India’s AI data centre reset is reshaping infrastructure economics and partner roles
Rising AI workload density, cooling constraints, and sovereignty demands are redefining how data centres are built, sold, and operated.
This is pushing operators away from traditional colocation models towards AI‑specific infrastructure services, reshaping how capacity is sold, where margins are captured, and how enterprise relationships are structured. Channel partners, in turn, are being pulled up the stack as demand rises for integration, optimisation, and long‑term operation of AI workloads.
Executives at data‑centre operators tell CRN India the shift is already underway, with visible impact on facility design, deal sizes, and go‑to‑market strategies.
AI workload density reshapes buying decisions
Across operators, AI workload density has emerged as a primary filter in enterprise buying decisions. Rack densities that previously sat in the 5–10 kW range are now being evaluated at significantly higher levels, driven by GPU‑based training and inference workloads.
Yotta Data Services’ chief revenue officer and president, Nitin Jadhav, said AI‑led deployments are changing deal structures, with higher compute concentration leading customers to plan larger engagements upfront.
Transactions tied to AI environments are coming in at two to four times the size of traditional enterprise deployments, he said.
Jadhav added that the buyer base is also expanding, with AI startups, global capability centres (GCCs), and enterprises building dedicated AI environments entering the market alongside hyperscalers.
At the enterprise level, the shift is not limited to large training clusters.
Colt Data Centre Services’ India sales director, Arif Khan, said organisations are deploying inference‑heavy AI workloads tied to analytics, automation, and internal productivity platforms. While smaller in scale, these still introduce higher power variability and thermal requirements compared with legacy IT environments, influencing design specifications and site selection.
Alongside density, sovereignty is increasingly shaping final decisions, particularly for regulated sectors.
Rackbank’s founder and CEO, Narendra Sen, said enterprise focus has shifted from performance and scalability towards control over data access and residency.
Procurement processes now frequently include data‑sovereignty clauses as non‑negotiable requirements, prompting re‑evaluation of providers and deployment models.
Revenue moves up the stack to AI infrastructure services
All three operators point to AI infrastructure services as the fastest-growing revenue layer across the data centre stack. This includes high-density GPU deployments, AI-ready cooling systems, and infrastructure designed for sustained, power-intensive workloads.
The demand is moving beyond traditional colocation, with operators building purpose-specific environments for AI use cases.
Sen said Rackbank holds core infrastructure and compute contracts directly, with partners supporting solutioning and deployment. This model allows the operator to retain control of the infrastructure layer and capture a larger share of value associated with AI deployments.
At Colt, monetisation is increasingly layered. Khan said operators derive value from power, space, and AI‑ready infrastructure, while system integrators and managed service providers generate revenue through architecture design, integration, and ongoing operations.
Cloud providers continue to monetise large‑scale AI consumption.
At Yotta, Jadhav said value is distributed across the ecosystem.
Cooling architecture moves from facility feature to deal qualifier
AI workloads have begun reshaping how data centres in India are designed and evaluated, with cooling architecture emerging as the most immediate change. Operators say air‑cooled designs built for 5–10 kW enterprise workloads are no longer adequate as GPU‑intensive AI deployments push rack densities sharply higher.
At Rackbank, this shift has translated into designing racks capable of supporting up to 150 kW for GPU clusters.
Sen said “Air cooling cannot handle this level of heat density at scale”, prompting the development of liquid‑based cooling systems as a prerequisite for competing in AI infrastructure.
The shift has materially raised the expectations placed on partners.
Yotta is seeing a similar redesign cycle. Jadhav said data centres are increasingly being planned with future AI workloads in mind, building for higher densities and sustained performance from day one rather than relying on later retrofits.
Jadhav said the shift is expanding the role of partners, creating opportunities for those able to integrate liquid‑cooled environments, optimise AI workloads for high‑density infrastructure, and support ongoing operations at scale.
A similar transition is underway at Colt Data Centre Services. Khan said cooling architecture has become a front‑line design consideration as rack densities increase.
He said hybrid designs help support higher densities while managing energy efficiency and operational performance, and are often implemented using standardised reference designs that can be adapted to local conditions, including power availability, climate, and regulatory requirements in India.
Khan added there are new requirements for partners, including an understanding of thermal planning at the rack and workload level, how AI workloads behave under sustained load, and how cooling decisions affect performance, resilience, and operating cost.
Partner capabilities reset as AI moves into production
As AI workloads move into production, data centre operators say the most valuable partner skill over the next two to three years will be integrating and optimising AI workloads, rather than simply provisioning infrastructure. Partners are now expected to understand how GPU‑intensive workloads perform at scale and how AI environments are designed and operated to deliver measurable outcomes.
Jadhav said customers now expect partners to balance cost, performance, and scalability across GPU environments, improve utilisation, and ensure AI infrastructure investments translate into results.
The scope for differentiation in simply provisioning space and power is narrowing, he added, with higher value emerging above the infrastructure layer, through optimisation, integration, and operations at scale.
According to Khan, partners that can bridge physical infrastructure and workload performance will be best positioned as AI environments become more complex.
This includes designing and operating AI platforms that align compute, power, cooling, connectivity, and cloud services, rather than treating the data centre as a static facility.
He said “partners that can take generative AI use cases from proof‑of‑concept to production” on sovereign infrastructure, and manage them end‑to‑end, will be best placed as India moves from AI experimentation to deployment at scale.
Sen contrasted this with traditional cloud migration work, which he said has become commoditised. Basic application and database migrations now offer limited margin upside, while rising compute costs and data‑localisation requirements are pushing enterprises towards private AI clouds and sovereign GPU‑as‑a‑service models.
These shifts, he said, demand specialised AI infrastructure and engineering skills.
Enterprise ownership fragments as data centres move up the stack
As data centres move up the stack from space and power to AI infrastructure and platforms, operators agree that the enterprise relationship is no longer owned by any single player.
Instead, it is increasingly shared across a defined ecosystem, with responsibilities splitting based on where value and risk sit.
Sen said channel partners continue to own the long‑term enterprise relationship, driven by trust and proximity to business workflows, while data centre operators remain directly accountable for the underlying infrastructure, particularly around high‑density design and data sovereignty.
He said partners typically lead application, integration, and managed services, while operators stay direct on infrastructure architecture and compliance, given the operational and regulatory exposure involved.
Khan said operators remain a central anchor for the physical and operational environment, while partners lead architecture design, workload integration, and optimisation above the infrastructure layer.
Hyperscalers, he added, engage at the cloud services layer, but do not intermediate the physical environment when infrastructure is deployed in third‑party data centres.
Jadhav said infrastructure providers anchor the platform, partners drive solution execution, and network and cloud platforms extend capabilities, with success determined by how effectively these layers are coordinated around business outcomes.