AI-Ready Data Centres: What Singapore Firms Should Copy

TCS just did something most enterprise tech teams in Asia don’t do early enough: it treated AI infrastructure as a financing and strategy problem, not just an IT problem.

In late 2025, Tata Consultancy Services (TCS) brought in global asset manager TPG with SGD 1.31 billion (US$1B) to co-fund expansion of its HyperVault platform—an AI data centre effort targeting more than 1GW of capacity over the next few years. The project is designed for high rack density, liquid cooling, and strong regional connectivity.

For this AI Business Tools Singapore series, that deal is useful because it reveals a practical truth: your AI roadmap will hit a wall if compute, data residency, latency, and cost aren’t planned as deliberately as your model selection. The companies that win with AI in 2026 won’t be the ones with the most pilots—they’ll be the ones that can run AI reliably, securely, and affordably.

What the TCS–TPG HyperVault deal really signals

Answer first: The HyperVault funding story is about scaling AI compute with lower risk and faster timelines—exactly the pressure enterprises face when AI moves from demos to production.

TCS said the partnership is expected to reduce capital outlay and improve returns to shareholders, while creating long-term value in the data centre platform. The structure is also telling: TCS and TPG plan to invest up to SGD 2.65 billion (INR 18,000 crore) via a mix of equity and debt, with TPG contributing up to INR 8,820 crore and taking 27.5% to 49% of the business.

That’s not a vanity announcement. It’s an admission that:

• AI-ready facilities (power, cooling, connectivity) are expensive and take time.
• Demand isn’t theoretical—hyperscalers, AI companies, and enterprises are already competing for capacity.
• The winners will treat infrastructure as a product platform, not a “back-office” asset.

Why “1GW” matters even if you’re not a hyperscaler

Answer first: Gigawatt-scale plans mean the region is shifting from general-purpose cloud capacity to AI-optimised power and cooling.

A 1GW pipeline signals heavy investment into the fundamentals that drive AI cost: electricity supply, thermal management, and density per square metre. As models get bigger and inference volumes rise, cost and reliability depend less on clever prompts and more on whether your compute stack can run at scale without throttling.

For Singapore companies, even if your workloads run in public cloud, these regional capacity decisions influence:

• GPU availability windows
• price stability for high-performance instances
• disaster recovery options within Asia
• latency to your users and data sources

What makes a data centre “AI-ready” (and why it changes your AI costs)

Answer first: “AI-ready” isn’t marketing fluff; it’s a set of design choices that determine whether AI workloads run efficiently or burn money.

HyperVault is described as offering purpose-built, liquid-cooled facilities with high rack densities, energy efficiency, and connectivity across key cloud regions. Those phrases matter because AI workloads stress infrastructure differently than traditional enterprise applications.

Liquid cooling and high rack density: the non-negotiables

Answer first: AI racks can demand so much power that traditional air cooling becomes a bottleneck.

High-density racks pack more compute into less space, but heat becomes the constraint. Liquid cooling removes heat more effectively, allowing higher sustained performance (and fewer “mysterious” slowdowns).

Why should a Singapore CFO care? Because the AI bill you see isn’t just “GPU hours.” It includes:

• idle time due to capacity constraints
• retries and slowdowns from thermal throttling
• overprovisioning for peak demand
• higher unit cost when you can’t commit to predictable usage

Connectivity across cloud regions: the hidden performance lever

Answer first: Connectivity determines latency and data movement cost, which directly affects AI product experience.

Many AI use cases aren’t a single workload in a single place. A typical setup might include:

• data sources in Singapore (CRM, ERP, call recordings)
• model endpoints in a regional cloud zone
• vector database or feature store in another service
• monitoring and security tools elsewhere

If connectivity is weak, you pay in slower response times, brittle pipelines, and rising egress fees.

The Singapore angle: how to benefit without building a data centre

Answer first: Singapore businesses don’t need to own infrastructure to benefit—but you do need to design your AI adoption plan around where compute and data will live.

The HyperVault story is a regional infrastructure build-out. Singapore companies can treat it as a prompt to tighten their own AI execution.

A practical decision: “Where will this AI run?”

Answer first: Every production AI project needs an explicit compute-and-data placement decision.

For Singapore firms, the common options are:

1. Public cloud (managed AI services) for speed and elasticity
2. Colocation / regional AI data centres for predictable performance and cost
3. Hybrid for sensitive data + scalable inference/training

What I’ve found works: decide this before the pilot becomes popular internally. Popular pilots become political, and then infrastructure decisions get delayed—right when demand spikes.

Three Singapore use cases that get stuck without AI-ready infrastructure

Answer first: The projects that fail first are the ones with continuous inference, large data, and strict uptime.

1. Customer service copilots (multilingual, real-time)

   • Needs low latency and stable throughput
   • Falls apart when concurrency grows

2. Marketing personalisation at scale

   • Real-time segmentation + content generation
   • Gets expensive fast if data movement is inefficient

3. Operations forecasting (inventory, logistics, workforce)

   • Model refresh cycles + large feature pipelines
   • Becomes brittle when pipelines span too many regions

These are exactly the “AI for marketing, operations, and customer engagement” outcomes this series focuses on. The difference between a nice demo and a dependable capability is infrastructure planning.

Partnerships are the playbook: what SMBs and mid-market firms can copy

Answer first: The most transferable idea from TCS–TPG isn’t the 1GW target—it’s the partnership model.

TCS didn’t say, “We’ll fund everything ourselves.” They brought in a financial partner to share risk and accelerate capacity.

Singapore businesses can apply the same thinking at a smaller scale:

Copy the pattern, not the price tag

Answer first: Pair domain owners, IT, and external partners early so AI can move from pilot to production.

Examples of partnership patterns that work:

• Business + IT + AI solution partner: clear ownership of outcomes (conversion, AHT reduction, churn)
• Cloud provider + security advisor: prevents last-minute compliance delays
• Data partner + internal ops team: ensures data quality doesn’t block rollout

This approach also helps avoid a common trap: buying “AI tools for business” and discovering later that identity, access, data pipelines, and monitoring were never prepared.

A simple scorecard for choosing AI infrastructure options

Answer first: Pick infrastructure based on workload shape, not hype.

Use this checklist when comparing cloud, private hosting, or regional AI-ready facilities:

• Latency target (e.g., under 1s for chat experiences)
• Concurrency expectations (how many users at peak)
• Data residency / governance needs (policy and client requirements)
• Cost predictability (on-demand vs committed spend)
• Reliability (multi-zone, DR, incident response)
• Observability (traceability, model monitoring, audit logs)

If you can’t answer these, you’re not “not ready for AI.” You’re just missing the planning layer that turns tools into outcomes.

What to do next: a 30-day plan for Singapore teams adopting AI

Answer first: The fastest path to ROI is to pick one production use case, map its infrastructure needs, then build guardrails before scaling.

Here’s a pragmatic 30-day sequence I recommend for teams adopting AI business tools in Singapore:

Week 1: Choose a production-worthy use case

Pick something with measurable outcomes and real users:

• reduce customer service handling time by 15%
• increase qualified leads by 10%
• cut manual reporting hours by 30%

Week 2: Map your “AI supply chain”

Document the full path:

• data sources → data processing → model endpoint → app → monitoring

Write down where each component lives today and where it should live for latency, cost, and governance.

Week 3: Decide your hosting and capacity model

Make an explicit choice:

• start on managed cloud AI services
• reserve capacity for predictable inference
• design a hybrid approach for sensitive data

Week 4: Put in the boring stuff (that saves you later)

• access controls and audit logs
• prompt and data retention policies
• evaluation metrics (quality, safety, drift)
• incident runbooks for outages and bad outputs

A useful rule: if you can’t monitor it, you can’t scale it.

The HyperVault announcement is a reminder that infrastructure is part of the product. Treat it that way.

Where this is heading in 2026

Answer first: AI demand is pushing Asia toward more specialised compute capacity, and enterprises will pay a premium for reliability.

As more platforms like HyperVault come online, Singapore firms will have more regional options for running AI workloads—especially those needing high density compute and strong connectivity. But choice doesn’t automatically reduce cost. The companies that control cost are the ones that:

• standardise a small set of AI use cases that matter
• centralise governance without blocking delivery
• plan infrastructure early (capacity, latency, data movement)

If your 2026 goal is real adoption—not a folder of pilots—start by making your AI workload “place and run” decisions explicit. Which parts run where, and why?

What would change in your business if your AI tools could run with predictable cost and performance for the next 12 months—no drama, no last-minute capacity hunts?