The Customization Imperative: Why Data Center Solutions Are No Longer Standardized

By Sam Zurzolo, VP of Data Center Services, 365 Data Centers

For years, data center decisions came down to a short list: how much space, how much power, and how much redundancy. Those fundamentals still matter today, but no two enterprises run the same workloads. As a result, a standard cage and a standard power feed rarely fit in most modern scenarios.

After more than three decades in telecom and data center operations, from U.S. Navy electronics to running facilities across the country at 365 Data Centers, one of the biggest changes I’ve seen is the move from standardized offerings to environments built around the customer.

What Changed

Several forces are driving the shift:

Density: AI and data-intensive applications are pushing power requirements well past what legacy facilities were designed for. Some deployments now call for anywhere between 50 to 220 kW per cabinet.
Cooling: Higher densities often mean air-cooled and water-cooled approaches working together, and the right mix depends on the workload.
Workload diversity: Inference, analytics, regulated data, and traditional applications each place different demands on power, latency, and connectivity.
Data control: Security, privacy, and sovereignty requirements increasingly determine where, and how, workloads can run.

Why One-Size-Fits-All Falls Short

A standardized environment forces compromises. Organizations either overbuild and pay for capacity they don’t use, or they underbuild and run into limits just as demand grows. Neither is efficient. Teaching fiber design early in my career taught me that the network path matters as much as the rack it connects to.

Hybrid complexity adds to the problem, because many enterprises now operate across colocation, private cloud, public cloud, and on-premises systems. Without a plan for how those pieces connect, teams end up managing fragmented vendors, contracts, and support models.

From an operations standpoint, customization also affects reliability. Power distribution, cooling design, maintenance schedules, and network paths all work best when they match the actual load. A well-matched environment is easier to maintain and easier to scale.

What to Evaluate

When assessing a provider, consider these questions:

Power and cooling: Can the facility support your density today, and can it grow with you?
Connectivity: Are carrier options, cloud on-ramps, and bandwidth available where your workloads live?
Flexibility: Can you start with colocation and add private cloud or other services later, rather than committing to everything at once?
Compliance: Does the environment meet the security standards your industry requires?
Support: Will experienced people work with you on design and ongoing operations, not just hand you a rack?

Start with your workloads, then work backward to the infrastructure. A clear picture of your current and projected power, latency, and compliance needs makes every other decision easier.

Planning for What’s Next

Customization doesn’t mean complexity. The goal is an environment that fits today’s requirements and can adapt as they change. Enterprises that treat infrastructure as a tailored foundation, rather than a commodity, are better positioned to deploy new technologies with confidence.

At 365 Data Centers, we work with customers to design colocation, connectivity, and cloud environments around their specific needs, with the support of a team that has operated critical infrastructure for decades. If you’re reassessing your data center strategy, we’re glad to help you think it through. Contact us today at https://365datacenters.com/contact/.

From Passive to Active: How Colocation Is Becoming an AI Optimization Engine

Authored by Derek Gillespie, Chief Executive Officer

For years, colocation was defined by what it didn’t do. It didn’t compute, optimize, or touch the workload itself. A customer brought in equipment, the facility provided space, power, and cooling, and the relationship largely ended there. That model is changing quickly, and AI is the reason.

As AI workloads push power and thermal demands beyond what legacy facilities were designed for, colocation providers are being asked to do more than host equipment. They’re being asked to actively shape how that equipment performs. The shift from passive real estate to active infrastructure is one of the more consequential changes happening in digital infrastructure today.

The density problem

A decade ago, a typical enterprise rack drew somewhere in the range of 5 to 10 kW. According to AFCOM’s 2026 State of the Data Center Report, cited by Upsite Technologies, average rack density climbed from 7 kW in 2021 to 16 kW in 2025, then nearly doubled again to 27 kW in 2026, a roughly fourfold increase in just three years. Separately, Uptime Institute’s 2026 Global Data Center Survey found a growing share of operators now reporting peak rack densities of 30 kW or higher to support advanced computing workloads, while AI training clusters built around the latest GPU platforms can require 100 kW per rack or more.

That kind of jump isn’t absorbed by simply adding circuits. It requires rethinking power distribution, cooling, and floor design from the ground up. Air cooling generally tops out around 40 to 50 kW per rack; beyond that, direct-to-chip and liquid cooling becomes necessary rather than optional. It’s a range we see reflected in day-to-day deployments: At 365 Data Centers, cabinet configurations today span roughly 50 to 160 kW, supported by liquid-cooled environments, depending on what a given workload demands.

This is where colocation stops being a neutral container and becomes an active variable in AI performance. A facility that delivers dense, reliable power and modern cooling directly affects how efficiently a customer’s workloads run. One that can’t do so becomes a bottleneck, regardless of how advanced the customer’s hardware is.

From hosting equipment to enabling performance

The practical implications of this shift show up in a few areas.

Power design now has to plan for variability, not just capacity. AI training creates sharp, uneven power draws as GPUs cycle between full utilization and idle states, a different pattern than the steady loads traditional applications produce. Facilities built only for consistent draw can struggle with that fluctuation.

Cooling has become a crucial design decision. Since not every workload needs the same thermal approach, many operators are deploying hybrid environments mixing air-cooled and liquid-cooled solutions within the same facility. That flexibility, more than any single cooling method, is becoming the more durable strategy.

Speed to capacity matters as much as capacity itself. Enterprises evaluating AI infrastructure often work on compressed timelines, and the choice increasingly comes down to retrofitting existing space versus waiting on new construction. Both paths are valid, but they serve different levels of urgency.

What this means for your infrastructure

Not every workload needs the most extreme density available. Inference workloads, which serve real-time AI responses, often run comfortably at 10 to 20 kW per rack, well within reach of many upgraded facilities. Training workloads are a different story, and that distinction matters when evaluating what a project actually requires.

The broader takeaway: Colocation’s role has expanded. It is no longer just a place to put equipment; it is a factor that directly influences whether AI infrastructure performs as intended. At 365 Data Centers, we’ve treated power and cooling upgrades as a design problem specific to each facility, not a one-size-fits-all retrofit.

As AI workloads evolve, the facilities that can adapt power delivery, cooling strategy, and deployment speed will matter most to the enterprises building on them. Contact us today at https://365datacenters.com/contact/ to discuss what our secure, reliable colocation services can do for you.

The Importance of Low-Latency Connections in Modern Business Applications

Authored by James Janssen, VP of Strategic Projects

In today’s digital economy, milliseconds matter. Sites that load in 1 second achieve conversion rates nearly 3 times higher than those that load in 5 seconds, according to digital marketing agency Portent. As enterprises increasingly rely on real-time applications – from AI inference and video collaboration to financial transactions and remote operations – low-latency connectivity has moved from a technical nicety to a business imperative.

What Low Latency Means for Business

Latency is the time it takes for data to travel from source to destination and back. Low latency means minimal delay, enabling applications to respond instantly and workflows to run smoothly. For modern enterprises, this translates to faster decision-making, better user experiences, and more reliable operations across distributed teams and systems.

Unlike bandwidth, which measures how much data can move at once, latency measures how quickly data moves. High bandwidth with high latency is like a wide highway with frequent stoplights; capacity exists, but responsiveness suffers. For time-sensitive applications, low latency is non-negotiable.

Industries Where Latency Drives Outcomes

Several sectors illustrate why low-latency infrastructure is critical:

Financial services: High-frequency trading platforms depend on microsecond-level execution. A 5-millisecond advantage can mean the difference between profit and loss on algorithmic trades.
Health care: Telemedicine, remote diagnostics, and AI-assisted imaging require near-instant data transfer to support time-critical clinical decisions.
Manufacturing and logistics: Real-time inventory tracking, robotics coordination, and supply chain visibility depend on stable, low-latency networks to prevent costly delays.
AI and machine learning: AI inference – the process of running trained models on live data – is highly latency sensitive, as reported by MarketsandMarkets. Applications like chatbots, fraud detection, and predictive analytics lose value if responses lag.

The Infrastructure Behind Low Latency

Achieving low latency requires more than fast internet. It demands purpose-built infrastructure that minimizes every source of delay. Edge proximity is critical, placing compute and storage closer to end users reduces the physical distance data must travel, which directly cuts round-trip time. High-density interconnection plays an equally important role. Data centers that serve as interconnection hubs enable direct, private paths between networks, cloud providers, and enterprises, avoiding the public internet’s congestion and unpredictable routing, as the Brookings Institution reports.

Supporting this performance requires advanced cooling and power systems. High-performance networking equipment generates significant heat and depends on stable, high-density power to maintain consistent throughput without thermal throttling or outages. Finally, redundant fiber routes are essential for resilience. At 365 Data Centers, we know diverse network paths prevent single points of failure and ensure latency remains low even during fiber cuts, maintenance, or other disruptions.

Practical Steps for Enterprises

Organizations seeking to optimize latency should start by auditing application performance to identify which workloads are truly latency-sensitive and establish baseline response times. From there, data center location becomes a strategic lever: Proximity to end users, cloud on-ramps, and major network exchanges can dramatically reduce latency by shortening the physical path data must travel.

Direct interconnection should be prioritized over public internet routing. Private connections to cloud providers and key partners eliminate unnecessary hops, reduce jitter, and improve reliability for time-critical applications. Finally, infrastructure planning must account for AI workloads. AI inference is highly latency-sensitive, according to MarketsandMarkets, especially for real-time use cases like customer service chatbots, fraud detection, and predictive maintenance, where delayed responses directly diminish business value.

The Bottom Line

Low-latency connectivity is no longer optional for enterprises that depend on real-time applications. As AI, automation, and distributed operations become standard, the infrastructure that supports them must deliver consistent, predictable performance.

At 365 Data Centers, we design our facilities around the needs of latency-sensitive workloads, from high-density power and advanced cooling to direct cloud on-ramps and redundant fiber networks. Our goal is to give enterprises the infrastructure foundation they need to run modern applications with confidence. Contact us today at https://365datacenters.com/contact/.

Adopting Hybrid Data Center and Multi-Cloud Strategies: The Way Forward

Authored by Chris Mirabello, Vice President Cloud, IT & Security

The way enterprises build infrastructure is changing for a simple reason: no single environment fits every workload anymore. Some applications need low latency and tight control, others need scale and flexibility, and many need both. That is why hybrid data center and multi-cloud strategies have moved from “nice to have” to a practical response to real business demands.

Why the model is shifting

Just a few years ago, many IT teams worked to standardize around one cloud or one primary data center model. Today, that approach often creates friction instead of efficiency. Data gravity, compliance requirements, rising cloud costs, and performance demands all push organizations toward a more balanced architecture.

Hybrid infrastructure gives companies a way to place workloads where they perform best. Multi-cloud adds another layer of flexibility by reducing dependence on a single provider and giving teams more options for resilience and pricing. Together, these strategies help organizations match the right workload to the right environment instead of forcing everything into one model.

What to plan for

The biggest mistake companies make is treating hybrid and multi-cloud as a tool choice rather than an operating model. Success depends on design, governance, and visibility across environments. Without clear policies, organizations can end up with duplicate tools, inconsistent security controls, and surprise costs.

A practical starting point is workload segmentation. Mission-critical systems, data-heavy applications, and regulated workloads may belong in a controlled colocation or private environment, while burstable or customer-facing services may fit better in a public cloud. Finding the split that works best for your workload can improve both performance and cost control.

The benefits of flexibility

The value of hybrid and multi-cloud strategies is not just technical. It shows up in business continuity, procurement flexibility, and faster response to changing needs. For example, an enterprise can keep sensitive data close to users in a secure data center while using cloud resources for analytics, test environments, or seasonal spikes.

It also helps during migrations. Rather than forcing a “big bang” move, organizations can modernize in phases. That lowers risk and gives teams time to validate security, network design, and application behavior before making bigger changes.

Operational discipline matters

These architectures work best when the network, security, and support model are designed together. Connectivity between environments must be reliable and easy to manage. Security controls should travel with the workload, not sit separately in each platform. In addition, teams need a clear view of cost, latency, and utilization across the full environment.

In these situations, experienced infrastructure partners add real value. At 365 Data Centers, we see strong demand for environments that combine colocation, network, and cloud in a way that supports flexibility without adding complexity. The goal is not to make every workload hybrid, but to make the right mix sustainable over time.

The path forward

Hybrid data center and multi-cloud strategies are becoming the default because they reflect the operational demands of modern IT. The organizations that do this well will not be the ones with the most platforms, but the ones with clear plans that take their present and future needs into account.

The most successful companies will build for control, resilience, and adaptability from the start. That is the kind of foundation 365 Data Centers helps customers create. Contact us today at https://365datacenters.com/contact/.

High-density Data Center and Colocation Footprints

Authored by Derek Gillespie, Chief Executive Officer

The digital infrastructure landscape is changing quickly, and the pressure on today’s infrastructure is changing with it. AI, high-performance computing, and real-time digital services are pushing power and density requirements far beyond what many legacy facilities were designed to support, which makes the question of where and how capacity gets added more important than ever.

The high-density shift

For operators and enterprise IT teams, the conversation is no longer just about adding space. It is about whether a facility can support the power, cooling, and connectivity required for cabinet densities that now range from 50 kW and above, especially as AI workloads continue to reshape demand. At 365 Data Centers, that growth takes shape in two ways: organic expansion and inorganic expansion.

Organic expansion

Organic expansion refers to facilities we already own and operate, where available space and power can be converted into high-density deployments. Our organic expansion strategy includes facilities in Pennsylvania, Texas, Georgia, and Colorado, where existing assets can be upgraded to support higher-density footprints. The practical advantage is speed: When space and power already exist, the transformation can happen faster than a greenfield build and with less friction for customers who need capacity now.

Not every workload needs a brand-new site. In many cases, the better answer is to reconfigure what already exists, optimize the footprint, and create a higher-performance environment that can support modern compute without forcing customers to start from scratch.

Inorganic expansion

The second path is inorganic expansion, which is where strategic partnerships help us increase our footprint more quickly. Through one such partnership, we are working to develop about 200 MW of AI-ready capacity across key U.S. markets over the next 18-24 months, with initial projects in Colorado and Kentucky and additional sites under evaluation in Connecticut, Kentucky, Virginia, Ohio, and Tennessee.

This pipeline reflects how the market is evolving: more demand, faster timelines, and a need for infrastructure that can support high-density liquid-to-chip environments rather than traditional lower-density deployment models. The goal is not simply to add buildings; it is to create power-rich environments that can support the next generation of AI and high-performance computing workloads.

What this means

For customers, the practical takeaway is that high-density capacity will increasingly come from a mix of approaches. Some of it will come from existing facilities being upgraded for greater power and cooling density, and some of it will come from partnerships that bring new AI-ready capacity online more quickly.

For the industry, the broader shift is clear – colocation is no longer just about where to place equipment. It is becoming a platform for enabling AI-era workloads, improving deployment speed, and aligning infrastructure with the operational realities of modern enterprises.

Ready to discuss your high-density needs? Let’s talk. Visit 365datacenters.com/contact or reach out directly. The future of computing is dense, distributed, and here.

Strategic Deployments, Better Outcomes: How Multi-Tenant, Single-Tenant, and Edge Data Centers Can Address Community Concerns

Strategic Deployments, Better Outcomes:
How Multi-Tenant, Single-Tenant, and Edge Data Centers Can Address Community Concerns

Authored by Charlie Dankner, Chief Sales Officer

Public concern around data centers is not going away, which is why the conversation needs to get more specific. Due to the demands of the digital era, data centers have become necessary infrastructure for everyday life, in a similar category to roads or electricity. Therefore, the question is no longer whether digital infrastructure belongs in a community, but what kind of facility best fits the workload, the site, and the people who live nearby.

Why this debate is changing

Communities are raising sharper questions about water use, power demand, tax incentives, and long-term local value, especially as AI and cloud growth accelerate, the World Resources Institute points out. At the same time, the industry is under pressure to explain why certain projects belong in certain places instead of treating every build as interchangeable. That makes the distinction between multi-tenant, single-tenant, and edge facilities more important than ever.

Three models, three tradeoffs

Multi-tenant data centers spread infrastructure across multiple customers, which can improve utilization and make the facility more efficient over time. They are often a strong fit when customers want flexibility, shared services, and room to scale without building everything from scratch.

On the other hand, single-tenant facilities are designed for one organization and one operating model, which can make planning more focused and help match the site to a very specific workload. That can be useful when the business needs are clear and the design can be optimized around them rather than around a broad customer mix.

Edge facilities bring compute closer to where it is used, which matters for latency-sensitive applications and distributed services. In practical terms, edge utilization can reduce unnecessary backhaul and help organizations place smaller, more targeted deployments in markets where a large, centralized footprint would be harder to justify.

Community concerns in practice

For many residents and local leaders, the real concern is not just that a data center exists, but whether the project is appropriately sized and responsibly designed. A facility that is too large for the market may strain perceptions of fairness and resource use, while a right-sized deployment can feel more like a good civic fit. That is why siting, density, power planning, and operational transparency matter as much as the building itself.

Residents also want to know whether a facility will increase strain on the grid, create traffic, or bring constant construction noise. Local leaders may wonder whether the economic benefits are durable. Different deployment models can change that equation: A smaller edge site may fit better into an existing commercial area, while a purpose-built single-tenant facility can be designed around a narrower set of operational needs.

By explaining these distinctions and tradeoffs to community members, data center developers and operators can address these questions head-on. If a site is intended to serve multiple tenants, the case for shared efficiency should be clear. If it is single-tenant, the rationale should be tied to the workload and the need for a more controlled footprint. If it is an edge facility, the benefit should be framed around local responsiveness and smarter distribution of compute.

What operators should consider

Operators should be ready to answer a simple set of questions: What problem does this site solve, why here, and why this design? They should also be prepared to discuss how the facility will manage energy use, support redundancy, and adapt over time as demand changes. When those answers are clear, the project feels less like an abstract build and more like a planned part of local infrastructure. At 365 Data Centers, that kind of site-specific thinking is central to how we approach colocation, connectivity, and distributed deployment options.

The strongest data center projects are those that fit the workload, the market, and the community. Multi-tenant, single-tenant, and edge deployments each have a role to play, and the right choice can improve performance while fitting into existing local infrastructure. That is the standard the industry should keep aiming for: smarter placement, clearer communication, and facilities that earn their place in the community.

Contact us today to discuss which deployment strategy works best for you: https://365datacenters.com/contact/.

Bridging the Skills Gap: How AI Infrastructure Changes Data Center Talent Demands

Authored by Tom Perkins, Vice President, Data Center Operations

The conversation around artificial intelligence tends to focus on algorithms, compute power, and cloud architectures. But behind every AI workload is a physical facility, and behind every facility is a team of skilled professionals who design, build, and run it. As AI infrastructure scales at unprecedented speed, the data center industry is confronting a workforce challenge that may be more pressing than any power or land constraint: a deepening skills gap.

A Workforce Built for a Different Era

The traditional data center workforce was built around IT technicians, network engineers, and facilities managers with expertise in conventional server environments. That foundation remains important, but AI is rewriting the job description at every level. According to Deloitte, 63% of data center executives cited a shortage of skilled labor as their single greatest obstacle to talent acquisition. Between 2023 and 2025, data center job postings surged 64% – far outpacing the 4% growth seen across the broader economy – while postings for electrical technicians alone climbed more than 180%, Deloitte also reported.

The Physical Side of AI Gets Overlooked

There is a tendency to think of AI talent shortages in terms of data scientists and machine-learning engineers. That’s only part of the picture. Randstad‘s analysis of over 50 million global job postings found that since 2022, demand for HVAC engineers has increased 67%, robotics technicians are up 107%, and electricians are up 18%. Hiring a skilled tradesperson now takes an average of 56 days, and the United States will need approximately 300,000 new electricians over the next decade on top of replacing the 200,000 expected to retire, according to FORTUNE. Uptime Institute found that 51% of data center operators struggled to find qualified candidates in 2024, with the largest shortfalls in junior and mid-level operations roles.

New Skills for New Infrastructure

As data centers evolve to support AI, the skills required to operate them are changing just as fast. Legacy cooling systems are giving way to liquid and immersion cooling. Power densities that once averaged 8 kW per rack have more than doubled to 17 kW, with some AI training environments pushing beyond 80 kW. Managing this infrastructure demands a new blend of expertise: part mechanical engineer, part IT architect, part energy specialist.

The World Economic Forum notes that companies prioritizing upskilling and reskilling their existing workforce, providing clear pathways for employees to grow and transition into more advanced roles, are better positioned to retain institutional knowledge and build a more adaptable team. This approach is especially critical given that nearly 60% of in-demand data center skills are actually non-technical, spanning problem-solving, critical thinking, and adaptability – qualities that can be developed in current employees rather than sourced exclusively from outside. Organizations that invest in competency-based training and cross-functional career pathways will be far better positioned than those chasing talent in an increasingly competitive market.

What This Means for Colocation Customers

This talent landscape has direct implications for enterprises evaluating their infrastructure strategies. Operating an in-house data center is not just a capital challenge – it is a workforce challenge. Recruiting and retaining the specialists needed to manage modern AI-capable facilities are costly and time-consuming, and when those roles go unfilled or turn over, operational continuity is at risk. Colocation providers absorb that burden. At 365 Data Centers, our team of operations professionals stays current with the evolving demands of high-density, AI-ready infrastructure, and our 24/7/365 staffing model means knowledgeable support is always available, giving customers access to deep operational expertise without building an in-house team in one of the tightest labor markets in recent memory.

Looking Ahead

The skills gap in data center operations is not going away on its own. It will require investment from operators, training partnerships with academic institutions and trade programs, and smarter workforce development strategies across the industry. But for enterprises looking to run AI workloads today, choosing the right infrastructure partner, one with experienced teams and the operational depth to manage what is coming next, is one of the most practical steps available. The AI era demands more from data center infrastructure. It demands equally more from the people who run it. Understanding that connection is where smart infrastructure decisions start.

Learn more about how 365 Data Centers can support your infrastructure needs at 365datacenters.com/contact.

The Edge Network Effect: Building AI Infrastructure That Spans Markets, Not Just Facilities

By James Ashton, Vice President of Network Operations

Artificial intelligence is reshaping data center demand, pushing beyond traditional centralized facilities toward distributed, high capacity, highly interconnected, edge networks. What we’re seeing is an “edge network effect” – where interconnected infrastructure across multiple markets multiplies performance, reduces latency, and scales AI workloads efficiently. At 365 Data Centers, our nationwide footprint enables these demanding workloads, turning isolated sites into a cohesive AI backbone.

Why Edge Matters for AI

AI applications, from real-time inference to generative models, require ultra-low latency and massive bandwidth that single facilities struggle to deliver alone. Processing data closer to the user via edge computing cuts delays, which is vital for autonomous systems, smart cities, and enterprise analytics. McKinsey notes that telcos’ edge assets will drive this shift toward enabling the AI era. Centralized clouds struggle; edge networks distribute compute where data is accessed, slashing transport costs and boosting responsiveness.

Our experience mirrors this. In partnerships like DE-CIX Chicago deployment, we enabled peering that extends connectivity across Midwest markets. Supporting the interconnection ecosystem brings us these benefits throughout the world.

The Power of Interconnected Markets

365 Data Centers operates 16 facilities in major and emerging U.S. markets, with 70-plus points of presence (PoPs), creating a low-latency fabric with high levels of interconnection that is ideal for AI. Our new AI-enabled platform integrates colocation, cloud, and networking with an intelligence layer for small-language models and business intelligence, optimized at the edge.

This spans markets; you can deploy in Chicago for Midwest AI workloads, sync with New York or Dallas via redundant fiber, and achieve 99.999% network uptime SLAs. Dark fiber and 86-plus carrier-neutral providers support hybrid AI, with Deloitte forecasting edge AI at $270 billion by 2032.

Lessons from 5G and Beyond

Drawing from 5G preparations, where we scaled edge for IoT and low-latency apps, AI demands similar evolution. Then, we modernized cabling, SDN, and security; today, it’s AI-specific: governed private clouds, redundant power (100% uptime SLA), and carrier-neutral ecosystems with 86-plus providers.

In a 2025 HostingAdvice.com interview, I noted network automation’s role in AI infrastructure, echoing how automation streamlines edge orchestration across facilities. This “edge network effect” emerges: More interconnected sites yield exponential value, like cheaper peering and resilient disaster recovery.

The Future of AI at the Edge

Organizations should prioritize multi-market providers with integrated AI-ready platforms. At 365, one contract covers colocation, connectivity, and dedicated sovereign AI – all with 24/7 support. Spanning markets equips enterprises for AI, as data centers triple to 200-plus GW globally by 2030, according to McKinsey.

The edge network effect isn’t hype; it’s infrastructure reality. By spanning markets, not just facilities, we’re equipping enterprises for AI’s demands. Connect with us at https://365datacenters.com/contact/ to architect yours.

Scaling Your Infrastructure: From Colocation to Cloud and Back

Authored by Charlie Dankner, Chief Sales Officer

The decision to scale your IT infrastructure shouldn’t feel like choosing sides. For too long, businesses have been told they must pick between colocation and cloud – as if committing to one means abandoning the other. After more than 20 years in this industry working for companies that focus on data center or bare metal/cloud and even a combination of both, I can tell you that the most successful client deployments that I’ve seen aren’t the ones that plant their flag in a single camp. They’re the ones understanding that infrastructure strategy is about flexibility, optimization, and having the right tool for the right job.

The Reality of Hybrid Infrastructure

Here’s what we see happening in the market today: Companies migrate workloads to public cloud seeking agility and scalability, only to discover that certain applications become cost-prohibitive due to egress fees, storage costs, and performance challenges. According to recent research from Barclays, 86% of enterprise CIOs are now planning to repatriate at least some public cloud workloads back to private cloud or on-premises infrastructure. At the same time, Gartner forecasts that by 2027, 90% of organizations will adopt hybrid cloud strategies for better flexibility and redundancy.

The truth is, your infrastructure needs are more nuanced than any single solution can address. McKinsey reported in August 2025 that global demand for data center capacity is expected to triple by 2030, driven largely by AI workloads. This explosive growth is exposing the limitations of relying solely on traditional cloud infrastructure and pushing organizations to rethink how and where they run compute.

When to Scale Where

The key to successful infrastructure scaling is understanding which workload placement delivers the best value. IDC research shows that planned and unplanned egress charges alone account for approximately 6% of total cloud storage costs – and for data-intensive applications, this figure can climb dramatically. Bandwidth-intensive, performance-sensitive applications often perform better and cost less when hosted on customer-owned hardware in colocation facilities or on private cloud solutions at the edge.

Industries with strict regulatory requirements – think health care, financial services, or government – can keep sensitive data in secure, compliant colocation environments while leveraging public cloud for less-regulated workloads. According to Broadcom’s “Private Cloud Outlook 2025” report survey of 1,800 IT leaders, 92% of enterprises run a blend of private and public clouds, with 75% saying this mix is part of an intentional strategy.

Our recent partnership with Robot Network exemplifies this evolution. By integrating AI-enabled capabilities directly into our colocation infrastructure, we’re transforming colocation from passive hosting into an active optimization layer. This means you can run small, efficient AI models locally and route selectively to large foundation models only when needed, gaining the full power of AI with a fraction of the cost, latency, and energy footprint.

Building for What’s Next

Infrastructure strategy isn’t static – it’s a continuous optimization exercise. The organizations winning today are those that maintain the flexibility to move workloads between environments as business needs evolve. The global hybrid cloud market is projected to reach $578.72 billion by 2034, according to Precedence Research, expanding at a compound annual growth rate of 17.63%, reflecting this fundamental shift in how enterprises approach infrastructure.

At 365 Data Centers, we’ve built our platform to support this journey. Our carrier-neutral facilities across 20 edge markets, combined with our nationwide fiber network and multiple cloud regions, give you the foundation to scale seamlessly in any direction. Whether you’re exploring AI deployments, building out hybrid cloud, or targeting more predictable IT expenses, our team is available 24/7/365 to help you navigate these decisions.

The future of infrastructure isn’t about choosing colocation or cloud – it’s about having both options available and the expertise to optimize between them. That’s the conversation we want to have with you.

Contact us today to discuss your infrastructure scaling strategy: https://365datacenters.com/contact/.

Customized Colocation: Tailoring Space and Power to Your Unique Requirements

Authored by Tom Perkins

Every enterprise has unique IT needs. Some organizations require modest power for traditional business applications. Others are deploying AI workloads that demand 10 times the energy of a standard server rack. The challenge is finding infrastructure that fits your specific requirements – not the other way around.

Why One-Size-Fits-All No Longer Works

The data center landscape is shifting dramatically. According to McKinsey, average power densities in data centers have more than doubled in just two years, climbing from 8 kilowatts (kW) per rack to 17, with expectations to reach 30 kW by 2027. Some AI training workloads already push beyond 80 kW per rack. Meanwhile, the global colocation market is projected to grow from $84 billion in 2024 to more than $204 billion by 2030, as projected by MarketsandMarkets.

This rapid evolution means that what worked five years ago likely won’t work today. Enterprises running advanced analytics, machine learning models, or high-performance computing need flexible configurations that can accommodate their growing power and cooling requirements. At the same time, organizations with more traditional workloads shouldn’t have to pay for infrastructure they don’t need.

The Case for Customized Colocation

The beauty of customized colocation lies in its flexibility. Rather than forcing your operations into a rigid framework, a trusted colocation provider can design space and power configurations around your actual business needs. This might mean a partial cabinet for a smaller deployment, a full rack with enhanced cooling for compute-intensive applications, or a private cage or suite for organizations requiring dedicated, secure space.

At 365 Data Centers, we work with customers to develop tailored solutions that align infrastructure with business goals. Whatever power density your workloads require, we configure space, power, and cooling to match your requirements – not ours. This approach eliminates stranded assets and ensures you’re not paying for capacity you’ll never use.

Scaling Without the Headaches

One of the greatest advantages of customized colocation is scalability. As your business grows, your infrastructure can grow with it. For example, if you need to expand from two racks to 10, or are experiencing seasonal demand fluctuations, flexible power and space arrangements can accommodate those needs without locking you into year-round overcapacity.

This adaptability is particularly valuable given how quickly technology requirements change. Goldman Sachs projects that global power demand from data centers will increase 50% by 2027 and as much as 165% by 2030. Having a colocation partner who can scale alongside your evolving needs 24/7/365 means you stay competitive without massive capital expenditures.

Beyond Space and Power

Customization extends beyond kilowatts and square footage. Enterprises today need infrastructure that meets specific compliance standards, connectivity requirements, and security protocols. A properly customized colocation solution addresses these needs holistically, providing not just the physical space but the network connectivity, redundancy levels, and compliance certifications your industry demands.

Moving Forward

The days of generic data center solutions are behind us. Whether your organization is exploring AI capabilities, managing hybrid cloud environments, or simply seeking more predictable IT costs, customized colocation provides a path forward that adapts to your needs rather than constraining them.

Our team at 365 Data Centers is here to help you design infrastructure that fits your unique requirements, today and as those requirements evolve. Contact us to start the conversation: https://365datacenters.com/contact/