End RAG Sprawl: The Case for Platform Standardization

Retrieval Augmented Generation (RAG), a way to enhance AI generation by retrieving relevant, trusted information, has rapidly become a standard practice within enterprises. It's a powerful tool for various functions that requires higher accuracy of output - support assistants, coding practices, legal research and summarization services, employee benefits Q&A and other operations or knowledge services. However, this widespread adoption across functions has led to a growing problem: RAG sprawl.

While one would think every function within an organization having its own RAG system specific to their use cases would be a great way to meet the mandates of aggressive AI adoption, they are often met with varying degrees of success during implementation. The "Do It Yourself" approach to RAG can be challenging in numerous ways, requiring significant technical expertise and resources and a constant awareness of what is moving forward in this fast-paced vendor and open-source ocean of options and advancements.

As more and more RAG implementations emerge within large organizations, each with its own variations and component flavors, leadership teams are now finding themselves facing a significant management and maintenance headache ahead: RAG Sprawl. This approach risks:

Locking into “post card” versions of tech, i.e. the innovation will rapidly move forward with Agentic and other needs around the corner
Spending more resources on building RAG and patching the integrations than on core business value delivery and impact applications
Lower visibility into vulnerabilities and data governance

This situation is already a reality for some enterprises, leading to inefficiencies, disconnected data management, custom integration risks, and other security concerns. Also, there is no centralized visibility in what is done with data or useful to the overall organization. Recognizing these challenges and the risks with RAG Sprawl, a new trend is rapidly emerging: a motion towards standardizing on a few selected platforms. But how should organizations go about choosing what platforms to standardize on?

The first and kind of obvious choice many enterprises start with is evaluating the options provided by their current hyperscalers, including LLMs. Some even safeguard and select two, as a lesson learned from the cloud-migration days and the fear of locking in or price jacks at the will of the hyperscalers’. Here, it mainly depends on whose cloud you have decided on and the natural selection becomes the general LLM provider who exists in that cloud environment.

The next critical decision for organizations is choosing a partner for their Retrieval Augmented Generation (RAG) strategy. It's estimated that RAG will be essential for 30-70% of enterprise AI use cases, depending on industry. Today more than 1 in 4 already deploy RAG in their organizations. Its success to date has helped deliver ROI on high-pressure GenAI investments - a safer path to quicker results. This is where the landscape becomes significantly more complex. In 2024 alone, there was a staggering 400 percent increase in AI core vendors (ref: Pitchbook, Generative AI Emerging Space - Q3 2024). Add to this the explosion of companies offering various AI infrastructure products to build out a production pipeline. This explosion of options has made it incredibly challenging for enterprises, venture capitalists, analysts, and everyone else tracking the AI market to confidently navigate the market and make informed decisions and provide objective guidance.

From what we’ve seen, some key factors have come to matter in this process, as we’ve worked with many organizations on this journey:

Flexibility: The chosen platform should be able to integrate seamlessly with existing enterprise systems and workflows. It should also offer the flexibility to "Bring Your Own" components and provide variations to support diverse use case needs.
Scale: Transitioning from proof-of-concept to production is a critical hurdle. Large organizations need a platform that can scale effectively, supporting not just a few applications but potentially hundreds, as generative AI's impact is felt across every function and industry. The complexity of managing a large-scale RAG deployment should not be underestimated.
Security: Robust security measures are paramount when dealing with sensitive data. The platform must ensure data privacy, granular access controls, and compliance with relevant regulations. And transparency.
Environment-agnosticism: to be able to run the same vendor platform as a SaaS, in private cloud (multiple flavors), and on prem - again the flexibility to meet high-ranked priorities.
Future-Proofing and Adaptability: The selected platform should not only meet current needs but also anticipate future technological advancements, such as Agentic AI. It should seamlessly integrate with ongoing enterprise transformation initiatives, ensuring long-term relevance and utility.
Vendor Viability and Strategic Partnership: Organizations should carefully assess the long-term viability of potential vendors. This involves looking beyond immediate offerings to consider factors like financial stability, a clear product vision and roadmap that emphasizes an end-to-end platform rather than just a single feature. Further, proven scalability, and the depth of the vendor's team expertise seems to weigh heavy, especially when in-house skills are costly or sparse.

In today's rapidly evolving AI landscape, choosing a partner capable of truly evolving with the enterprise's growing AI needs, rather than just offering a 'post card' version of tech, is absolutely crucial to avoid RAG sprawl. This ensures you're not just buying a tool (or a feature) but forging a strategic partnership that will stand the test of time and innovation through this transformative era.

Vectara has emerged as a reliable partner, acknowledged and trusted by both Fortune 500 and Industry Analysts. Vectara’s focus on providing a trusted AI end-to-end platform that enterprises can grow and scale with, along with high focus and innovation around accuracy and hallucination prevention - in each step of the fully integrated RAG pipeline - has put them on the forefront as a platform to standardize on and vendor partner with. By prioritizing accuracy, security, scalability, and adaptability, Vectara aims to help enterprises navigate the complexities of RAG and achieve their AI transformation goals - and address RAG Sprawl. To learn how Vectara can help your organization reduce RAG Sprawl, schedule time to chat with an advisor.