Looking to Scale AI Infrastructure? Don’t Overlook Maintenance Costs

Over the past several years, hyperscalers in the AI business have focused on building fast — without waiting for a revenue model to take root. However, they are now facing a threat to profitability: infrastructure costs that are getting out of hand.

Of all such costs, the cost to maintain is the most controllable, yet the most neglected. How can procurement and supply chain leaders manage risk, ensure uptime, and sustain long-term value from their infrastructure?

They should

• Think Strategically: View maintenance not as a cost center, but as a lever for ROI.
• Invest in Intelligence: Partner with third-party maintenance (TPM) providers who leverage AI and automation.
• Balance Cost and Risk: Find the right maintenance balance by applying data-driven methodologies.
• Build Flexibility: Design flexible contracts with TPM providers to avoid vendor lock-in while ensuring performance.

Think strategically

AI infrastructure carries mainly three types of cost: the cost to build, the cost to serve, and the cost to maintain. In AI, the cost to build data centers is already spent, and the cost to serve AI users is soaring. The battle for profitability will thus center on how efficiently and cost-effectively hyperscalers, such as Google, Meta and OpenAI, can maintain these data centers.

As generative AI transitions from cutting-edge innovation to enterprise-grade infrastructure, the focus of conversation is shifting. 

The core topic is no longer about how powerful a model is, but how efficiently it can be deployed and sustained. What anchors this shift is a growing recognition of inference economics — the cost of running AI at scale. Inference economics should be built into TCO and ROI models, especially for internal tools like AI copilots and developer platforms.

AI profitability hinges on this equation:

Gross Profit = Revenue – (Operational Cost Per Token x Token Volume) – Maintenance Cost
This equation now governs all hyperscalers’ AI businesses.

Invest in Intelligence

Given the technical depth and operational intensity of AI data center maintenance, many organizations engage third-party or specialized service providers to support operations. 
As AI data centers grow more complex, third-party maintenance providers are evolving beyond their traditional roles as hardware repair vendors. Hyperscalers should leverage these providers to build technological intelligence into maintenance. 

TPM providers are today harnessing a suite of emerging technologies to shift from the break-fix models to insight-driven service delivery. The following innovations are driving this shift:

AI-Powered Predictive Maintenance

TPM providers use machine learning models trained on huge datasets drawn from real-time telemetry, historical failure logs, and environmental variables to predict component failures ahead of time. Key input parameters include temperature fluctuations and voltage and current anomalies.

For example, a TPM provider leveraged predictive models to anticipate a cooling fan failure on a GPU node 14 days before thermal degradation occurred, allowing seamless intervention with zero downtime.

Digital Twin Technology

Digital twins replicate the physical environment of a data center, such as racks, servers, power systems, and cooling infrastructure, into a virtual environment. This allows TPM providers to simulate “what-if” scenarios for breakdown prediction and maintenance scheduling.

Use cases include proactive risk assessment for high-load periods or seasonal temperature changes.

Using this technology, TPM providers can manage maintenance with minimal impact on performance by running simulations that identify optimal windows for intervention.

Remote Monitoring and Automation Platforms

Cloud-native platforms provide TPM providers and clients with a centralized dashboard for overseeing infrastructure health, often integrating with existing data center infrastructure management (DCIM) and AIOps tools.

Core capabilities include predictive alerting and automated ticketing.

These platforms cut the need for physical presence, accelerate mean time to repair (MTTR), and improve data-driven decision-making for facility managers.

Edge Analytics and IoT Integration

In AI environments, where latency and performance variability can compromise training or inference cycles, TPM providers deploy smart edge devices embedded into racks, power distribution units (PDU), and cooling systems.

For instance, if a PDU reports erratic current loads beyond a set threshold, local edge analytics can initiate power redistribution or trigger a pre-emptive node migration, avoiding bigger outages.

Blockchain for Maintenance Record Integrity

Blockchain offers a secure and immutable ledger for recording all maintenance activities, part replacements, firmware updates, and system changes.

The benefits include auditability (e.g., compliance with regulation) and accountability (e.g., traceability for SLA disputes).

Some TPM providers are also exploring smart contracts (automated, blockchain-encoded agreements) to trigger service delivery and payments based on uptime performance.

Augmented Reality (AR) and Remote Assist Tools 

Top TPM providers are adopting AR headsets and mobile apps to enable guided remote support. Onsite staff can perform complex maintenance tasks with real-time assistance from remote experts.

Use cases include visual overlays for hardware replacements and remote diagnostics through live-streamed visuals.

This approach reduces travel-related delays and increases first-time fix rates.

Balance Cost and Risk

While outsourcing can offer scale, expertise, and efficiency, procurement leaders need to consider the decision across both strategic and operational areas.

One of the challenges of outsourcing is data security and compliance risks. Granting access to hardware, logs, and telemetry can introduce compliance risks under frameworks such as GDPR, HIPAA, or export control regulations.

For procurement directors and program managers, the outsourcing decision must align with broader goals related to business continuity, cybersecurity, and scalability. A hybrid approach — outsourcing routine or non-differentiating tasks while retaining ownership of strategic components — often provides the right balance between control, efficiency, and resilience.

Prioritize Flexibility

Long-term contracts or proprietary platforms can reduce flexibility and make it difficult to pivot as technology or business evolves. So, when engaging TPM providers, AI enterprises should be mindful of the risk and work a clause into their contract to guard against this risk.

As long as procurement and program leaders balance the risks of outsourcing and maintenance costs, TPM providers can greatly help in containing them. With rising capital investment and increasingly demanding workloads, enterprises should adopt smart strategies to make the most of AI infrastructure.

To learn more about how hyperscalers can make the most of AI data center infrastructure, download our white paper, Data Center Maintenance Costs: The Hidden Risk to AI Profitability (And How To Fix It).