From data silos to RAG Sprawl: why the next AI revolution needs a standard platform
History is repeating itself in AI. Just as enterprises once built siloed databases before embracing centralized data platforms, today’s rapid adoption of Retrieval-Augmented Generation (RAG) is leading to a fragmented mess of custom-built AI systems—what we call RAG Sprawl.
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When I look at what’s happening in Retrieval-Augmented Generation (RAG) today, I can’t help but feel a strong sense of déjà vu. I’ve seen this movie before.
Back when I was the CTO of Cloudera, I had a front-row seat to the rapid evolution of the big data world. In the early days, we had purpose-built data marts—one per team, per use case. Then came Hadoop and Spark, which democratized big data at scale. Eventually, we saw a new breed of cloud-native platforms like Snowflake and Databricks emerge, enabling centralized, governed, and highly elastic data lake architectures.
But let me take you back further. When I first started learning about the data space almost 40 years ago, everyone was building their own database systems from scratch. You had to create your own data storage layer, query indexing,, query engine, query execution, schema+access enforcement, and middleware to stitch everything together—the whole thing. And at the time, it was revolutionary! These systems allowed teams to finally get real value out of their data. But they didn’t scale. You’d end up with siloed solutions, duplicate infrastructure, runaway costs, and worst of all—engineers who left their jobs, taking tribal knowledge with them.
Fast forward to today, and no one builds their own database anymore. They use Oracle, Hana, Spanner, Cosmos, Aurora, and Snowflake—battle-tested, secure, well-supported systems with a full control plane for data governance, security, lineage, performance, and compliance. We now treat data infrastructure as infrastructure. Standardized. Centralized. Scalable.
We are now entering that same transitional moment—but this time, with RAG.
The rise of RAG—and the sprawl
Retrieval-Augmented Generation is powerful. It lets you bring your organization’s knowledge to large language models in a safe, grounded, and controllable way. It’s the bridge between private knowledge and public intelligence. Understandably, companies are rushing to implement RAG systems—often starting with a bespoke setup for a single AI assistant or agent.
At first glance, it seems simple enough. You grab an open source vector database, grab an embedding model from HuggingFace, hook them up to an LLM, and you’re off to the races. But then comes the second project. And the third. Before you know it, you’ve got RAG Sprawl—a term we’re now hearing frequently from our customers.
RAG Sprawl is what happens when every team builds its own custom RAG stack. Different frameworks. Different Language Models. Different vector databases. Different security mechanisms. No centralized control plane for IT. No versioning, no audit trail, no enterprise governance, no departmental inference cost tracking. And critically—no way to explain or reproduce what your AI assistant or agent just told a customer.
This is fine when you're building one assistant. But we foresee a future where every application in the enterprise will need a RAG system under it to imbue it with intelligence, in the same way it requires a database system under it for data processing. Enterprises will not build one. They will be building dozens. Hundreds. Agents that interact with users, with systems, with each other. The cost of ad-hoc RAG becomes untenable. Innovation slows. IT drowns. Trust erodes. And this will happen really fast.
Toward a standard platform for RAG
What companies are telling us now is clear: they want a standardized way to build RAG systems—a platform approach that brings the same benefits to enterprise AI that platforms like Snowflake and Databricks brought to enterprise data.
An analogy I like to use is to think of the LLM as the CPU. It’s powerful, but it needs a motherboard. It needs storage. It needs cooling, memory, I/O—all the components to turn that raw power into usable computing. Enterprise RAG is that supporting structure, it is the scaffolding around the LLM that makes it sing accurately, securely, and repeatably. It is what makes the value of the system way more than the sum of the parts.
At Vectara, we’re helping companies build this repeatable foundation. A unified platform that handles powerful multi-modal retrieval, reranking, grounding, summarization, explainability, quality guardrails, and security—so developers can focus on what matters: building amazing AI assistants and agents that scale. With a common interface. While simultaneously empowering the RAG administrators with comprehensive security. With full transparency and control. Across on-premises or the cloud.
This is how we avoid RAG Sprawl. This is how we scale AI responsibly. And this, I believe, is the key to unlocking the full promise of agentic AI—systems that don’t just answer questions, but act on your behalf in a trustworthy and auditable way.
A future without sprawl
It’s been a long journey from hand-built databases to the modern data cloud. I see the same arc unfolding now in the world of AI assistants and agents. We’re moving from the age of DIY RAG toward the era of Enterprise RAG Platforms—and the benefits will be just as transformative.
As someone who’s experienced firsthand the big data revolution, I couldn’t be more excited to guide you through this next transformative wave.
