Oct 9, 2025
12-15 Mins
Why Colocation is the Smartest First Step for Your AI Strategy Under the New Action Plan
Colocation is the go-to option for businesses that require high levels of customization, control, performance, and security for their AI workloads. Colocation can deliver faster setup times, and a more tailored and resilient foundation with dedicated infrastructure that maximizes cost efficiency and long-term ROI.
As governments establish new AI standards and hyperscalers race to expand capacity, colocation stands out as the most effective way for enterprises to build stable, high-performance environments aligned with strategic and compliance goals.
This post breaks down why colocation is the most strategic first step for AI infrastructure, what to look for in a provider, and how to get started.
Why the Action Plan Makes Infrastructure a National Priority
Governments worldwide are shifting from talking about AI infrastructure to actually building it. Under the new AI Action Plan, the U.S. is making infrastructure a top priority for business and national security. Similarly, the European Union’s parallel strategies are pushing for massive increases in data center capacity, streamlined regulation, and upgraded energy systems. For companies planning AI work, this means infrastructure is no longer optional or peripheral… It’s central.
What the U.S. AI Action Plan Says and Why It Matters
The Action Plan lays out three pillars, one of which is Building American AI Infrastructure.
Under that pillar, the plan specifically calls for:
Technical standards for high-security AI data centers. This means secure, resilient facilities for sensitive or government/defense workloads.
Permitting reforms: speeding up approvals for data center builds, energy infrastructure, and semiconductor manufacturing. This includes changes to environmental reviews (e.g., NEPA), plus reducing delays under Clean Air / Clean Water laws.
Power and grid reliability: increasing the electric grid’s ability to support large, dense compute loads without blackouts or supply interruptions. The plan includes incentives and procurement for clean power and advanced grid tech.
There’s also a focus on security, compliance, and sovereignty: ensuring that infrastructure (hardware, networking, data centers) adheres to guardrails to protect against foreign adversaries, along with stricter oversight of AI systems used in critical and high-risk contexts.
Global Context and EU Strategy
The EU is matching pace. Under its AI Continent Action Plan, the European Commission aims to triple EU data center capacity in the next 5-7 years. This is to close the gap with countries like the U.S. and China.
And it’s not just about build volume. The EU is tying capacity to sustainability, energy use, and regulatory harmonization. Any new capacity will be expected to meet high standards around energy efficiency, water use, cooling, and environmental impact.
The EU is also pushing regulatory reforms: proposed Cloud and AI Development Act, service desks to help businesses navigate new AI laws, faster permitting in certain zones, and incentives (public funding, streamlined licensing) for providers building high-security infrastructure.
Why These Policy Moves Matter to You (AI Teams / Enterprise Leaders)
These legal and regulatory shifts are making infrastructure non-negotiable. Compliance, energy sourcing, and security certifications now carry as much weight as raw compute.
What used to be operational hurdles such as permitting, grid interconnects, or power availability are quickly becoming strategic risk factors. In some regions, those bottlenecks could determine whether your AI workloads can even go live.
At the same time, new subsidies, tax incentives, and government programs are reshaping the economics of where to build. Certain colocation zones and high-security data centers may soon deliver better performance and lower costs than doing it all in-house.
The Immediate Problem for Enterprise AI Teams
Enterprises trying to build or launch serious AI capabilities now face a set of urgent, interlocking bottlenecks. These aren’t future problems either. Many are already impacting teams that want real performance, compliance, and speed.
Bottleneck 1: GPU and Rack Capacity
AI workloads demand huge numbers of GPUs. There’s often not enough supply of GPU hardware (and related supplies like high-bandwidth memory) to scale models or train large LLMs.
Enterprises are finding that even cloud providers or internal divisions can’t always get GPU allocations when needed. For example, Amazon’s “Project Greenland” was created because Amazon’s own business units were delayed for months by GPU shortages.
And rack space is also constrained. AI racks need not just physical space but also very high power and density. That limits how many usable racks exist in many data centers today.
If you are experiencing a long wait time on GPUs or difficulties finding a fast and reliable source to get the hardware you need, reach out to Inflect’s GPU expert now.
Bottleneck 2: Power and Advanced Cooling
AI racks consume enormous amounts of power. As power per rack climbs, data centers need to upgrade both their electrical infrastructure and cooling systems to keep up. Options include advanced air cooling, liquid cooling, or full immersion cooling, each designed to prevent overheating and sustain performance. Digital Realty explores the future of data center cooling here.
Many existing or legacy data halls were not built for such high-density workloads. The Uptime Institute’s 2024 Global Data Center Survey found that nearly 30 percent of operators are actively upgrading facilities to handle more concentrated compute.
Cooling isn’t only about removing heat. It’s also about efficiency and sustainability. Rising electricity costs, water usage, and environmental impact all add to the challenge. Inefficient cooling can lead to hotspots, equipment failure, and throttled performance, each a potential bottleneck to AI scale.
Bottleneck 3: Connectivity and Low-Latency Access to Clouds
For AI workloads, proximity to major cloud providers and network backbones isn’t just convenient; it’s critical. Latency, data transfer costs, interconnect stability, and overall performance all depend on location. Place your compute in a remote or poorly connected site, and efficiency suffers.
This is why direct cloud on-ramps and carrier-neutral interconnects are in such high demand. Colocation facilities provide them, along with stronger network fabrics and multiple carrier options. These are advantages that most enterprises cannot match in a private build-out.
Bottleneck 4: Compliance and Sovereignty
Many jurisdictions have passed or are passing laws that require certain sensitive data to remain within the same region, or only processed or stored under certain controls. Terms like GDPR (Europe), CCPA (California), HIPAA (healthcare), and others enforce strict rules on data residency, encryption, access control, auditability, etc.
Companies need to weigh more than just where their data resides. They need to know who controls it, what certifications the data center holds, and whether cross-border data flows are legal and secure. Mistakes in sovereignty or compliance can result in regulatory fines, legal exposure, and lasting reputational damage.
Market Evidence: Surging Construction, Limited Supply
McKinsey reports that demand for AI-ready data center capacity is expected to grow at ~33% per year between 2023 and 2030 in a midrange scenario. By 2030, around 70% of total data center capacity demand will be for sites equipped for advanced AI workloads.
BCG (Boston Consulting Group) also estimates global data center power demand will rise ~16% CAGR from 2023-2028, driven strongly by AI and GenAI workloads.
There are also supply chain constraints for GPUs, high-bandwidth memory, and cooling components are under pressure. Manufacturers are struggling to keep up.
Why These Problems Matter to You
If you try to build AI infrastructure the “old way” (buying hardware, putting it in some facility, hoping power will scale, and cooling will hold), you may find delays of many months or cost overruns.
Relying too heavily on cloud only can cause unpredictable costs (especially as training usage spikes) or latency issues. Plus cloud may not meet your compliance needs or allow data residency.
If you do nothing, you’ll likely find yourself competing for limited capacity, paying premiums, or being forced into suboptimal locations or providers.
Colocation 101 for AI Teams
What is colocation?
At its core, colocation is renting space in a purpose-built data center to house your IT infrastructure. Instead of building your own facility, you lease racks, cages, or private suites inside a highly secure, professionally managed environment.
Why it matters for AI:
AI workloads demand infrastructure that’s powerful, scalable, and always available. Colocation provides the foundation without the cost or delays of building from scratch.
Key features for AI teams:
High power density – Supports racks loaded with GPU servers that would overwhelm most on-prem setups.
Liquid cooling options – Many colocation providers now offer immersion or direct-to-chip cooling to handle the intense heat of AI hardware.
Carrier-neutral interconnect – Direct access to cloud providers, network carriers, and partners, so you can move data where it needs to go fast.
Retail vs. wholesale colocation:
Retail colocation gives you individual racks or small cages. It’s flexible, fast to deploy, and perfect for early-stage AI projects.
Wholesale colocation offers large suites or entire data halls. It’s ideal once your AI infrastructure scales and you need full control over power, cooling, and design.
Why Colocation is the Smartest First Step Right Now
Enterprises under pressure to launch AI workloads quickly need an option that balances speed, flexibility, and compliance. Colocation offers a proven middle ground between building your own facility and going all-in on cloud. Here’s why it’s the smartest move today.
Time to Value: Deploy in Months, Not Years
Building a greenfield data center can take 3–5 years, time most AI teams don’t have. With colocation, you can secure capacity, racks, and network connections in a matter of months. Providers already have the power, cooling, and security infrastructure in place, so your focus can shift from construction delays to getting workloads online.
Cost Control: Avoid Heavy Capex and Unpredictable Cloud Bills
AI training clusters demand expensive infrastructure. Instead of locking up capital in land, buildings, and utilities, colocation lets you invest in GPUs and networking while paying only for the space and power you consume. Compared with cloud, colocation provides predictable monthly costs and eliminates the risk of surprise overages when workloads spike.
Performance and Connectivity: Direct Access to What Matters
Many colocation facilities sit inside dense network ecosystems with multiple carriers, ISPs, and cloud onramps. That proximity reduces latency for model training and inference while cutting data transfer costs. Enterprises can interconnect directly to hyperscalers and still keep critical workloads in a controlled environment.
Compliance and Sovereignty: Built for Enterprise Standards
Colocation providers offer facilities certified for standards like SOC 2, ISO 27001, HIPAA, and PCI DSS, giving you a head start on meeting regulatory requirements. For AI workloads involving sensitive data, location matters: with colocation, you can choose facilities within the right jurisdiction to meet sovereignty rules while retaining control over access policies.
Future Proofing: Vendor-Neutral Flexibility
Unlike proprietary cloud services, colocation gives you a vendor-neutral base that supports hybrid and multi-cloud growth. As your AI strategy evolves, you can shift workloads between providers or scale into new regions without being locked into one platform.
How to Evaluate a Colocation Partner for AI
Not every data center is built for the demands of AI. Use this checklist to evaluate whether a colocation partner can support your current needs and scale with you as AI workloads grow.
Power per Rack (kW)
Look for providers that can support high-density racks - 15–50kW or more, depending on your GPU clusters.
Ask about both standard rack densities and the ability to customize power delivery for future expansion.
Cooling Options (Air vs. Liquid)
Traditional air cooling may not be enough for dense GPU clusters.
Ensure the provider offers advanced cooling options, including liquid cooling or immersion cooling, and confirm whether those solutions are available now or “on roadmap.”
Network Ecosystem (Carriers, Clouds)
Proximity to carrier-dense meet-me rooms and direct cloud onramps lowers latency and transfer costs.
Evaluate how many carriers and hyperscale cloud providers are already connected to the facility.
Certifications (ISO, SOC, HIPAA, etc.)
Confirm the facility meets the compliance needs of your business (SOC 2, ISO 27001, HIPAA, PCI DSS, FedRAMP, etc.).
Certifications demonstrate that security and operational standards are audited and enforced.
Contract Flexibility and Timeline to Deploy
Ask about deployment timelines. Can they deliver space and power in weeks or will you wait months?
Flexible contract terms (shorter commitments, expansion options) are important in a fast-moving AI landscape where requirements may change.
The First 90 Days in Colocation for Your AI Program
The first three months in a colocation environment are all about momentum and turning plans into live infrastructure that can handle AI-scale workloads.
Weeks 1–4: Secure your contract, reserve space, and order hardware. This phase sets the foundation for everything that follows. Making sure lead times for power, network connectivity, and delivery are aligned can save weeks later in the process.
Weeks 5–8: Rack and stack your systems, configure networking, and begin initial testing. At this stage, collaboration between your hardware, networking, and software teams becomes critical to validate power draw, connectivity, and cooling performance.
Weeks 9–12: Ingest your data, launch pilot workloads, and establish performance baselines. This is where you start to measure how your infrastructure performs under real AI workloads and make adjustments to optimize efficiency and throughput.
Key metrics to track: cost per training hour, utilization rates, and time to production. These KPIs help benchmark success and reveal early opportunities for optimization.
Case Studies and Use Cases
These real-world examples show how enterprises and hyperscalers are already reaping the benefits of colocation. They also illustrate use cases that likely match situations facing many AI teams right now: speed, compliance, cost predictability, and scaling.
Example 1: Enterprise Accelerating AI Projects with Colocation
SingleStore is a good example. As their platform grew to power real-time analytics, vector workloads, and generative AI for customers in finance, tech, and media, they found cloud costs becoming unsustainable and visibility/optimization lacking. Moving to a hybrid model with Evocative Data Centers allowed them to offload core infrastructure into colocation. This improved efficiency, cut cost overhead, and gave them more control over performance and governance.
Example 2: Hyperscaler / Partner Outsourcing to Colocation
Hyperscalers are making deals for massive wholesale colocation leases (100 MW+) as they race to lock in compute, power, cooling, and interconnect in key global markets. Deals of this size change how data center campuses are designed, financed, and delivered.
These hyperscaler-colocation relationships aren’t about renting small racks. They’re about getting anchor capacity, securing power ahead of grid constraints, and future-proofing for exponential AI growth.
Use Cases: What Colocation Makes Possible
Faster model iteration - Enterprises can get hardware and infrastructure in place faster, run experiments locally (or hybrid), and iterate without waiting for cloud provisioning. Colocation reduces “infrastructure friction.”
Compliance and sovereignty - With data laws tightening, having physical control over where hardware sits, access procedures, certifications, etc., gives enterprises the guardrails they need.
Predictable costs - Instead of variable cloud bills, colocation offers more steady operating expenses. You pay for space, power, cooling, etc. Many large enterprises find that for steady usage, colocation becomes much more cost-efficient.
Conclusion
The new AI Action Plan makes one thing clear: infrastructure is no longer an afterthought. For enterprises serious about building and scaling AI, colocation offers the fastest, safest, and most cost-effective path forward. It delivers the power, cooling, compliance, and network density required to run high-performance workloads without the delays of building from scratch or the unpredictability of going all-in on cloud.
That said, colocation isn’tthe perfect fit for every organization. Small teams running lightweight AI models may be better served by staying in the cloud. Fully cloud-native strategies can also work if compliance, performance, and cost predictability aren’t major concerns. But for the majority of enterprises aiming to align with the Action Plan and launch AI initiatives at scale, colocation is the smart first step.
Now is the time to assess your options and secure capacity before demand surges further.
About the Author
Trevor Hopkins
Account Manager at Inflect
Trevor is an expert in the digital infrastructure industry with a proven track record of helping buyers navigate complex markets—whether building next-gen data centers, expanding global networks, or evaluating compliance-heavy workloads like blockchain. He shares insights and observations drawn from practical experience and real cases, writing at the intersection of technology, regulation, and the systems that keep the internet running.
Contact:
Email:
trevor.hopkins@inflect.com
https://www.linkedin.com/in/trevor-hopkins-2ab3ba201/