Real-Time RAG vs Indexing: A Technical Deep Dive

For years, RAG indexing seemed like the go-to solution: copy data, store it externally, and periodically reindex to keep it usable. But this method introduced significant drawbacks:

Storing sensitive data on external servers raises serious security concerns.
Reindexing and hosting large datasets come with substantial financial and operational burdens.
Delays in updating indexed data make real-time accuracy impossible.
Managing permissions for duplicate data adds unnecessary complications.

Recognizing these barriers, Qatalog developed an ActionQuerry search engine with real-time RAG capability that eliminates the need for indexing. To dive deeper, I spoke with experts Zachary Nickerson, Product Manager at Qatalog, and Ankit Mishra, Product Director at Qatalog, about the differences between indexed and no-indexed RAG.

What is RAG indexing?

RAG indexing is a pre-processing method where a system:

Takes the data and breaks it into smaller segments or "chunks"
Transforms them into mathematical representations called vectors
Stores (indexes) these vectors (copies of your data) in a database for quick retrieval

For example, to index your Google Drive, standard RAG tools make a complete copy of all your Google Drive files upfront, download them, and create vector embeddings in advance. While new files are downloaded periodically, the system doesn’t update in real-time, which means you might miss important updates when searching for information.

The purpose of indexing in RAG is to enable quick matching between queries and relevant content and allow fast retrieval of information without processing in real-time.

This approach requires regular reindexing to stay current, has high setup and storage costs, and requires recreating permissions. Additionally, indexed RAG solutions raise security concerns since sensitive data must be stored on external servers.

What is real-time RAG?

Real-Time RAG, also known as No-Index RAG, eliminates the need to index data. Instead, it performs searches directly through live data sources using each platform's search API to retrieve the most accurate and up-to-date information while minimizing data security risks and reducing maintenance costs.

Zach Nickerson, Product Manager at Qatalog, explains the nuance:

"Real-Time RAG can be misleading because all RAG systems process queries in real-time. What makes our approach unique is that we access data directly from the source, retrieving information the instant it's created, rather than relying on pre-indexed copies of data."

The key difference is where the data comes from: traditional RAG retrieves from pre-indexed storage, while Real-Time RAG accesses live data directly from source systems. This approach, also known technically as No-Index RAG, ensures you always get the latest information, right up to the moment of your query.

While all RAG systems process queries in real-time, Real-Time RAG is unique because it accesses data directly from source systems rather than from pre-indexed storage. This means you always get the latest information, right up to the moment of your query.

Qatalog is a pioneer of Real-Time RAG. Through its federated search capability, it connects directly to live tools and platforms, searching across multiple sources simultaneously. The system is optimized to be selective about which platforms to search - users can either manually select specific platforms or let the system determine which sources are most relevant to their query.

How does Real-Time RAG work?

When a user submits a query, real-time RAG:

Transforms user query into keywords
Submits keywords to source systems' search APIs
Gets results from multiple platforms
Downloads and process relevant content on demand
Creates embeddings temporarily
Generates answers using LLM
Discards content after use so that nothing is stored

Ankit Mishra, Product Director at Qatalog, explains real-time RAG importance:

“That is how Qatalog’s always able to have the latest information, and it doesn’t depend on when was the last time the information was reindexed, which is what any other competitor does today."

Why do companies choose Qatalog as their RAG provider?

1. Instant setup

Sign up for a free trial
No need to index or duplicate data
Start using existing search APIs immediately
Minimal upfront costs

2. Always fresh data

Access information in real-time
No lag between updates and availability
Perfect for dynamic, frequently changing content

3. Enhanced security

No data duplication or storage
Leverage existing permission systems
Maintain data sovereignty

4. Native integration

Works with existing search APIs
Maintains platform-specific features
Preserves existing workflows

Real-time RAG vs RAG indexing

Zach explains the key differentiators between traditional RAG and real-time RAG in more detail.

1. Speed and performance

Traditional RAG: "With indexed RAG, since the content is pre-processed and stored, finding matches is nearly instantaneous — the system is just looking up pre-existing information rather than processing it in real-time."

Real-Time RAG: "One of the clear tradeoffs is speed. Because every time you do a search, the system has to find content, download it, and send it to an LLM. That process takes longer, but it ensures you're always working with fresh data."

2. Accuracy and relevance

Real-time RAG offers immediate access to updated information: "Qatalog can unlock real-time information by plugging into a live database and pulling information created seconds ago. With indexing, you're limited by your indexing schedule — any information created since the last index isn't available."

3. Scalability and maintenance

Traditional RAG has significant setup requirements: "Setup costs with indexing are really expensive because you have to copy everything and create an index for every document. You basically have to duplicate all of your information, which impacts both storage costs and setup time."

RAG without data indexing offers more flexibility: "As long as a system has a search API, Qatalog can typically integrate with it. It transforms the user's query into whatever format that particular system requires."

4. Security and compliance

A significant advantage of real-time RAG is its security model: "Permissions are a huge consideration. When you index data, you have to recreate all the permission models. With Qatalog’s approach, we leverage existing permissions because we're using the native search APIs — we inherit the security model that's already in place."

With RAG indexes, companies have to recreate permissions models in the new system, maintain two separate security systems, trust another company to store sensitive data, and deal with the compliance implications of data duplication.

This is a significant overhead and security concern for companies, especially those with sensitive information.

A table comparing real-time RAG vs indexed RAG.

When to use real-time RAG?

Real-time RAG is particularly valuable for:

Organizations requiring immediate access to fresh data
Systems where permissions and security are paramount
Scenarios where storage costs are a concern
Cases where quick implementation is needed
Cases where data changes frequently
Use cases involving multiple data sources

Zach emphasizes: "Real-time RAG focus is on use cases that need a mix of real-time information and structured data, where the freshness of information really matters."

When to use RAG with indexing?

RAG indexing remains valuable for:

Static content repositories
Cases where query performance is the top priority
Vast datasets that need comprehensive analysis
Scenarios where sub-second response times are more important than fetching fresh data

The choice between real-time RAG and traditional indexing depends heavily on your specific use case. Real-time RAG offers superior freshness and security with minimal setup costs, while traditional RAG indexing provides faster response times for static data.

How do RAG providers handle large datasets?

Real-Time RAG actually has an advantage with large datasets. As Zach Nickerson explains: "Our solution supports large data sets much better. Because we aren't creating, storing, updating, or managing permissions for a huge dataset. Additionally, the costs associated with this are high, which may force orgs to focus their efforts on a smaller window of indexed information. Qatalog can theoretically connect to all of your business data sources."

This is particularly important for enterprises with vast amounts of data across multiple systems. While the technical challenge shifts to efficiently filtering source data to find relevant information, this is handled by the Qatalog’s ActionQuerry system and doesn't impact the user experience.

Conclusion

While indexed RAG solutions like Glean Search will continue to have their place, no indexed RAG represents a significant step forward in making real-time AI applications more practical and accessible. By eliminating the need for complex vector databases and focusing on real-time data access, this approach democratizes enterprise search by removing traditional barriers to entry—weeks of setup and high upfront costs for data indexing.

The evolution from index-based RAG to real-time RAG mirrors a broader trend in technology: the move from batch processing to real-time operations. By eliminating the need for indexing and embracing real-time data access, this new approach makes AI-powered search more accessible, secure, and practical for organizations of all sizes.

Whether you're just starting your AI journey or looking to enhance existing systems, considering a real-time RAG approach could save significant time and resources while delivering better results.

FAQ

What is RAG in AI, and why do companies use it?

RAG (Retrieval Augmented Generation) is a method that enhances LLM responses by providing them with relevant context from your organization's data. Think of it as giving the AI a set of reference materials before asking it to answer a question. Companies use it to get accurate, contextual answers from their private data sources.

What's the difference between indexed and no-index RAG?

Indexed RAG pre-processes and stores data in vector databases, while no-index RAG queries live data sources in real-time. It's like having a pre-made reference book versus looking up original sources each time.

How does RAG with indexing work?

Index all your documents by transforming them into vectors
Store these vectors in a specialized database
When a question comes in, find the most relevant documents through a vector similarity search
Feed these documents to the LLM along with the question

How does RAG without indexing work?

No-index RAG:

Transforms queries into keywords
Searches across live data sources
Downloads relevant content temporarily
Processes content in real-time
Generates response
Discards processed content

How do they handle permissions?

Indexed RAG requires rebuilding permission systems and maintaining duplicate security models
No-index RAG inherits existing permissions from source systems, requiring no additional security setup

What are the data privacy implications?

Indexed RAG stores copies of sensitive data, raising privacy concerns
No-index RAG doesn't store data, minimizing privacy risks

What are the setup costs?

Indexed RAG: High initial costs for indexing and storage infrastructure
No-index RAG: Seat based pricing, 14-day free trial available

What are the ongoing costs?

Indexed RAG: Storage costs, index maintenance, lower per-query costs
No-index RAG: No setup, noindexing costs, no vector storage costs. Theoretically it is higher per-query processing cost but in Qatalog model, we don't charge by the query.

How long does implementation take?

Indexed RAG: Longer implementation due to indexing and permission setup
No-index RAG: Faster implementation using existing APIs

How do they handle data updates?

Indexed RAG: Requires regular re-indexing to stay current
No-index RAG: Always uses current data from source systems

What are the integration requirements?

Indexed RAG: Needs direct access to data sources for indexing
No-index RAG: Requires search API access to data sources

Which approach is faster?

Indexed RAG provides faster responses since content is pre-processed.
No-index RAG takes longer because it processes content in real-time but ensures data freshness.

How do they handle large datasets?

Indexedl RAG: High storage costs and maintenance overhead may force organizations to be selective about what data they index
No-index RAG: Can connect to virtually unlimited data sources since there's no storage overhead. The system handles complexity of finding relevant information behind the scenes, making it seamless for users

How do they handle API rate limits?

Indexed RAG: Mainly affected during indexing
No-index RAG: Must manage rate limits for each query

How do they scale with user growth?

Indexed RAG: Scales well with users, may need more storage
No-index RAG: Scales with API and processing capacity

How do they handle increasing data volumes?

Indexed RAG: Requires more storage and indexing time
No-index RAG: May face processing limitations with very large datasets

What are the common issues with each approach?

Indexed RAG:

Stale data
Permission syncing issues
Storage costs
Index maintenance