Top federated search vendors: Why Qatalog stands out?

The AI-powered search market is evolving fast, with innovative solutions challenging traditional federated search vendors. As businesses increasingly demand secure, real-time, and scalable tools, new approaches redefine what search can achieve. Qatalog leads this shift by addressing the limitations of traditional data indexing while unlocking new possibilities for enterprise search.

In this guide, we’ll explore how Qatalog compares to top federated search vendors and why its indexless search sets a new standard.

1. Qatalog

Qatalog offers a unique combination of federated search and RAG capabilities, which allows it to fetch information from multiple sources and synthesize it for better data discovery.

Qatalog skips the traditional step of creating and storing copies of your data (called an index), typical federated search or RAG systems. Instead, it pulls live data directly from source systems using real-time API queries. This architectural difference allows it to remove traditional barriers—weeks of setup and high upfront costs for data indexing.

It connects to popular tools, like SharePoint, Google Drive, Salesforce, Zendesk, BigQuerry, Snowflake, etc. and processes structured data and unstructured documents.

Why is Qatalog the best federated search provider?

1. Zero data retention

By avoiding data indexing entirely, Qatalog eliminates the security risks associated with maintaining copies of sensitive information.

2. Real-time results

Direct API access ensures that search results always reflect the current state of source systems. It discards retrieved data after query resolution.

3. Native permission inheritance

Security permissions are automatically inherited from source systems, eliminating the need for complex permission synchronization.

4. Efficient scaling

Without the overhead of index maintenance, the solution scales linearly—adding more source data doesn't directly increase infrastructure needs.

How to get started?

Start by signing up for a free trial and connecting to your actual data sources. Qatalog supports a wide variety of integrations, such as Microsoft 365 tools, Google Workspace, BigQuery, Snowflake, Salesforce, Zendesk, and a few others.

The setup process is fast and easy so Qatalog clients can usually see Qatalog's value from day one.

Paid plans start at $15/mo per user. Enterprise companies can reach out for customized pricing after discussing their use case on a call.

2. Elastic (formerly Elasticsearch)

Long considered the standard for federated search, Elastic offers robust text analysis capabilities and scales well for large organizations. However, its index-based architecture requires significant infrastructure and ongoing maintenance. While powerful, it comes with the traditional challenges of managing indexed data.

Key features

• Enterprise-grade federated search across multiple data sources

• Integration with Kibana for advanced data visualization and interactive dashboards

• Creates and maintains full-text inverted indexes of all ingested content

• Strong text analysis and relevance ranking capabilities using indexed data

• Stores and indexes both structured and unstructured data

• Popular in large organizations due to the scalability of index structures

Related: Elasticsearch alternatives

3. Microsoft SharePoint Search

SharePoint Search offers deep integration with the Microsoft ecosystem, providing native federated search for Microsoft-centric organizations. Its hybrid architecture provides flexibility but still relies heavily on indexing for optimal performance. Microsoft has enhanced SharePoint Search capabilities with Copilot, their AI-powered assistant, to provide AI-generated summaries and insights.

• Hybrid approach indexes SharePoint content directly while federating some external sources

• Native federation capabilities for SharePoint and Microsoft 365

• Can maintain indexes of external content or perform real-time federation based on connector type

• Enterprise-wide search across SharePoint sites, file shares, and databases

• Part of broader Microsoft collaboration platform

Related: The best intranets

4. Coveo

This federated search company is known for its strong analytics and AI-powered knowledge management. Coveo excels in creating personalized search experiences and is particularly popular in customer service and knowledge management scenarios. Its unified index approach simplifies management but still requires maintaining copies of source data. 

Key features

• AI-powered enterprise search platform that creates unified indexes

• Maintains secure cached copies of indexed content with regular updates

• Strong analytics and personalization features leveraging indexed metadata

• Extensive connector library for various data sources

• Popular in customer service and knowledge management

Related: The enterprise search tools

5. Sinequa

Sinequa’s cognitive search capabilities set it apart from other federated search companies, particularly in regulated industries like life sciences and manufacturing. Its advanced linguistic processing and deep learning provide highly relevant results, even in complex, multilingual environments. While it excels at uncovering insights from vast data sets, its traditional index-based model inherits common challenges like high maintenance costs.

Key features

• Cognitive search and analytics platform that creates comprehensive indexes

• Stores and indexes enriched versions of documents with linguistic processing

• Strong in multilingual search through specialized language indexes, ideal for global enterprises

• Deep learning capabilities for improved relevance using indexed content

• Popular in life sciences and manufacturing

6. Lucidworks Fusion

Built on Apache Solr, Lucidworks Fusion offers robust e-commerce capabilities and strong machine learning features. While powerful, this federated search tool is built for large enterprises that can afford significant investment in index maintenance and infrastructure.

Key features

• Built on Apache Solr with full indexing capabilities

• Creates and maintains complete indexes of all content

• Strong machine learning capabilities using indexed data

• Maintains index replicas for redundancy and scale

• Popular in e-commerce and digital workplace

How to choose the right federated search vendor?

When selecting a federated search provider, organizations should consider their specific requirements:

• For organizations with stringent security requirements or real-time data needs, Qatalog's innovative API-first approach offers clear advantages.

• For businesses and startups seeking rapid time-to-value without complex indexing infrastructure to set up, Qatalog is the best federated search tool.

• Companies deeply invested in the Microsoft ecosystem might find SharePoint Search's integration benefits compelling.

• Organizations with specific e-commerce needs might lean toward Lucidworks.

• Those requiring advanced multilingual capabilities might consider Sinequa.

However, the broader trend toward secure, and efficient real-time RAG based search solutions suggests that API-first architectures like Qatalog's represent the future of enterprise search. As organizations increasingly prioritize quick deployment, security, data freshness, and cost-effective scaling, federated searh vendors that eliminate traditional index-based limitations become increasingly attractive. The ability to get immediate value from existing data without complex setup processes or infrastructure changes marks a significant advantage over traditional approaches that require months of preparation and indexing before delivering results.

What are federated search challenges?

The four key challenges that organizations face when implementing federated search:

1. Data security and privacy

With growing regulatory requirements and cyber threats, protecting sensitive information has never been more critical. Traditional federated search vendors often require indexing data, creating additional copies that expand the potential attack surface. This duplication of sensitive information increases security risks and complicates compliance efforts.

2. Data freshness and accuracy

Many federated search solutions struggle to maintain current data, as indexed content becomes stale between updates. This lag between reality and searchable content can impact decision-making and operational efficiency.

3. Scaling and cost management

As data volumes grow exponentially, the resources required to maintain search capabilities often grow even faster. Organizations frequently find themselves caught between performance requirements and escalating maintenance costs, particularly with traditional index-based federated search tools.

4. Access control and permissions

Maintaining proper access controls across a complex enterprise environment is challenging enough without adding another layer of permission management for search. Many organizations struggle to keep search permissions synchronized with source systems, risking either data exposure or access problems.

How to overcome federated search limitations?

Key federated search challenges stem from relying on indexed data. Qatalog addresses them with a novel approach that specifically avoids the indexing standard to both traditional federated search. It uses real-time API calls rather than searching indexed content, delivering a robust, scalable alternative for enterprises wanting to solve specific problems with their valuable data.

Here's how Qatalog's ActionQuery search engine works:

• No indexing required - uses real-time API and GraphQL queries
• Combines federated search with RAG capabilities
• Transient data approach - discards retrieved data after query resolution
• Maintains native permissions through direct API access
• Strong focus on security by avoiding data duplication
• Includes LLM capabilities for query processing and response generation
• Popular in enterprises requiring strict data security and real-time results

Questioning your current federated search solution? See what's possible with Qatalog Try it for free

Indexed vs indexless federated search comparison

What are federated search examples?

1. General document discovery

A legal team can search across document repositories like SharePoint and Google Drive to quickly find case files, contracts, or policy documents for compliance audits.

Federated search helps them access historical records and documentation from multiple departments, ensuring they have comprehensive information when preparing legal briefs.

2. Enterprise knowledge management

A product team in a tech company can connect Jira, Confluence, and Slack to access project updates, technical specifications, and shared resources in one search.

A marketing team can use federated search to find insights from different department repositories, such as sales data, customer feedback, and industry research, helping them create more targeted campaigns.

3. Research and academic use

A university research department can search multiple databases like PubMed and JSTOR to gather publications, journal articles, and citations for grant proposals or academic papers.

They can search across journals, finding the latest research articles, conference papers, and patents to stay updated on advancements in their field and make connections between disparate studies.

4. Customer support

A customer support team can use federated search to quickly access case histories across Zendesk and Salesforce, streamlining resolution times and improving customer satisfaction.

They can also search across various knowledge bases to find troubleshooting guides, FAQs, and case histories enabling faster issue resolution.

What are ideal case studies for indexless search?

• High-value data use cases, such as real-time AI business analytics, financial data processing, customer data analysis, and operational intelligence.

• Sensitive data applications, such as regulated industries, healthcare data, financial services, personal information.

• Complex data relationships, like business intelligence, data warehouse integration, cross-system analytics, and real-time AI reporting.

• Critical decision support, for example, executive dashboards, risk analysis, market intelligence, and operational monitoring.

What is federated search?

Federated search is a technology that allows users to search multiple data sources simultaneously through a single search interface. Instead of searching databases individually, federated search engines query multiple sources and aggregate the results. This is particularly valuable for organizations with distributed information across various repositories, databases, and platforms.

For example, a financial institution can use federated search to find client applications and customer correspondence. But won’t be able to process real-time financial data or perform complex risk analysis.

What is the difference between federated search and RAG?

The main difference between federated search and RAG lies in their architecture and capabilities. Federated search queries multiple indexes to retrieve existing documents based on keyword matches. Conversely, RAG connects directly to data sources in real-time, using AI to understand context and generate new insights.

For example, real-time RAG enables complex data analysis for sensitive information, delivering current insights without index lag, such as interpreting real-time customer feedback trends from multiple platforms. On the other hand, federated search is ideal for simple document discovery across systems, such as finding specific policy documents across various departments.

Find out more about search as a service.