

How to choose

The right tool depends on your technical capacity, existing stack, and budget. Here are some common decision paths:

If you’re a Microsoft shop and most of your data lives in SharePoint, OneDrive, and Teams, Azure AI Search is the path of least resistance. The integration is native, data stays within Azure, and you’re probably already paying for most of the required licensing.

If you want turnkey AI search across 100+ apps and have the budget for it, Glean is the most polished option. The knowledge graph and generative assistant work well out of the box. But expect to spend $50,000+/year minimum.

If verified knowledge accuracy matters most (regulated industries, customer support teams), Guru’s verification workflows ensure that search results are backed by recently reviewed content. At $15/user/month, it’s also one of the more affordable per-seat options.

If your primary need is answering questions from a specific knowledge base (product docs, support articles, internal policies) and you want conversational answers rather than document lists, Quickchat AI offers the fastest deployment and the lowest cost for larger teams. Flat pricing (not per-seat) makes it especially economical compared to per-user alternatives.

If you need search embedded inside Salesforce, SAP, or Adobe, Coveo is purpose-built for this. No other tool on this list offers the same depth of integration with enterprise commerce and service platforms.

If you have a search engineering team and want full control over the stack, Elastic gives you maximum flexibility at the lowest infrastructure cost. It’s the only option with a free self-hosted tier.

If you need the fastest setup and your team lives in Slack, Dashworks’ no-indexing approach gets you running in minutes. At $9.99/user/month, the entry cost is low.

FAQ

Can enterprise search software work with on-premises data?

Yes, but options vary. Elastic can be fully self-hosted. Coveo and Azure AI Search support hybrid connectors that index on-premises data while running the search service in the cloud. Glean and Dashworks are SaaS-only, so on-premises data must be accessible through APIs or cloud-synced copies.

How do these tools handle data privacy and access controls?

All platforms on this list respect source-system permissions. If a user doesn’t have access to a document in Google Drive, they won’t see it in search results. Glean and Coveo have the most granular permission mapping across a large number of connectors. Quickchat AI handles data privacy through GDPR compliance, PII scrubbing, and a policy of not training models on customer data.

What’s the difference between enterprise search and an AI chatbot?

Enterprise search indexes content from multiple sources and returns relevant documents or answers. An AI chatbot (or agent) engages in a conversation, can ask clarifying questions, and take actions. Some tools on this list (Quickchat AI, Glean Apps) blur this line by combining search with conversational AI. The choice depends on whether your users prefer browsing results or asking questions.

How long does implementation typically take?

Quickchat AI and Dashworks can be set up in under an hour. Guru takes a few days to populate the knowledge base and configure verification workflows. Glean requires a few weeks for full connector setup and knowledge graph indexing. Coveo and Elastic implementations typically take weeks to months, depending on the number of data sources and customization required. Azure AI Search setup time depends on the complexity of your indexing pipeline.

Is Qatalog still available?

No. Qatalog was acquired by ClickUp in November 2025. Its ActionQuery technology is being integrated into ClickUp’s platform. If you were evaluating Qatalog, the closest alternatives in terms of workflow flexibility are Dashworks and Glean.