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Month: May 2025

Understanding Data Center Pricing: The Four Pillars Explained

Posted on by yotta

As enterprises undergo rapid digital transformation, data centers have emerged as critical enablers of always-on, high-performance IT infrastructure. From hosting mission-critical applications to powering AI workloads, data centers offer organisations the scale, reliability, and security needed to compete in a digital-first economy. However, as more businesses migrate to colocation and hybrid cloud environments, understanding the intricacies of data center pricing becomes vital.

At the core of every data center pricing model are four key pillars: Power, Space, Network, and Support. Together, these elements determine the total cost of ownership (TCO) and influence operational efficiency, scalability, and return on investment.

1. Power: Power is the single largest cost component in a data center pricing model, accounting for up to 50-60% of the overall operational expense. Power is typically billed based on either actual consumption (measured in kilowatts or kilowatt-hours) or committed capacity – whichever pricing model is chosen, understanding usage patterns is essential.

High-density workloads such as AI model training, financial simulations, and media rendering demand substantial compute power, and by extension, more electricity. This makes the power efficiency of a data center critical measured by metrics like Power Usage Effectiveness (PUE). A lower PUE indicates that a greater proportion of power is used by IT equipment rather than cooling or facility overheads, translating to better cost efficiency.

When evaluating power pricing, businesses should ask:

– Is pricing based on metered usage or a flat rate per kW?

– What is the data center’s PUE?

– Can the data center support high-density rack configurations?

– Is green energy available or part of the offering?

2. Space: Data centers are increasingly supporting high-density configurations, allowing enterprises to pack more compute per square foot – a shift driven by AI workloads, big data analytics, and edge computing. Instead of traditional low-density deployments that require expansive floor space, high-density racks (often exceeding 10–15 kW per rack) are becoming the norm, delivering greater value and performance from a smaller footprint.

Advanced cooling options such as hot/cold aisle containment, in-rack or rear-door cooling, and even liquid cooling are often available to support these dense configurations, minimising thermal hotspots and improving energy efficiency.

Key considerations when evaluating space pricing include:

– What is the maximum power per rack the facility can support?

– Are advanced cooling solutions like in-rack or liquid cooling supported for AI or HPC workloads?

– Are there flexible options for shared vs. dedicated space (cage, suite, or row)?

– Is modular expansion possible as compute demand grows?

– Does the provider offer infrastructure design consulting to optimize space-to-performance ratios?

3. Network: While power and space house the infrastructure, the network enables it to function connecting systems, users, and clouds seamlessly. Power and space host your infrastructure, but it’s the network that activates it – enabling data to flow seamlessly across clouds, users, and geographies. Network performance can make or break mission-critical services, especially for latency-sensitive applications like AI inferencing, financial trading, and real-time collaboration.

Leading hyperscale data centers operate as network hubs, offering carrier-neutral access to a wide range of Tier-1 ISPs, cloud service providers (CSPs), and internet exchanges (IXs). This diversity ensures better uptime, lower latency, and competitive pricing.

Modern facilities also offer direct cloud interconnects that bypass the public internet to ensure lower jitter and enhanced security. Meanwhile, software-defined interconnection (SDI) services and on-demand bandwidth provisioning have introduced flexibility never before possible.

The network pricing model varies by provider, but key factors to evaluate include:

– Are multiple ISPs or telecom carriers available on-site for redundancy and price competition?

– What is the pricing model for IP transit flat-rate, burstable, or 95th percentile billing?

– Are cross-connects priced per cable or port, and are virtual cross-connects supported?

– Is there access to internet exchanges or cloud on-ramp ecosystems?

– Does the data center support scalable bandwidth and network segmentation (VLANs, VXLANs)?

4. Support: The fourth pillar Support encompasses the human and operational services that keep infrastructure running smoothly. From basic tasks like reboots and cable management to advanced services such as patching, compliance assistance, and disaster recovery, support offerings can vary significantly between providers.

Beyond tangible infrastructure, support services form the fourth critical pillar of data center pricing. These include remote hands, monitoring, troubleshooting, infrastructure management, and compliance support – all of which have a direct impact on uptime and business continuity.

While some providers bundle basic support into the overall pricing, others follow a pay-as-you-go or tiered model. The range of services can vary from basic reboots and cable replacements to advanced offerings like patch management, backup, and disaster recovery services.

Important support considerations include:

– What SLA’s are in place for incident resolution and response times?

– Is 24/7 support available on-site and remotely?

– What level of expertise do support engineers possess (e.g., certifications)?

– Are managed services or white-glove services available?

For enterprises without a local IT presence near the data center, high-quality support services can serve as a valuable extension of their internal teams.

Bringing It All Together

Evaluating data center pricing through the lens of Power, Space, Network, and Support helps businesses align their infrastructure investments with operational needs and long-term goals. While each of these pillars have individual cost implications, they are deeply interdependent. For instance, a facility offering lower space cost but limited power capacity might not support high-performance computing. Similarly, a power-efficient site without robust network options could bottleneck AI or cloud workloads.

As enterprise IT evolves driven by trends like AI, edge computing, hybrid cloud, and data sovereignty so too will pricing models. Providers that offer transparent, flexible, and scalable pricing across these four pillars will be best positioned to meet the demands of tomorrow’s digital enterprises.

As enterprise IT & data center needs evolve – driven by trends like AI, hybrid cloud, edge computing, and data sovereignty – so will pricing models. Providers that offer transparent, scalable, and flexible offerings across these four pillars will be best positioned to meet the demands of future digital enterprises.

Conclusion: For CIOs, CTOs, and IT leaders, decoding data center pricing goes beyond cost it’s about creating long-term value and strategic alignment. The ideal partner delivers a well-balanced approach across Power, Space, Network, and Support, combining performance, scalability, security, sustainability, and efficiency. Focusing solely on price, can result in infrastructure bottlenecks, hidden risks, and lost opportunities for innovation.  A holistic, future-ready evaluation framework empowers organizations to build infrastructure that supports innovation, resilience, and growth.

Colocation Infrastructure – Reimagined for AI and High-Performance Computing

Posted on by yotta

The architecture of colocation is undergoing a paradigm shift, driven not by traditional enterprise IT, but by the exponential rise in GPU-intensive workloads powering generative AI, large language models (LLMs), and distributed training pipelines. Colocation today must do more than house servers it must thermodynamically stabilize multi-rack GPU clusters, deliver deterministic latency for distributed compute fabrics, and maintain power integrity under extreme electrical densities.

Why GPUs Break Legacy Colocation Infrastructure

Today’s AI systems aren’t just compute-heavy, they’re infrastructure-volatile. The training of a multi-billion parameter LLM on an NVIDIA H100 GPU cluster involves sustained tensor core workloads pushing 700+ watts per GPU, with entire racks drawing upwards of 40–60 kW under load. Even inference at scale, particularly with memory-bound models like RAG pipelines or multi-tenant vector search, introduces high duty-cycle thermal patterns that legacy colocation facilities cannot absorb.

Traditional colocation was designed for horizontal CPU scale, think 2U servers at 4–8 kW per rack, cooled via raised floor air handling. These facilities buckle under the demands of modern AI stacks:

i. Power densities exceeding 2.5–3x their design envelope.

ii. Localized thermal hotspots exceeding 40–50°C air exit temperatures.

iii. Inability to sustain coherent RDMA/InfiniBand fabrics across zones.

As a result, deploying modern AI stacks in a legacy colocation isn’t just inefficient it’s structurally unstable.

Architecting for AI: The Principles of Purpose-Built Colocation

Yotta’s AI-grade colocation data center is engineered ground-up with first-principle design addressing the compute, thermal, electrical, and network challenges introduced by accelerated computing. Here’s how:

1. Power Density Scaling: 100+ kW Per Rack: Each pod is provisioned for densities up to 100 kW per rack, supported by redundant 11–33kV medium voltage feeds, modular power distribution units (PDUs), and multi-path UPS topologies. AI clusters experience both sustained draw and burst-mode load spikes, particularly during checkpointing, optimizer backprop, or concurrent GPU sweeps. Our electrical systems buffer these patterns through smart PDUs with per-outlet telemetry and zero switchover failover.

We implement high-conductance busways and isolated feed redundancy (N+N or 2N) to deliver deterministic power with zero derating, allowing for dense deployments without underpopulating racks, a common hack in legacy setups.

2. Liquid-Ready Thermal Zones: To host modern GPU servers like NVIDIA’s HGX H100 8-GPU platform, direct liquid cooling isn’t optional it’s mandatory. We support:

– Direct liquid cooling

– Rear Door Heat Exchangers (RDHx) for hybrid deployments

– Liquid Immersion cooling bays for specialized ASIC/FPGA farms

Our data halls are divided into thermal density zones, with cooling capacity engineered in watts-per-rack-unit (W/RU), ensuring high-efficiency heat extraction across dense racks running at 90–100% GPU utilization.

3. AI Fabric Networking at Rack-Scale and Pod-Scale: High-throughput AI workloads demand topologically aware networking. Yotta’s AI colocation zones support:

– InfiniBand HDR/NDR up to 400 Gbps for RDMA clustering which allows data transfer directly between the memory of different nodes

– NVLink/NVSwitch intra-node interconnects

– RoCEv2/Ethernet 100/200/400 Gbps fabrics with low oversubscription ratios (<3:1)

Each pod is a non-blocking leaf-spine zone, designed for horizontal expansion with ultra-low latency (<5 µs) across ToRs. We also support flat L2 network overlays, container-native IPAM integrations (K8s/CNI plugins), and distributed storage backplanes like Ceph, Lustre, or BeeGFS critical for high IOPS at GPU memory bandwidth parity.

Inference-Optimized, Cluster-Ready, Sovereign by Design

The AI compute edge isn’t just where you train it’s where you infer. As enterprises scale out retrieval-augmented generation, multi-agent LLM inference, and high-frequency AI workloads, the infrastructure must support:

i. Fractionalized GPU tenancy (MIG/MPS)

ii. Node affinity and GPU pinning across colocation pods

iii. Model-parallel inference with latency thresholds under 100ms

Yotta’s high-density colocation is built to support inference-as-a-service (IaaS) deployments that span GPU clusters across edge, core, and near-cloud regions all with full tenancy isolation, QoS enforcement, and AI service-mesh integration.

Yotta also provides compliance-grade isolation (ISO 27001, PCI-DSS, MeitY-ready) with zero data egress outside sovereign boundaries enabling inference workloads for BFSI, health, and government sectors where AI cannot cross borders.

Hybrid-Native Infrastructure with Cloud-Adjacent Expandability

AI teams don’t want just bare metal they demand orchestration. Yotta’s colocation integrates natively with Shakti Cloud, providing:

i. GPU leasing for burst loads

ii. Bare-metal K8s clusters for CI/CD training pipelines

iii. Storage attach on demand (RDMA/NVMe-oF)

This hybrid model supports training on-prem, bursting to cloud, inferring at edge all with consistent latency, cost visibility, and GPU performance telemetry. Whether it’s LLM checkpoint resumption or rolling out AI agents across CX platforms, our hybrid infra lets you build, train, and deploy without rebuilding your stack.

Conclusion

In an era where GPUs are the engines of progress and data is the new oil, colocation must evolve from passive hosting to active enablement of innovation. At Yotta, we don’t just provide the legacy colocation, we deliver AI-optimized colocation infrastructure engineered to scale, perform, and adapt to the most demanding compute workloads of our time. Whether you’re building the next generative AI model, deploying inference engines across the edge, or running complex simulations in engineering and genomics, Yotta provides a foundation designed for what’s next. The era of GPU-native infrastructure has arrived and it lives at Yotta.

Partnering for the AI Ascent: Critical role of Colocation Providers in the AI Infrastructure Boom

Posted on by yotta

As the global race to adopt and scale artificial intelligence (AI) accelerates from generative AI applications to machine learning-powered analytics, organisations are pushing the boundaries of innovation. But while algorithms and data often take center stage, there’s another crucial component that enables this technological leap: infrastructure. More specifically, the role of colocation providers is emerging as pivotal in AI driven transformation.

According to Dell’Oro Group, global spending on AI infrastructure including servers, networking, and data center hardware is expected to reach $150 billion annually by 2027, growing at a compound annual growth rate (CAGR) of over 40%. Meanwhile, Synergy Research Group reports that over 60% of enterprises deploying AI workloads are actively leveraging colocation facilities due to the need for high-density, scalable environments and proximity to data ecosystems. AI’s potential cannot be unlocked without the right physical and digital infrastructure and colocation providers are stepping in as strategic enablers of this transformation.

The Infrastructure Strain of AI

Unlike traditional IT workloads, AI applications require massive computational resources, dense GPU clusters, ultra-fast data throughput, and uninterrupted uptime. Traditional enterprise data centers, originally designed for more moderate workloads, are increasingly unable to meet the demands of modern AI deployments. Limitations in power delivery, cooling capabilities, space, and network latency become significant bottlenecks. Enterprises that attempt to scale AI on-premises often face long lead times for infrastructure expansion, high capital expenditures, and operational inefficiencies. This is where colocation data centers offer a compelling, scalable and efficient alternative.

Why Colocation is the Backbone of AI Deployment

1. Rapid Scalability: AI projects often require rapid scaling of compute power due to the high computational demands of training models or running inference tasks. Traditional data center builds, or infrastructure procurement, can take months, but with colocation, companies can quickly expand their capacity. Colocation providers offer pre-built, ready-to-use data center space with the required power and connectivity. Organisations can scale up or down as their AI needs evolve without waiting for the lengthy construction or procurement cycles that are often associated with in-house data centers.

2. High-Density Capabilities: AI workloads, particularly those involving machine learning (ML) and deep learning (DL), require specialised hardware such as Graphics Processing Units (GPUs). These GPUs can consume significant power, with some racks filled with GPUs requiring 30kW or more of power. Colocation facilities are designed to handle such high-density infrastructure. Many leading colocation providers have invested in advanced cooling systems, such as liquid cooling, to manage the extreme heat generated by these high-performance computing setups. Additionally, custom rack designs allow for optimal airflow and power distribution, ensuring that these systems run efficiently without overheating or consuming excessive power.

3. Proximity to AI Ecosystems: AI systems rely on diverse data sources like large datasets, edge devices, cloud services, and data lakes. Colocation centers are strategically located to provide low-latency interconnects, meaning that data can flow seamlessly between devices and services without delays. Many colocation facilities also offer cloud on ramps, which are direct connections to cloud providers, making it easier for organisations to integrate AI applications with public or hybrid cloud services. Additionally, peering exchanges allow for fast, high-volume data transfers between different networks, creating a rich digital ecosystem that supports the complex and dynamic workflows of AI.

4. Cost Optimisation: Building and maintaining a private data center can be prohibitively expensive for many organisations, especially startups and smaller enterprises. Colocation allows these companies to share infrastructure costs with other tenants, benefiting from the economies of scale. Instead of investing in land, physical infrastructure, cooling, power, and network management, businesses can rent space, power, and connectivity from colocation providers. This makes it much more affordable for companies to deploy AI solutions without the large capital expenditures associated with traditional data center ownership.

5. Security & Compliance: AI applications often involve handling sensitive data, such as personal information, proprietary algorithms, or research data. Colocation providers offer enterprise-grade physical security (such as biometric access controls, surveillance, and on-site security personnel) to ensure that only authorised personnel have access to the hardware. They also provide cybersecurity measures such as firewalls, DDoS protection, and intrusion detection systems to protect against external threats. Moreover, many colocation facilities are compliant with various regulatory standards (e.g., DPDP, GDPR, HIPAA, SOC 2), which is crucial for organisations that need to meet legal and industry-specific requirements regarding data privacy and security.

Yotta: Leading the Charge in AI-Ready Infrastructure

    While many colocation providers are only beginning to adapt to AI-centric demands, Yotta is already several steps ahead.

    1. Purpose-Built for the AI Era: Yotta’s data centers are designed with AI workloads in mind. From ultra-high rack densities to advanced cooling solutions like direct-to-chip liquid cooling, Yotta is ready to host the most demanding AI infrastructure. Their facilities can support multi-megawatt deployments of GPUs, enabling customers to scale seamlessly.

    2. Hyperconnectivity at Core: Yotta’s hyperscale data center parks are strategically designed with hyperconnectivity at the heart of their architecture. As Asia’s largest hyperscale data center infrastructure, Yotta offers seamless and direct connectivity to all major cloud service providers, internet exchanges, telcos, and content delivery networks (CDNs). This rich interconnection fabric is crucial, especially for data-intensive workloads like Artificial Intelligence (AI), Machine Learning (ML), real-time analytics, and IoT. We also actively implement efficient networking protocols and software-defined networking (SDN) to optimise bandwidth allocation, reduce congestion, and support the enormous east-west traffic typical in AI training clusters. The result is greater throughput, lower latency, and improved AI training times.

    3. Integrated AI Infrastructure & Services: Yotta is more than just a space provider — it delivers a vertically integrated AI infrastructure ecosystem. At the heart of this is Shakti Cloud, India’s fastest and largest AI-HPC supercomputer, which offers access to high-performance GPU clusters, AI endpoints, and serverless GPUs on demand. This model allows developers and enterprises to build, test, and deploy AI models without upfront infrastructure commitments. With Shakti Cloud:

    – Serverless GPUs eliminate provisioning delays enabling instant, usage-based access to compute resources.

    – AI endpoints offer pre-configured environments for training, fine-tuning, and inferencing AI models.

    – GPU clusters enable parallel processing and distributed training for large-scale AI and LLM projects.

    Additionally, Yotta provides hybrid and multi-cloud management services, allowing enterprises to nify deployments across private, public, and colocation infrastructure. This is critical as many AI pipelines span multiple environments and demand consistent performance and governance. From infrastructure provisioning to managed services, Yotta empowers businesses to focus on building and deploying AI models not managing underlying infrastructure.

    4. Strategic Geographic Advantage: Yotta’s data center parks are strategically located across key economic and digital hubs in India including Navi Mumbai, Greater Noida, and Gujarat ensuring proximity to major business centers, cloud zones, and network exchanges. This geographic distribution minimises latency and enhances data sovereignty for businesses operating in regulated environments. Additionally, this pan-India presence supports edge AI deployments and ensures business continuity with multi-region failover and disaster recovery capabilities.

    The Future of AI is Built Together

    As organisations race to capitalise on AI, the importance of choosing the right infrastructure partner cannot be overstated. Colocation providers offer the agility, scale, and reliability needed to fuel this transformation. And among them, Yotta stands out as a future-ready pioneer, empowering businesses to embrace AI without compromise. Whether you’re a startup building your first model or a global enterprise training LLMs, Yotta ensures your infrastructure grows with your ambitions.