Search as a Service evolution

While traditional Search as a Service (SaaS) solutions have served businesses well, a new approach is emerging: real-time search powered by Retrieval Augmented Generation (RAG). This comprehensive guide explores both approaches, with a particular focus on next-generation solutions that reshape how organizations think about search and data access.

What is Search as a Service?

Search as a Service is a cloud-based solution that allows organizations to implement powerful search capabilities without building and maintaining complex search infrastructure. These solutions typically require indexing your data - creating copies of your documents in a specialized database. Providers like Elasticsearch and Algolia offer features like:

• Full-text search across multiple data sources

• Advanced relevance ranking and scoring

• Faceted search and filtering

• Analytics and search metrics

• Typo tolerance and fuzzy matching

What is search as a service market right now?

However, the search as service landscape is evolving with Retrieval Augmented Generation (RAG), represented by innovative players like Qatalog. This new approach is transforming how organizations think about search and information retrieval.

No-index search as a new alternative

Qatalog is pioneering a different approach: Search as a Service without data indexing. Instead of copying and storing your data, it connects directly to your existing platforms and searches them in real-time. This brings several unique advantages:

1. Immediate implementation

• No complex setup or data migration
• Connect your tools and start searching
• No infrastructure changes needed

2. Real-time information access

• Always get the latest information
• No waiting for index updates
• Direct access to live data sources

3. Enhanced security

• No data copying or external storage
• Maintain existing security permissions
• Reduce compliance concerns

4. Natural language understanding

• Ask questions in plain language
• Get contextual, accurate answers
• Sources cited automatically

Comparing search service approaches

Traditional Search as a Service relies on data indexing to return exact document matches, focusing on retrieval and ranking. In contrast, Qatalog search engine works without indexing, combining data retrieval with AI-powered synthesis.

Indexed search is ideal for structured data and precise queries, while Qatalog excels with unstructured data and natural language questions, offering more flexibility and context-aware responses.

For example, the finance team can use traditional search to find exact transaction records, like specific invoice numbers. But with indexless search, they can go further—asking questions like, “What are common reasons for late payments?” to get a context-rich answer based on unstructured data.

In customer support, traditional search finds specific troubleshooting guides with keywords. With Qatalog, however, agents can ask complex questions like, “What are common issues with Product X?” and receive a comprehensive answer that pulls from mixed sources—such as chat logs, manuals, and feedback—enabling faster, more informed resolutions.

To understand the search-as-a-service market, let's compare search solutions.

How to choose the right search as a service company?

When selecting a search service, organizations should consider several factors.

1. Data structure

Highly structured data, such as product catalogs, customer databases, and HR records, might benefit from traditional search services due to their reliance on established indexing patterns. Consider factors like:

• Schema consistency 

• Data standardization level

• Update frequency

RAG could better serve unstructured or frequently changing data that require natural language understanding, such as documentation, emails, chat logs, or data warehouse entries. To make the best choice, evaluate:

• The variety of content types

• Document complexity

• Update frequency

2. Volume considerations

Assess the scale of the data you need to search. For smaller data sets, traditional search services might be cost-effective, requiring lower infrastructure investments. However, consider the costs associated with indexing, including storage needs and processing overhead for larger data volumes. It's also important to project data growth over the next year, data retention needs, and any seasonal volume fluctuations.

2. Document retrieval needs

Indexed search as a service can handle simple document retrieval where users primarily use attribute-based filters to get exact marches. It’s appropriate when retrieval speed is the critical factor and document locations are clearly defined. It’s great for: 

• Static content

• Historical records

• Archive access

• Batch processing needs

In contrast, RAG-based search as a service is preferred when the information users seek needs interpretation and requires context synthesis across multiple sources. It allows users ask natural language questions and get answers from multiple documents. Real-time RAG is critical for:

• Financial data

• Inventory systems

• Customer service

• Live collaboration tools

3. Technical infrastructure

Evaluate the resources required to maintain a search as a service system, including in-house search expertise, DevOps resources, and support staff. Ensure your budget accounts for infrastructure, licensing fees, and overhead costs. Compatibility with your existing tech stack can vary, and integrating indexed solutions may involve longer timelines due to data migration and staff training.

When selecting a provider, consider your growth needs, including user base expansion, increasing data volumes, and query load management. Set realistic performance expectations, as indexed search as service may become less accurate as data volumes grow.

4. User experience goals

Enterprise search-as-a-service is ideal for use cases requiring direct document access, such as legal research, compliance reviews, or archive management. It’s especially beneficial for teams that need to preserve documents, control versioning, and maintain audit trails.

However, if your goal is to receive synthesized answers that help your team process information more efficiently, RAG-powered search as service is the better choice. RAG provides accurate answers and includes source attribution, allowing you to rely on the information confidently.

One drawback of RAG is that its response times can be slower. If fast performance is critical, traditional search systems may be a better fit.

What are search as service applications examples?

1. Market intelligence access

An electronics manufacturer's insights team struggled with market intelligence scattered across multiple platforms—from PDF reports to Nielsen data and Statista. A traditional enterprise search as service implementation would have required creating and maintaining indexes of all these sources, with regular updates to keep information current.

By leveraging AI for consumer insights through a RAG-based solution, they connected directly to their existing platforms. This enabled analysts to ask complex questions like, "What were our market share trends in the premium segment last quarter compared to our top competitors?" The system seamlessly extracted relevant data from PDFs, performed calculations that were previously done manually in Excel, and delivered contextualized answers by synthesizing multiple sources.

2. Healthcare provider network

A concierge medical practice needed to optimize its provider network management. Traditional search service approaches would have required indexing data across Salesforce, clinical documentation systems, and internal documents, with complex security configurations to maintain HIPAA compliance.

Instead, their RAG-based implementation directly queries existing systems in real-time. Staff can ask natural language questions like "Find me a dermatologist who does house calls within 25 miles of San Francisco and has availability this week," and the system synthesizes responses from multiple sources while maintaining existing security permissions. The solution integrates seamlessly with their Salesforce, Confluence, Google Drive, and clinical documentation systems, automatically ranking providers based on patient volume, specialist expertise, and geographic proximity.

3. Customer support portal

Support teams often need to search across multiple data sources - past tickets, product documentation, and knowledge bases. A traditional implementation would index all this content and return relevant documents based on support agent queries. With RAG, when an agent types "How do I help a customer upgrade their enterprise plan?", the system pulls real-time data from the CRM, billing system, and product documentation to provide a contextualized answer that includes current pricing, the customer's history, and the specific steps for their situation.

4. Technical documentation search

Software companies often implement search across their technical documentation. Search as a search helps developers find specific API endpoints or code examples through keyword matching and filters. RAG implementation goes further—when a developer asks "How do I implement authentication in the mobile SDK?" it can pull together information from the API docs, sample code repositories, and recent updates to provide a complete, contextual response that includes the current best practices and common pitfalls.

What is the future of search?

The enterprise search market continues to evolve, with traditional Search as Service providers incorporating AI capabilities and new players like Qatalog pushing the boundaries of what's possible with real-time RAG technology. We're likely to see:

• Increased integration of AI and traditional search capabilities

• More sophisticated natural language understanding

• Enhanced personalization and context awareness

• Improved handling of multimodal content

The emergence of RAG as a Service, exemplified by Qatalog's innovative approach, represents a significant evolution in the search service landscape. While traditional Search as a Service solutions continue to provide value for certain use cases, RAG-based solutions offer new possibilities for organizations seeking more intuitive and dynamic search capabilities.

Considering both traditional and RAG-based solutions will be crucial as organizations evaluate their search needs. The choice between them will depend on specific requirements, data characteristics, and desired user experiences. What's clear is that the future of search is becoming more intelligent, more natural, and more capable of meeting diverse organizational needs.

Related: The benefits of enterprise search without indexing