Advances in AI are transforming the enterprise search market. As organizations face challenges in extracting insights from growing data spread across multiple platforms, AI-powered enterprise search tools are emerging as alternatives to traditional solutions. New tools offer search and contextualized insights, faster implementation, and better security at a lower price point.
This guide compares the leading enterprise search vendors with emerging solutions. We'll explore how they address key security, scalability, and time-to-value challenges, helping you make an informed decision.
1. Qatalog: real-time AI enterprise search
Qatalog is an AI-powered enterprise search tool that prioritizes data security by never copying or storing your information. Its innovative no-index search engine allows for implementation in hours, not months, minimizes maintenance costs, and ensures your data remains private.
Qatalog seamlessly connects with popular tools like Google Workspace, Microsoft 365, Salesforce, HubSpot, Zendesk, BigQuery, Snowflake, and more. Using Retrieval-Augmented Generation (RAG), it searches across multiple sources and file types—emails, PDFs, databases, and more—to deliver insights that drive smarter and more efficient work.
Key features
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AI-powered search: Qatalog offers a combination of federated search with RAG technology to find and synthesize your company documents and databases.
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Enterprise data integration: With plug-and-play connectors, Qatalog easily integrates into your existing tech stack, ensuring a smooth setup without causing disruptions.
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Self-service capability: Non-technical users can access AI-powered answers independently, reducing the need for data or support teams and improving overall operational efficiency.
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AI with access to real-time data: Qatalog retrieves data directly from your sources without indexing, ensuring access to the most accurate, up-to-date information crucial for scaling operations.
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Enterprise-grade search security and compliance: Qatalog prioritizes data security and compliance with ISO 27001, SOC2-Type II, HiTrust, and GDPR standards. It does not train its models on your company’s data.
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Built-in permissions: Qatalog automatically adheres to enterprise-level access controls without requiring custom integrations.
Pricing
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Starts at $15 per user/month for the Pro Plan.
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The setup process is fast and easy, but we're happy to discuss your use case on a call.
2. Azure AI Search: Microsoft's cloud-based search as a service
Azure AI Search (formerly Azure Cognitive Search) is a robust search engine that helps developers create their own cloud search solutions. It provides infrastructure, APIs, and tools for building search experiences over private, heterogeneous content in web, mobile, and enterprise applications. It’s different from Microsoft Search, which is integrated across all Microsoft 365 apps and services.
Key features
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Vector search, full-text search, and hybrid search capabilities.
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Rich indexing with integrated data chunking and vectorization
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Semantic ranking and relevance tuning
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Integration with Azure AI services and Azure OpenAI
Pricing
Check out the best AI tools for business and productivity.
3. Elastic Enterprise Search: scalable open-source solution
Built on the Elasticsearch engine, Elastic Enterprise Search offers developers a powerful and flexible enterprise search as a service platform. It suits various business needs, such as customer-facing search experiences and workplace search platforms. It also combines different types of searches (structured and unstructured) and copies your company data for faster retrieval.
Learn more about copying and indexing data for AI search.
Key features
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Full-text search capabilities.
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Behavioral analytics dashboards.
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Scalable data store.
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Available for cloud, on-premises, or hybrid deployments.
Pricing
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Tiered pricing starts at $95 per month.
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A pay-as-you-go option available.
Explore Elasticsearch alternatives.
4. Algolia: developer-friendly search and discovery platform
Algolia provides a search and data discovery API that allows developers to build fast and relevant search experiences across various platforms, making it one of the best enterprise search software options for technical teams. It’s also widely used in e-commerce and SaaS applications for its high-speed performance.
Key features
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Fast, typo-tolerant search results.
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Extensive options for customizing search relevance and ranking.
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Analytics and A/B testing for optimizing search performance.
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AI-powered personalization and recommendations.
Pricing
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Offers a free plan for developers, with pay-as-you-go and enterprise plans available.
5. Coveo: AI-powered enterprise search and recommendations
Coveo is a comprehensive AI-powered enterprise search solution that focuses on delivering personalized experiences across various touchpoints. It combines data from multiple sources to offer a unified search experience. It also provides predictive recommendations based on past interactions and searches.
Key features
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Machine learning for improved search relevance over time.
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Personalized search experiences for employees and customers.
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Integration with CRMs, e-commerce platforms, and customer service systems.
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Advanced analytics and insights to optimize content based on user behavior and search trends.
Pricing
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Subscription-based model, starting at $18 per month.
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Enterprise plans available upon request.
6. Amazon Kendra: AWS-powered intelligent search service
Amazon Kendra is a managed and easy-to-use enterprise search service powered by machine learning, ideal for businesses looking for a corporate search engine technology with advanced AI capabilities. It indexes various data repositories and lets users ask questions in natural
language.
Key features
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Natural language processing for accurate, context-aware answers.
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Integration with various AWS services and connectors for other data sources.
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Continuous learning from user interactions to improve accuracy.
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Pre-trained across multiple industry domains.
Pricing
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Enterprise Edition is $1,008 per month.
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Developer Edition is $810 per month.
7. Sinequa: AI-powered insight engine for large organizations
Sinequa offers an enterprise search platform designed for large-scale organizations with complex data environments, making it one of the top enterprise search vendors for big businesses. It indexes and retrieves data from structured databases, unstructured documents, emails, intranet platforms, and other diverse sources. It uses natural language understanding (NLU) to comprehend complex queries and retrieve relevant information from vast data lakes.
Key features
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Unified search across multiple sources.
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Advanced natural language processing and machine learning capabilities.
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Capable of handling large-scale, multi-terabyte datasets.
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No-code UI builder for configuring search interfaces.
Pricing
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Subscription-based model, with costs depending on data volume and specific requirements.
8. Glean: enterprise search for extensive knowledge bases
Glean is an enterprise search software and AI knowledge management tool designed to help employees easily find information across a company’s vast knowledge base. It indexes all connected data for faster retrieval and improved productivity. However, if your company faces strict privacy restrictions and can’t implement Glean, several Glean alternatives offer similar features without copying or storing your data.
Key features
- Indexes content across a wide range of workplace apps.
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AI-powered contextual search based on user behavior.
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Insights into how employees search for and use information.
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Adheres to enterprise-grade security standards and respects existing access permissions across different apps.
Pricing
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Glean’s pricing plans aren't public.
FAQ about enterprise search tools
How quickly can we get up and running?
Look for enterprise search vendors that offer quick deployment options. Some tools can get you started in hours, while others might take weeks or months due to the lengthy data indexing process. Non-indexing solutions like Qatalog typically launch faster since they don't need to copy your data. Ask vendors about their typical implementation timeline and what factors might slow things down. Get specifics about what you'll need to do on your end to speed up deployment.
What's enterprise search really going to cost us?
Don't just look at the per-user price, but dig into the full picture. Ask about storage fees, API calls, and whether there are hidden costs for features you'll need. Some vendors charge extra for AI capabilities or certain integrations. Get clear on whether you'll need dedicated IT resources for maintenance. A higher upfront cost might actually save you money if it means less maintenance and infrastructure work down the line.
How do you handle our sensitive data?
Data privacy and security shouldn't be an afterthought. You need to know exactly where your data lives and who can access it. Ask vendors point-blank: "Do you copy and store our data?" Some tools need to index everything, while others access it directly from your systems. If you're in a regulated industry, make sure the enterprise search vendor can provide specific compliance documentation for standards like GDPR or HIPAA. Don't forget to ask about their security certifications and audit history.
Will the enterprise search engine integrate well with our existing tools and workflows?
Start by listing out all the tools your team uses daily and ask vendors about native integrations versus custom connectors. Native ones tend to be more reliable and easier to maintain. Get details about authentication methods and whether you'll need to modify your current security setup. If you're heavily invested in specific platforms like Microsoft 365 or Google Workspace, look for vendors with deep integration experience in those ecosystems.
What happens to our data?
Some enterprise search solutions need to copy and store your data to work effectively, while others can access it directly from source systems. If real-time accuracy is crucial for your business, you might prefer a tool that doesn't rely on indexing. Think about how often your data changes and whether slightly delayed search results would impact your operations.
How reliable are the AI search results?
Demo your AI-driven enterprise search using your actual data, and try some complex questions. Pay attention to how the tool handles specialized terminology in your industry and multitool connections. Look for features like semantic search and natural language understanding that can help surface relevant results even when queries aren't perfectly formed. Ask about how the tool ranks results and whether you can customize the ranking criteria.
Can my team use this enterprise search without calling IT?
Look for tools enabling self-service, intuitive interfaces, and natural language support. During demos, have non-technical team members try the tool because their experience matters more than the IT team's in this case. Ask about built-in help features and what kind of training the vendor provides. Good enterprise search tools should feel as easy to use as Google or ChatGPT.
How well do enterprise search products handle growth?
Think about your data growth rate and user base expansion plans. Ask vendors about their largest customers and typical performance at scale. Get specific about query limits and response times under heavy loads. Find out if there are additional costs as you scale up. Look for tools that can handle both structured and unstructured data growth.
What can your AI search actually do for us?
Instead of getting caught up in AI buzzwords, ask for specific examples of how AI improves search results and user experience. Look for features like query understanding, result summarization, contextualized answers, and intelligent recommendations. Ask about the AI's ability to learn from your organization's specific enterprise context and usage patterns. Get clarity on whether the AI search requires special training for your industry or use case.
How can we measure the ROI of enterprise search software?
The real impact of enterprise search tools shows up in both time savings and insight quality. Start by tracking how much faster people find information, for example, if there's a drop in internal "where can I find X?" messages. But don't stop at time metrics. The deeper value comes from better decision-making and knowledge discovery. Watch for teams uncovering useful information they didn't know existed, making better-informed decisions because they have access to complete information, and surfacing institutional knowledge that was previously buried. Complement your quantitative data with qualitative feedback from teams about how the tool is changing their work. Often, the most significant impacts are those harder-to-measure moments when someone discovers a crucial insight they wouldn't have found otherwise.
Key takeaways
When evaluating enterprise search solutions, it's essential to consider various factors. Here's a brief comparison:
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Data handling: While most tools index data, Qatalog stands out with its real-time RAG, non-indexing approach.
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AI capabilities: All listed tools incorporate AI, but the level and type of AI integration vary.
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Deployment: Options range from cloud-only (like Algolia) to flexible deployment choices (like Elastic Enterprise Search).
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Scalability: Large-scale solutions like Sinequa and Azure AI Search are designed for enterprise-level scalability.
- Industry focus: Some tools, like Amazon Kendra, offer pre-trained models for specific industries.