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As computing demands soar in 2025, traditional air cooling systems are hitting their limits. Hyperscalers are pushing infrastructure harder than ever, driven by AI model training, high-frequency trading, gaming, and other compute-heavy workloads. Enter liquid cooling—the innovation rapidly becoming the gold standard for hyperscale data centers worldwide.The Problem: Heat Is the Enemy of PerformanceAs server density increases, so does the heat they generate. Modern CPUs and GPUs produce more thermal output than older hardware, and data centers running thousands of these machines in a confined space face a major cooling challenge.Air cooling systems, while cost-effective and easy to maintain, struggle to handle power densities above 10-15 kW per rack. Many AI workloads require racks running at 30-60 kW—or even more.Without efficient cooling, hardware throttles, fails, or underperforms, making cooling not just a maintenance concern but a performance limiter.What Is Liquid Cooling?Liquid cooling uses fluids to transfer heat away from hardware components. There are two main approaches:Direct-to-chip (D2C) cooling: Liquid circulates through cold plates mounted directly on processors and high-heat components.Immersion cooling: Entire servers are submerged in thermally conductive dielectric fluid.Both systems are vastly more efficient than air cooling at conducting heat—up to 1,000 times more effective in some cases.Why Hyperscalers Are SwitchingIn 2025, hyperscalers like Google, Meta, AWS, and Microsoft are rolling out liquid-cooled environments across their newest facilities. Here's why:Increased power densities: AI workloads and HPC demand more compute per rack.Energy efficiency: Liquid cooling reduces the need for energy-hungry air conditioning systems.Space savings: Allows for tighter rack configurations, increasing floor utilization.Sustainability: Many liquid cooling systems reuse water or use low-impact fluids.Noise and dust reduction: Eliminates fans and improves air quality in server environments.Microsoft, for example, has tested two-phase immersion cooling for AI training clusters, reporting a 30% energy efficiency gain and increased hardware reliability.Comparing Liquid vs. Air CoolingWhile both air and liquid cooling serve the same goal—removing heat from IT equipment—their performance, efficiency, and use cases differ significantly in 2025. Here’s how they stack up across key dimensions:Power Density LimitsAir cooling typically handles power densities up to 10–15 kW per rack. This is sufficient for many legacy workloads but inadequate for AI and GPU-driven tasks.Liquid cooling, in contrast, can comfortably support racks running at 30–100+ kW, making it ideal for hyperscale and high-performance computing (HPC) environments.Energy EfficiencyAir systems rely heavily on fans and chillers, which consume significant energy. Liquid cooling is inherently more thermally efficient, reducing the need for energy-intensive HVAC systems and achieving lower Power Usage Effectiveness (PUE) values.Noise LevelsAir-cooled data centers generate considerable noise from server fans and air handling units. Liquid cooling reduces or eliminates the need for these fans, creating quieter environments and improving working conditions in the data hall.Space RequirementsBecause air cooling depends on hot and cold aisle containment and requires spacing between racks, it consumes more floor space. Liquid cooling enables denser hardware configurations, optimizing space utilization and lowering real estate costs per kilowatt.Maintenance ComplexityAir cooling is generally easier to maintain, using familiar HVAC systems. Liquid cooling introduces complexity with pumps, fluid handling, and leak mitigation, requiring specialized skills and proactive maintenance.Deployment SpeedAir-cooled systems are faster to deploy and easier to retrofit in existing facilities. Liquid cooling often demands more planning and infrastructure upgrades, which can extend deployment timelines—though modular solutions are improving rollout speeds.The Environmental AngleLiquid cooling also supports sustainability goals. By reducing total power usage and enabling waste heat reuse, it lowers carbon footprints. Some facilities capture the waste heat and divert it to district heating systems for local buildings.Water usage is a concern with some systems, especially in water-scarce areas. However, many liquid cooling systems now use closed-loop designs that reuse the same fluid indefinitely.Challenges and ConsiderationsDespite the benefits, liquid cooling isn’t without hurdles:Higher upfront costs: Installation is more complex and expensive than air-cooled systems.Infrastructure upgrades: Requires retrofitting or designing for specialized piping, containment, and redundancy.Skill requirements: Maintenance teams must be trained in new procedures and safety protocols.Vendor diversity: Fewer plug-and-play options than traditional servers.However, as more hyperscalers adopt these systems, the ecosystem is maturing. Vendors like Submer, Vertiv, LiquidStack, and Iceotope are driving innovation, lowering costs, and standardizing form factors.Who’s Already Using It?Meta: Deploying two-phase immersion cooling in AI-focused data centers.Google: Using custom-built liquid-cooled racks in its Tensor Processing Unit (TPU) deployments.Microsoft: Active RD in immersion cooling and modular systems.Equinix: Trialing liquid cooling pods in select colocation facilities.The trend is also reaching the enterprise and edge. Smaller operators are starting to embrace liquid cooling for edge deployments where space and energy constraints are more intense.What It Means for Data Center DesignLiquid cooling isn’t just a feature—it changes the blueprint. In 2025, architects and engineers are designing from the ground up with:Compact layouts that accommodate denser rack placement.Custom power and cooling pathways to optimize flow and redundancy.Sustainability integrations like waste heat reuse, closed-loop systems, and renewable power sources.AI-powered cooling management to dynamically optimize thermal performance.As the demand for high-density compute rises, these systems are no longer optional—they’re strategic.Liquid cooling is no longer a niche solution—it’s the future of hyperscale data centers. As AI and HPC workloads reshape infrastructure needs, traditional cooling methods are simply not up to the task.By adopting liquid cooling, hyperscalers are achieving better performance, greater sustainability, and more efficient use of space and power. In 2025, this trend is setting a new standard—one that colocation providers, enterprises, and edge operators will need to follow to stay competitive.

As cloud-native development continues to evolve, DevOps teams face a crucial decision: should they deploy in the public cloud, or colocate infrastructure in a third-party data center? While cloud computing offers scalability and convenience, colocation brings performance, control, and cost predictability. In 2025, this decision isn’t binary—it's strategic.The rise of hybrid environments, AI/ML workloads, edge computing, and tighter security requirements is reshaping how development teams think about infrastructure. This blog breaks down the key differences between colocation and cloud, explores use cases where each excels, and offers guidance on how DevOps teams can make the right call for their needs.What Is Colocation?Colocation (or "colo") is the practice of renting physical space in a third-party data center to house your own servers and hardware. You manage the hardware; the provider supplies the power, cooling, connectivity, and physical security.Colo facilities often come with robust network options, interconnection ecosystems, and 24/7 support. Think of it as your data center, without having to build or maintain the building.What Is Public Cloud?Public cloud services like AWS, Azure, and Google Cloud offer on-demand infrastructure and platforms. Resources are virtualized, shared, and metered. Dev teams get instant provisioning, flexible scaling, and access to advanced services without managing physical infrastructure.Cloud is excellent for rapid experimentation, elastic workloads, and services like databases, machine learning, and analytics.Comparing Colocation vs. Cloud: Key Factors for DevOps TeamsWhen evaluating colocation and public cloud, DevOps teams should consider several critical infrastructure factors, including control, performance, scalability, cost, and security. These elements directly impact the speed, reliability, and flexibility of development pipelines.Controlis one of the most significant differentiators. Colocation gives teams complete ownership of the hardware and network stack. You can configure servers, firewalls, and switches precisely to your needs. In contrast, public cloud platforms abstract much of this control, offering pre-configured services that limit how deeply teams can tune the infrastructure.Performancealso diverges between the two. In colocation environments, bare-metal servers deliver consistent and predictable performance, ideal for workloads sensitive to latency or processor noise. Cloud instances, however, often share underlying resources with other tenants, making performance more variable due to virtualization overhead and resource contention.Scalabilityfavors the cloud. Public cloud environments allow teams to scale infrastructure almost instantly—spinning up new instances or services in minutes. Colocation, on the other hand, involves procuring and installing physical hardware, making it more deliberate and time-consuming to scale.Cost structureis another major consideration. Colocation typically involves higher upfront capital expenditures (CapEx) but offers predictable operating expenses (OpEx) over time. This can be advantageous for stable, long-term workloads. Cloud follows a consumption-based OpEx model, which is flexible but can become expensive as usage scales or if resources are not tightly managed.Securitydiffers in implementation and responsibility. With colocation, organizations control the full stack—hardware, software, and physical security—allowing for tighter control and isolation. In the cloud, a shared responsibility model means the provider handles infrastructure security, while customers secure their applications and data, requiring trust and vigilance.Latencyis often lower in colocation, especially for edge deployments located close to end-users. Cloud latency can vary depending on the chosen region and service architecture, which may affect performance for real-time applications.From aDevOps toolingperspective, colocation integrates well with standard infrastructure-as-code (IaC) tools like Terraform, Ansible, and monitoring platforms like Prometheus. However, public cloud platforms offer tightly integrated, native DevOps services—such as CI/CD pipelines, serverless computing, and monitoring suites—that can accelerate deployment.Finally,compliancerequirements can tip the scales. Colocation is often favored in industries with strict data residency, air-gapping, or audit requirements, thanks to its physical isolation. Public cloud environments can still meet regulatory standards, but doing so demands diligence in selecting certified providers and ensuring configurations align with compliance mandates.When Colocation Makes Sense for Dev Teams1. High-Performance and Consistency NeedsAI training, rendering, simulation, and other performance-sensitive workloads often run better on dedicated servers. Colo eliminates noisy neighbors and hypervisor overhead.2. Cost Predictability at ScaleLong-term workloads may be cheaper to run on owned hardware hosted in colo, especially if optimized for power and density.3. Data Sovereignty and ComplianceHeavily regulated industries (e.g., finance, healthcare, government) may require physical isolation, audit trails, and strict data residency controls that are easier to enforce in a colo model.4. Edge DeploymentsColo facilities in regional markets enable low-latency access to users. This is valuable for content delivery, gaming, IoT, and real-time analytics.5. Custom Hardware RequirementsSome DevOps teams need specific GPU configurations, storage setups, or networking hardware that cloud providers don’t offer.When Public Cloud Wins1. Rapid Prototyping and Dev/TestSpin up environments in minutes, run tests, and tear them down without touching a data center. Ideal for agile teams and frequent iterations.2. Elastic ScalingWhen traffic spikes, cloud lets you scale up without provisioning physical servers. This is perfect for SaaS applications and unpredictable workloads.3. Advanced Cloud ServicesPublic clouds offer a vast ecosystem of managed services—from Kubernetes and serverless to AI/ML APIs and global CDN networks.4. Global ReachDeploy applications closer to users with cloud regions across continents. Great for multi-region applications and latency-sensitive content.5. Smaller Teams and StartupsStartups with limited staff can benefit from offloading infrastructure management to the cloud, allowing them to focus on core development.Colocation + Cloud: The Hybrid OptionMost mature DevOps organizations adopt a hybrid approach:Dev/test in the cloudProduction in colocationBackups and DR in cloudEdge compute in regional colo sitesHybrid architectures offer flexibility, cost savings, and the ability to leverage the best of both worlds.Tools to Manage Hybrid Deployments:Terraform: Automate infrastructure across cloud and on-premAnsible: Manage configurations for both environmentsKubernetes: Unified container orchestrationArgo CD / Flux: GitOps for hybrid clustersEquinix Fabric / Megaport: Connect cloud to colo with low latencyReal-World Example: Media Streaming PlatformA video streaming company uses:Colo for storing petabytes of high-res video contentCloud for transcoding and analyticsEdge colo for content delivery near usersThis hybrid setup optimizes storage costs, processing performance, and user experience.Best Practices for Dev TeamsAssess Workload Types: Classify based on performance, latency, and scaling needsUse IaC Everywhere: Abstract differences with Terraform and Helm chartsStandardize Monitoring: Use tools like Prometheus and Grafana across environmentsPlan for Portability: Avoid hard dependencies on one providerBuild for Failure: Design systems to fail gracefully, regardless of environmentIn 2025, the question isn’t "cloud or colocation?" It’s "how do we best combine them?" For DevOps teams, choosing between cloud and colo depends on application architecture, cost structure, and operational goals.Colocation offers power and control. Cloud delivers speed and scalability. Together, they form a powerful, flexible foundation for modern software delivery.Evaluate your workloads, embrace automation, and stay agile. That’s the blueprint for DevOps success in a hybrid world.

As the digital economy grows at breakneck speed, data centers are becoming the backbone of everything from cloud computing and AI to e-commerce and streaming. But building a data center isn’t just about racks and power—it all starts with the land.In 2025, real estate site selection for data centers is undergoing a technological revolution, powered by artificial intelligence (AI). AI is helping developers make smarter, faster, and more strategic decisions by analyzing massive datasets that were once difficult or impossible to process manually. The result: lower risk, faster time to market, and higher ROI.Why Site Selection Is So ComplexSelecting the right site for a data center is a multidimensional challenge. Factors to consider include:Power availability and costFiber connectivity and proximity to network hubsLand cost and zoning regulationsWater access and cooling optionsNatural disaster risk (floods, earthquakes, etc.)Local tax incentives and government supportProximity to users (for edge and low-latency apps)Traditionally, this process involved site surveys, manual research, and fragmented data. Now, AI is bringing speed, precision, and predictive insights to the table.How AI Transforms Site Selection1. Geospatial Analysis at ScaleAI can ingest satellite imagery, topographical data, and GIS layers to assess land usability, elevation, flood zones, and nearby infrastructure. This allows planners to instantly filter out unsuitable sites.2. Power Grid IntelligenceMachine learning models analyze utility data to:Forecast power availability and reliabilityIdentify substations and transmission linesPredict long-term energy costs based on local trendsThis reduces the guesswork around one of the most critical inputs in data center planning.3. Connectivity MappingAI platforms scan and cross-reference ISP, fiber route, and data exchange maps. They can suggest locations with:Short latency paths to internet backbonesDense peering ecosystemsDiverse carrier optionsThis helps ensure strong performance and network resilience.4. Regulatory and Zoning ComplianceNatural language processing (NLP) tools parse zoning codes, environmental restrictions, and permitting regulations across jurisdictions. This allows teams to avoid regions with long or costly approval processes.5. Environmental Risk AssessmentAI models simulate the risk of natural disasters by analyzing climate history, seismic activity, and flood patterns. They can even factor in climate change projections.6. Economic Incentive OptimizationMachine learning algorithms can compare tax breaks, grants, and subsidies across regions to identify the best financial environment for investment.Real-World Tools and PlatformsSeveral AI-driven platforms are changing the game in data center site selection:UrbanFootprint: Combines land use, infrastructure, and demographic data to evaluate site suitability.Descartes Labs: Uses satellite imagery and AI to assess terrain, land cover, and construction risk.ESRI ArcGIS with AI Plugins: Enables smart mapping and predictive modeling for planners.Doxel and AirWorks: Use AI for site scanning, visual analysis, and construction feasibility.Custom ML Models: Large operators are developing proprietary AI engines for internal use.How Developers Are Using AI Insights1. Faster Time to MarketAI helps shortlist viable sites in hours instead of weeks, accelerating deal flow and development.2. Optimized Capital AllocationBy accurately forecasting costs, performance, and risk, developers can make smarter financial decisions.3. Data-Driven NegotiationsArmed with AI-generated insights, site acquisition teams can negotiate better pricing and terms with landowners and municipalities.4. Sustainability ModelingAI can help assess carbon impact, water usage, and renewable energy options at the site level.5. Multi-Site Portfolio PlanningLarge operators use AI to evaluate entire portfolios and optimize deployments across regions based on business goals and performance.AI + Human Expertise: A Powerful ComboAI doesn’t replace human expertise—it enhances it. Real estate strategists, engineers, and planners bring the intuition, local knowledge, and experience that AI lacks. When combined:AI does the heavy data liftingHumans validate assumptions and apply contextTeams make faster, smarter, and more confident decisionsChallenges and Considerations1. Data Quality and IntegrationAI is only as good as the data it learns from. Inconsistent, outdated, or siloed data can lead to inaccurate predictions.2. Regulatory ShiftsLocal policies change, and AI models must be constantly updated to reflect new incentives, environmental laws, or geopolitical risks.3. Interpretability and TrustSome stakeholders may resist AI-driven decisions unless they understand how insights are generated. Explainability is key.Future TrendsAI-Driven Digital Twins: Simulate site builds before construction begins, using real-world data.Integration with BIM and CAD: Align AI insights with architectural and engineering plans.Edge AI for Micro-Site Selection: AI will be used to identify sites for modular or edge data centers based on micro-latency and IoT demand.Climate-Aware Site Modeling: Predict future climate risk to ensure long-term viability.AI is redefining how data center sites are chosen. With the ability to process complex variables across geographies, utilities, regulations, and sustainability, AI empowers developers to move with speed and certainty.For real estate leaders, infrastructure strategists, and DevOps professionals, embracing AI in site selection isn’t just a competitive advantage—it’s becoming an industry standard.As data centers become more critical and more distributed, the smartest decisions will start with smart data. And AI is the engine that will drive the next generation of digital infrastructure from the ground up.

Not long ago, bare metal servers were considered outdated—a relic of an earlier era in computing. But fast forward to 2025, and they’re back in the spotlight, thanks in large part to artificial intelligence (AI) and high-performance computing (HPC). The raw power, isolation, and customizability of bare metal are proving essential for AI workloads, and DevOps teams are taking notice.As organizations prioritize performance, cost control, and data sovereignty, bare metal offers a powerful alternative to traditional cloud VMs. Especially for workloads like deep learning, inference, and GPU-intensive training models, bare metal can outperform virtualized environments and provide greater transparency into performance.In this blog, we explore how AI is driving the resurgence of bare metal, what this means for DevOps teams, and how to leverage bare metal effectively in your infrastructure strategy.Why Bare Metal Is Making a Comeback1. Performance Without OverheadUnlike virtual machines, bare metal doesn’t have a hypervisor layer, which means no resource contention or virtualization tax. This is critical for:AI model trainingGPU-intensive tasksReal-time analyticsLatency-sensitive applications2. Custom Hardware ConfigurationsAI use cases often require:High memory-to-core ratiosSpecialized GPUs (NVIDIA A100, H100, etc.)NVMe storageBare metal gives DevOps teams the freedom to tailor hardware to specific workloads.3. Predictable Cost and PerformanceWith fixed hardware, you get consistent throughput and latency—no noisy neighbors. This predictability is vital when optimizing ML pipelines and tuning performance.4. Data Sovereignty and SecurityFor industries handling sensitive data (healthcare, finance, defense), bare metal offers physical isolation, stronger compliance alignment, and lower risk of shared infrastructure vulnerabilities.AI Workloads That Benefit from Bare MetalBare metal is particularly well-suited for the following AI applications:Large Language Model (LLM) TrainingComputer VisionAutonomous SystemsSpeech Recognition and SynthesisRecommendation EnginesAI-Powered Simulation and ModelingThese workloads typically demand high-performance GPUs, fast I/O, and minimal interruptions—a perfect match for bare metal environments.How DevOps Teams Can Leverage Bare Metal1. Infrastructure as Code for Bare MetalModern DevOps teams can manage bare metal just like cloud:Use Terraform, Pulumi, or Ansible to provision servers.Tools like Tinkerbell, MAAS, and Razor automate physical provisioning.Combine with GitOps workflows for deployment automation.2. Kubernetes on Bare MetalRun containerized workloads with the performance of metal:Bare metal Kubernetes offers better GPU scheduling and less overhead.Tools like MetalLB, Cilium, and Rancher simplify networking and cluster management.3. Hybrid Cloud with Bare Metal EdgeBare metal at the edge provides compute close to users and devices:Ideal for autonomous vehicles, retail analytics, and smart factoriesEdge-native platforms like Equinix Metal, Packet, and Ridge support global bare metal deployments4. CI/CD for Hardware-Centric WorkloadsUse CI/CD pipelines to deploy to bare metal environments:Integrate GitHub Actions, GitLab CI, or Jenkins for automated builds and testsUse hooks for hardware provisioning, BIOS/firmware checks, and OS installationComparing Bare Metal and Cloud VMs for AI WorkloadsWhen evaluating infrastructure for AI, DevOps teams must weigh performance, cost, security, and scalability. Here's howbare metal serverscompare tocloud virtual machines (VMs)in key categories:PerformanceBare Metal: Offers the highest performance since there’s no hypervisor layer. Ideal for compute-intensive tasks like AI model training and real-time analytics.Cloud VMs: Generally provide good performance, but virtualization introduces overhead and potential resource contention.CostBare Metal: Typically comes with predictable pricing. You pay for dedicated hardware, which avoids surprise costs or billing spikes.Cloud VMs: Operate on a pay-as-you-go model, which is flexible but can result in unexpected charges during peak usage.CustomizationBare Metal: Provides full control over hardware configurations. You can specify GPU models, memory, storage, and networking to match AI workload needs.Cloud VMs: Limited to predefined instance types. Customization is minimal compared to physical servers.SecurityBare Metal: Offers physical isolation, reducing the risk of multi-tenant vulnerabilities. Preferred for sensitive workloads.Cloud VMs: Operate in multi-tenant environments, which can introduce security and compliance concerns depending on the application.GPU FlexibilityBare Metal: Enables custom GPU configurations, including advanced setups like multi-GPU interconnects or dedicated PCIe lanes.Cloud VMs: Provide access to GPUs via fixed instance types, which may limit configuration flexibility.ScalabilityBare Metal: Hardware provisioning can take longer, which impacts scalability for dynamic workloads.Cloud VMs: Highly scalable with near-instant provisioning, ideal for bursty or unpredictable workloads.Common Bare Metal Pitfalls and How to Avoid Them1. Provisioning DelaysSolution: Use APIs and IaC tools to automate setup2. UnderutilizationSolution: Consolidate workloads and use container orchestration for efficiency3. Monitoring and LoggingSolution: Integrate with Prometheus, Grafana, and ELK Stack to maintain visibility4. Scalability LimitsSolution: Combine bare metal with cloud-native autoscaling in hybrid architecturesBare Metal Providers to Watch in 2025DedicatedNodes: Custom bare metal with edge locations and GPU configurationsEquinix Metal: API-driven global bare metal infrastructureOVHcloud: High-performance, cost-effective hardware in Europe and North AmericaPhoenixNAP: Specialized GPU and AI-ready serversOracle Cloud Infrastructure (OCI) Bare Metal: Integration with high-speed networking and block storageThe DevOps Mindset for Bare MetalTo succeed with bare metal, DevOps teams should:Embrace automation at every layerTreat infrastructure like code, even if it’s physicalMonitor resource consumption rigorouslyBuild modular, portable deployment workflowsStay on top of firmware, OS, and driver updatesFuture Trends: AI + Bare Metal + EdgeComposable Infrastructure: Dynamically assemble compute, storage, and GPU resourcesAI Workload Scheduling: Advanced schedulers optimize GPU clustersLiquid Cooling: Reduces power consumption and improves densityAI on ARM Servers: Low-power, high-efficiency alternatives to x86-based hardwareAI isn’t just transforming software—it’s reshaping infrastructure priorities. As DevOps teams strive to meet growing demands for speed, scale, and intelligence, bare metal offers a compelling solution.By combining the best of both worlds—cloud-like automation with physical performance control—bare metal is shedding its legacy image and becoming a strategic asset in the age of AI.DevOps teams ready to embrace this evolution will gain a competitive edge in deploying smarter, faster, and more secure infrastructure.Stay tuned as we explore the next frontier: where bare metal meets AI at the edge.