Artificial intelligence has moved from experimental to mission-critical faster than anyone predicted. According to Flexera 2025 data, 68% of enterprises now classify AI as mission-critical, 69% are actively repatriating AI workloads from public cloud, 53% plan to build new AI applications in private cloud, and 41% of AI training already runs in colocation facilities.

These numbers tell a clear story: enterprises have hit the limits of public-cloud economics, performance, and control when it comes to serious Artificial Intelligence workloads.

Where Enterprises Are Today

Public cloud delivered speed and elasticity in the early days of AI experimentation. But production-scale training, inference at the edge, and data-sovereignty requirements have changed the equation.

Massive GPU clusters are expensive and often oversubscribed in hyperscaler regions.

Data egress fees, variable pricing, and latency-sensitive inference workloads erode the “pay-as-you-go” advantage.

Compliance, security, and IP protection concerns push organizations toward environments they can fully own and audit.

The result? A quiet but accelerating repatriation trend. Organizations are pulling high-value AI workloads back to infrastructure they control — either dedicated private cloud or retail colocation with direct access to high-density power and cooling.

The Rise of Private Cloud & Colocation for Artificial Intelligence

Private cloud gives you the isolation, predictability, and performance of on-premises infrastructure with the operational simplicity of cloud. Colocation delivers bare-metal access to the carrier-neutral facilities that have the capabilities to support workloads of any density. Plus, efficiency and performance with 100% renewable power options and sub-2ms latency to major internet exchanges.

Together, they form the foundation of AI-ready infrastructure — purpose-built environments that deliver:

  • Predictable, flat-rate pricing (no surprise egress bills)
  • Single-tenant isolation and full root control
  • Right-sized GPU density with liquid cooling readiness
  • Direct peering to public cloud for true hybrid flexibility
  • Compliance certifications (PCI-DSS, HIPAA, SOC 2, ISO 27001)

Why Storage Is the Make-or-Break Factor in Artificial Intelligence Infrastructure

As AI workloads scale, storage becomes the single largest cost driver — and the biggest potential bottleneck.

Here are the non-negotiable characteristics that separate production-grade AI infrastructure from everything else:

Performance: Predictable and consistent performance is essential. AI inference and training demand low-latency, high-bandwidth access. The only way to guarantee this is all-flash storage for both file and object data.

Reliability & Data Protection: Built-in protection against individual component, rack, or even site failure — with zero data loss or downtime.

Security: Enterprise-grade, best-practice security baked into the storage layer.

Native Kubernetes Integration: Because Kubernetes is the de-facto platform for modern AI/ML, storage must offer native K8s support out of the box.

Self-Service ML Ops Acceleration: Data scientists and ML engineers need instant, declarative access to storage, vector databases, and ML services — without tickets or waiting.

Non-Disruptive Scalability: Add capacity and performance on the fly as training datasets grow from terabytes to petabytes.

Operational Simplicity: Zero complex tuning required. Simple configuration = faster time-to-value for every AI project.

Cost Predictability: Storage cost must scale linearly with capacity — without sacrificing performance, protection, or features.

Power Efficiency: Every watt saved on storage is a watt available for more GPUs. Power-efficient, high-performance storage directly increases the number of accelerators you can run in the same rack or data-center footprint.

Building for Where Artificial Intelligence Is Going

The next 12–24 months will see AI workloads grow even more demanding: multimodal models, real-time inference at the edge, agentic systems, and sovereign AI initiatives. Organizations that wait for public-cloud capacity to catch up will lose competitive ground.

Forward-looking enterprises are designing AI-ready infrastructure now with three principles in mind:

Performance-first architecture — direct GPU-to-GPU fabrics, low-latency networking, and future-proof power/cooling.

Sustainable operations — renewable energy, right-sized workloads, and efficient utilization.

Hybrid by design — seamlessly burst to public cloud when needed, but core training and sensitive inference stay private.

How Opus Interactive Delivers Artificial Intelligence-Ready Infrastructure Today

At Opus Interactive, we’ve spent 20+ years building this kind of infrastructure in Tier III+ data centers in Oregon, Texas, and Virginia.

Our Dedicated IaaS and Private Cloud offerings give you:

  • Single-tenant bare-metal and virtualized environments
  • Predictable billing and 24×7 U.S.-based expert management
  • Seamless hybrid connectivity to AWS, Azure, Google Cloud, and on-prem

Our Cloud Storage Services offer:

  • Non-disruptive, independent scaling of performance and capacity with FlashArray™ and FlashBlade™ – accelerating AI model training and inference without downtime or data movement
  • Unified all-flash consolidation of multiple data sources for simplified operations and strengthened protection with built-in replication and snapshots
  • DirectFlash™ Modules deliver <1W/TB power consumption and up to 95% less rack space, freeing power and footprint for more GPUs

Our Retail Colocation services deliver:

  • 100% renewable power options (Oregon)
  • FISMA-High rated facilities (Virginia)
  • Flexible power densities for today’s and tomorrow’s AI hardware
  • Direct fiber to major cloud on-ramps

Whether you need a few racks for inference or a multi-megawatt GPU cluster for training, we design, deploy, and manage the environment so your team can focus on models — not racks.

The Bottom Line

AI is no longer a side project. It’s core to competitive advantage. The enterprises winning today are the ones that moved beyond the public-cloud default and built (or partnered for) infrastructure purpose-built for AI — secure, performant, sustainable, and fully under their control.

If your organization is evaluating where to run next-generation AI workloads, now is the time to explore private cloud, storage, and colocation options that were designed for exactly this moment.

Ready to future-proof your AI infrastructure? Email a team member at sales@opusinteractive.com.