If you're building AI features into your applications, you're probably arriving at the same crossroads as everyone else: “Do I add another database to my stack, or find a way to make my existing infrastructure work smarter?”
A new report from industry analysts at Intellyx tackles this tricky dilemma. "The New Blueprint for AI-Ready Data: Vector Search Meets Distributed SQL" makes a case that may sound counterintuitive at first – that you can get enterprise-grade vector search without actually running a vector database.
The report explores why the current approach of bolting specialized vector databases onto existing stacks creates more problems than it solves. It also reveals an alternative that's gaining traction among platform engineers and data teams.
The vector database gold rush (and its hidden costs)
Vector databases have exploded in popularity, and for obvious reasons. They're what make semantic search work, power the LLM integrations that make AI assistants so effective, and drive the recommendation engines that understand users. Instead of crude keyword matching, they let you search by meaning – it’s how you ask for "acoustic folk music" and get "unplugged indie ballads" in your results.
Here's where the architecture often goes awry, however: Most organizations end up running vector workloads separate from their operational data, in a standalone vector database. That spawns constant syncing between systems, brittle ETL pipelines that break at the worst moments, and the threat of stale or inconsistent data.
As Cockroach Labs Co-Founder Peter Mattis puts it: "By isolating vector search in its own database, you push the responsibility for combining data into the application layer - adding complexity and creating potential staleness and consistency headaches."
Beyond the complexity, there's a deeper issue. The report states that specialized vector systems rarely offer the operational resilience that distributed SQL databases provide. An enterprise’s AI features might be fast, but they often prove fragile when it comes to the kind of uptime and consistency that enterprise applications demand.
Enter C-SPANN: distributed vector indexing
Rather than forcing teams to choose between operational reliability and AI capabilities, Cockroach Labs developed something different: C-SPANN (CockroachDB SPANN), a distributed vector indexing engine that brings high-performance similarity search directly into the distributed SQL database CockroachDB.
“The New Blueprint for AI-Ready Data” explains how this approach builds on advanced algorithms, including Microsoft's SPANN and SPFresh, but adapts them for the unique challenges of distributed SQL environments. The result is a system that delivers:
Incremental updates so new vectors become searchable instantly, without rebuilding entire indexes
Fully distributed indexing across nodes with no central-coordination bottlenecks
Resource efficiency that handles massive workloads without infrastructure bloat
Predictable low latency through smart hierarchical search and reduced network hops
Elastic scaling that supports billions of vectors using native CockroachDB data distribution
"With C-SPANN, CockroachDB can run vector search alongside operational data without sacrificing performance, freshness, or resilience," explains Dikshant Adhikari, Senior Product Manager at Cockroach Labs. You get vector database performance, without the compromises of running a standalone vector database.
Why a unified architecture changes everything
When enterprises bring vector search into their distributed SQL system, they don't just reduce complexity – they unlock capabilities that aren't possible with separate systems:
Query everything at once – Filter and join vector results with structured data using standard SQL. No more awkward context-switching or fragile cross-system joins.
Maintain real consistency – Insert vector embeddings and metadata in a single transaction, eliminating mismatched or orphaned records.
Stay current automatically – Because indexing happens natively and incrementally, your AI features always reflect real-time business data.
Scale globally – CockroachDB's geo-distributed architecture keeps AI features moving fast under worldwide traffic or during regional outages.
Real applications, real impact
The report highlights practical scenarios where unified AI and operational data create genuine business value:
Retail recommendations that use vector similarity to suggest products, then filter by location, user preferences, and purchase history – all in a single query.
Insurance image analysis that lets AI compare damage photos across claims while accessing time-series data, text records, and user metadata stored in the same system.
Banking chatbots that combine RAG-style semantic search with secure access to customer account data, without complex synchronization between separate stores.
These aren't theoretical use cases. They're the kind of intelligent, personalized features that users expect and that unified architecture makes possible.
The trend is clear
Every major database vendor is adding vector support, and here’s why: The distinction between transactional and AI workloads is disappearing.
As Cockroach Labs Fellow Andy Kimball notes in the report: "Rather than shuffling data between separate systems, it's far more intuitive to bring vector indexing into the distributed SQL database you already trust."
Organizations using CockroachDB and C-SPANN are proving this approach works. They’re building next-generation AI applications without rearchitecting their stacks or compromising on operational control.
Get the full blueprint
“The New Blueprint for AI-Ready Data” covers the technical challenges of vector indexing in distributed environments, how C-SPANN's novel approach solves them, the business benefits of unified architecture, and practical examples of what this enables in production.
If your team is evaluating vector databases or figuring out how to support generative AI at scale, this 12-page analysis from Intellyx offers a different path forward. It's an essential resource for enterprises that require operational simplicity alongside AI .
Download the full report here and see how to unify your AI data stack.
David Weiss is Senior Technical Content Marketer for Cockroach Labs. In addition to data, his deep content portfolio includes cloud, SaaS, cybersecurity, and crypto/blockchain.
