AI-ready data center infrastructure
that adapts to the future.

Stream’s standardized-but-configurable approach to design and construction — and our flexible hybrid cooling solution — enable customers to late-bind their decisions, adapting to configuration changes without introducing added risk or ballooning build timelines. Our powered shell options also deliver high levels of flexibility.

Stream’s flexible access to long-term capital and land bank of vetted sites developed for data center use prep projects for certainty and success up front while preserving flexibility across development thanks to adaptable design, construction and operations strategies.

Yes. Our approach creates third-party capacity that feels self-developed.

A data center is AI ready when it can match the speed, density and flexibility requirements of modern GPU workloads. That requires high‑density power configurations, flexible electrical and mechanical systems and a design approach that can scale quickly as AI requirements change. AI readiness also means providing adaptable cooling strategies, including hybrid air and liquid solutions, that can shift seamlessly as rack densities rise or as data hall usage is reallocated.

Stream strengthens this foundation with standardized yet highly configurable design frameworks, a vetted national land bank, a 4 GW power pipeline across the United States and a resilient supply chain that absorbs shifting forecasts and late‑stage specification changes. For us, being AI ready means giving hyperscale customers the certainty, control and optionality they need to build confidently in a rapid and unpredictable environment.

AI-ready data centers are built to handle much higher-density, GPU-heavy AI workloads than traditional facilities, so they differ most in cooling architecture, power density, and network design.

• Cooling Design: Traditional data centers are predominantly air-cooled, which works for mixed enterprise workloads but struggles beyond roughly 5–10 kW per rack. AI-ready sites like Stream’s use configurable hybrid cooling that supports both air and direct liquid cooling in the same hall.
• Power Density: A typical traditional rack often runs in the 5–10 kW range, while AI racks commonly reach 100+kW and are rapidly moving toward even greater capacities. AI-ready data centers are therefore engineered with much higher-capacity power distribution and cooling plants sized for multi‑megawatt AI halls.
• Flexibility for AI: Traditional AI data center facilities are usually planned around relatively stable, general-purpose workloads, so changes in thermal profile often require expensive retrofits or stranded capacity. AI-ready designs emphasize modularity, allowing for greater flexibility.

AI data center infrastructure must support very high-power density, often reaching 100+kW per rack. It also requires advanced liquid cooling systems to manage heat produced by large scale compute resources. Processing is typically handled by high-performance computing clusters that rely on accelerated processors. Essential elements include low latency networking, high-speed storage systems, and a strong and scalable power architecture, all designed to meet the demands of intensive training and inference workloads.

AI optimized data centers often rely on a combination of air cooling and liquid cooling to support both conventional information technology systems and high-density AI workloads. Air cooling remains effective for storage and general cloud applications, while AI hardware often requires liquid cooling because it provides far more efficient heat transfer. Since future computing demands are difficult to predict, many modern data centers are built with modular and configurable cooling systems that can accommodate any mix of air or liquid cooling. This flexibility allows facilities to scale, adjust to changing heat capture requirements, and avoid unnecessary spending or overbuilding of infrastructure. However, some cooling solutions allow for more flexibility and seamless transitions than others. For deeper insights, please read, “How Configurable, Modular Hybrid Cooling Systems Solves for Hyperscale Uncertainty”

Many AI-ready data centers handle massive, shifting inference power demands by using high-density infrastructure, including advanced liquid cooling, localized on-site power generation (e.g., fuel cells, microgrids), and Battery Energy Storage Systems (BESS) for rapid, millisecond-level surge management. Data centers often have intelligent energy management systems to optimize efficiency and reduce grid dependency.

The most important certifications for AI-ready data centers are those that prove reliability, security, energy performance and sustainability. Key standards include Uptime Institute Tier certifications for facility resistance, ISO 27001 for information security, ISO 50001 and ENERGY STAR for energy management, and ISO 14001 for environmental performance. SOC 2 Type II is also widely required for compliance and operational trust. Together, these certifications validate that a data center can support high-density workloads safely, efficiently and with consistent uptime.

Real-time AI processing depends on network architectures that provide very high bandwidth and very low latency, using designs that resemble spine and leaf topologies and that support dense traffic flows within the data center. These systems rely on fast interconnect technologies to keep hop counts low and reduce congestion, allowing accelerated processors to exchange information quickly during both model training and inference. AI-focused data centers also apply advanced traffic management techniques, remote direct memory access methods, and software-defined networking to maintain consistent performance as clusters grow. The result is a fast and adaptable network fabric that supports tightly coordinated compute workloads without creating bottlenecks.

AI data centers support hybrid cloud AI strategies by providing flexible, high‑performance infrastructure that can run workloads across on‑premises systems, public clouds, and edge environments. They use scalable GPU clusters, high‑speed interconnects, and secure connectivity to move data and models efficiently between platforms. This allows organizations to train models in the cloud, fine‑tune or run inference on‑premises, and shift workloads based on cost, performance, or compliance needs. The result is a seamless environment where AI workloads can run in the best location at any moment without disruption.

Protecting AI workloads begins with a robust physical security posture that utilizes “concentric rings” of protection. This approach—which is employed by Stream—establishes multiple layers of protection at the perimeter, exterior, interior, restricted area, asset, and observation levels to deter, delay, and detect threats. By integrating advanced surveillance, biometric access controls, and strict “least privilege” protocols at every ring, the data center provides a resilient environment that secures the high-density infrastructure required for AI. This holistic approach ensures that while customers manage their specific models and data, the underlying facility remains in a constant state of readiness. For more information about data center security, read “Are You Ready for a Security Breach?”

AI ready data centers are built for scalability so they can support the rapid and unpredictable growth of AI model sizes, including large language models. Industry projections show that total capacity is expected to expand by a factor of six between 2025 and 2035. In contrast with traditional facilities, modern AI ready centers rely on modular and prefabricated designs that enable fast, incremental additions of power and cooling capacity, often in units of 5 megawatts to 10 megawatts, rather than requiring lengthy construction efforts at the outset.