---
title: "Inside the infrastructure strategies propelling AI leaders"
description: "The post examines why enterprise AI efforts can become too expensive, slow, and difficult to scale, citing survey findings that 67% of organizations with disconnected data environments identify storage, movement, and duplication as their largest recurring AI cost. It presents three infrastructure considerations: delivering infrastructure at agentic speeds, streamlining data, and adopting systems built for AI scale. Examples include instant temporary environments with secure rollback and restoration, unified operational and analytical data stored separately from compute in low-cost cloud storage, and elastic scaling that can move from high concurrency to zero in seconds. The conclusion is that open, AI-ready, purpose-built databases can reduce pipeline complexity and costs, support experimentation, and let organizations align spending with unpredictable workloads while enabling faster AI innovation."
---

# Inside the infrastructure strategies propelling AI leaders

[Databricks](https://yomu.fyi/company/databricks) · Christy Maver · Jul 2, 2026

**Type:** Explainer

## Summary

The post examines why enterprise AI efforts can become too expensive, slow, and difficult to scale, citing survey findings that 67% of organizations with disconnected data environments identify storage, movement, and duplication as their largest recurring AI cost. It presents three infrastructure considerations: delivering infrastructure at agentic speeds, streamlining data, and adopting systems built for AI scale. Examples include instant temporary environments with secure rollback and restoration, unified operational and analytical data stored separately from compute in low-cost cloud storage, and elastic scaling that can move from high concurrency to zero in seconds. The conclusion is that open, AI-ready, purpose-built databases can reduce pipeline complexity and costs, support experimentation, and let organizations align spending with unpredictable workloads while enabling faster AI innovation.

## Context

Organizations are accelerating AI efforts but encountering infrastructure that is too expensive, slow, and difficult to scale. Disconnected data environments make storage, movement, and duplication a major recurring cost, while legacy architectures complicate migrations, constrain scaling, and delay production workloads.

## Approach / What changed

The post proposes three infrastructure considerations: provide agentic-speed provisioning with secure rollback and restoration; unify operational and analytical data while separating storage from compute; and use reliable, elastic, purpose-built AI databases that scale independently and align costs with usage.

## Takeaways

- Among organizations with disconnected data environments, 67% cited data storage, movement, and duplication as their largest recurring AI cost; the figure was just over half for organizations with unified data architectures.
- AI-ready databases can unify operational and analytical data, keeping required data available in low-cost cloud storage separately from the compute layer.
- Independent compute scaling can support workloads ranging from high concurrency to zero in seconds, helping align spending with unpredictable AI agent activity.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Architecture](https://yomu.fyi/topic/architecture), [Scalability](https://yomu.fyi/topic/scalability)

- Source: [Databricks](https://www.databricks.com/blog/inside-infrastructure-strategies-propelling-ai-leaders)
- Source URL: https://www.databricks.com/blog/inside-infrastructure-strategies-propelling-ai-leaders
- Ingested by Yomu: 2026-08-30T16:59:25.640Z

[Read original post](https://www.databricks.com/blog/inside-infrastructure-strategies-propelling-ai-leaders)