O'Reilly Report

Why Distributed SQL Is the Modern Foundation for AI
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Simplifying and Accelerating the Development of Resilient AI Applications

AI behaves differently in production than it does in a pilot. Traffic is unpredictable, data is scattered across separate systems, and things that worked fine in testing start to break under real concurrency and scale. 

Download this complimentary O'Reilly report to learn the architectural decisions that keep AI systems consistent and scalable in production.

Discover how to resolve shared-state failures, draw the line between advisory and authoritative AI output, and scale storage and compute independently as traffic grows.

Walk away with an action plan of how to keep transactional data, vector embeddings, and unstructured content working from one consistent, current source of truth.

Download the Report

About the Report

Why Distributed SQL Is the Modern Foundation for AI explores why organizations that plan for linear growth get caught off guard by AI's exponential pace, and why a traditional build-to-last mindset doesn't meet today's AI-ready requirements.

What You'll Learn

  • The AI Growth Curve
    Why AI capability and demand grow exponentially instead of linearly, and why a build-to-last mindset breaks down as a result.

  • Architectural Principles for AI Applications
    How outcome-driven design and human-in-the-loop review turn non-functional requirements, like latency, replication, and scaling, into core system requirements instead of afterthoughts.

  • The Four Considerations for Production AI
    What it takes to navigate evolving AI tech stacks, diverse and unstructured data sources, and brittle handcrafted pipelines, plus the scaling decisions that determine whether any of it survives real traffic.

  • Governance, Security and Observability
    Keep sensitive data protected, from Row-Level Security and encryption to comprehensive audit logging and enterprise-grade controls for AI-era access patterns.

  • From Prototype to Production-Ready AI
    What production actually demands that a pilot never does: continuous ingestion, concurrent queries, strict latency targets, and correctness maintained across distributed nodes without rewriting application logic.

  • Scaling to Billions of Vectors
    How native vector types, ANN indexing tuned for recall and latency, and automatic sharding and rebalancing scale embeddings into the billions without standing up a separate vector store.

  • Plus much more!

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