KAYTUS Introduces AI Managed Service for Data Centers

KAYTUS OEM Onsite support reduces hardware recovery time by over 30% and enables repairs in less than four hours

SINGAPORE, Sept. 28, 2026 (GLOBE NEWSWIRE) -- KAYTUS today introduced OEM AI Managed Service, providing integrated hardware maintenance and technical support for AI data centers (AIDCs) and hyperscale clusters. The service combines locally stocked critical replacement components, factory-grade diagnostic and repair capabilities, and certified field service engineers onsite to complete complex hardware repairs in as little as four hours. KAYTUS field operations data indicates that this onsite delivery model reduces hardware recovery time by more than 30% relative to generally available industry service levels, with an average end-to-end incident resolution time of 12 hours per failed node.

This launch addresses the operational demands of AI infrastructure as it transitions from rapid deployment to sustained, reliable production. Gartner projects global AI spending to reach $2.67 trillion in 2026, including approximately $1.48 trillion allocated to AI infrastructure. As clusters scale to thousands of accelerators, hardware failures become an inherent operational challenge, making system availability and recovery time critical service-level agreement (SLA) indicators. Streamlined fault isolation, hardware remediation, and service restoration help preserve schedulable computing capacity and sustain workload continuity.

1. Beyond Deployment: Sustaining Operational Reliability.

Meta’s technical report on Llama 3.1 405B highlights the operational challenges of AI training at scale. During 54 days of pretraining on a cluster of 16,384 GPUs, the system recorded 419 unexpected interruptions, averaging one every three hours. Approximately 78% were attributed to confirmed or suspected hardware failures.

A study presented at SOSP 2025 documented more than 44,000 incidents across over 778,000 training jobs during three months of operation on a large production LLM training platform. These findings underscore the importance of rapid fault detection and recovery in preserving compute availability and sustaining productive capacity as AI data centers scale.

Uptime Institute’s 2026 outage analysis found that 57% of respondents reported costs exceeding $100,000 for their most recent major outage, while one in five reported losses above $1 million. Some compute leasing agreements impose compensation of up to 25% of the monthly rental fee for major SLA breaches. For compute providers, hardware failures compound financial exposure through lost revenue, contractual compensation, continued asset depreciation, and disruption to customer operations.

A smart and effective maintenance strategy is therefore essential to AI data center operations. The time required to restore service directly affects the loss of productive compute capacity during each incident.

2. The Limits of Traditional Maintenance Models.

Modern AI data centers operate at rack power densities exceeding 40 kW, with tightly integrated compute, networking, and cooling infrastructure. A single node failure can disrupt customer AI workloads. These demanding operating conditions, characterized by dense infrastructure and sustained resource utilization, expose several limitations of conventional maintenance models:

• Extended Spare Parts Lead Times: AI data centers deploy heterogeneous hardware with diverse component specifications and configurations. Sourcing critical replacement parts from regional warehouses or transferring inventory between regions can extend delivery times to several days, delaying hardware recovery.

• Prolonged Factory Repair Cycles: Complex node failures may require offsite diagnostics and repair at a factory equipped with specialized tools. Transportation and service queue delays extend node downtime, reducing available compute capacity and increasing exposure to SLA breaches.

• Increased Diagnostic Complexity: AI clusters integrate complex network topologies, dense compute infrastructure, and liquid or air-cooling systems. Faults spanning these domains can prolong diagnosis for conventional operations teams, while remote support alone cannot fully replace specialized onsite expertise.

Together, these maintenance constraints can extend node recovery to 48 hours or more. For compute infrastructure billed by the hour, prolonged downtime directly translates into lost revenue.

3. AI Managed Service: OEM Expertise Delivered Onsite.

KAYTUS AI Managed Service addresses these maintenance gaps by bringing OEM engineering expertise, repair infrastructure, and incident response capabilities directly to AI data centers and hyperscale compute clusters. Built around five core service components, this onsite model delivers measurable service commitments and more predictable recovery times:

• Customized Lifecycle Maintenance: KAYTUS evaluates each customer’s infrastructure and operational requirements to deliver tailored OEM hardware maintenance throughout the equipment lifecycle, supporting both standard deployments and technically complex environments.

• Onsite Critical Spares Inventory: KAYTUS stocks essential replacement hardware, including compute nodes, network switches, and high bandwidth network interface cards (NICs), within the customer’s data center. Immediate access eliminates external dispatch delays and enables hardware repairs in as little as four hours.

• Onsite OEM Diagnostics and Repair: KAYTUS deploys factory diagnostic equipment and specialized repair tools directly at customer sites, enabling engineers to diagnose and repair individual components and complete nodes without returning systems to the factory. In customer deployments, this approach has reduced average incident handling time to 12 hours per failed node, from initial assessment through repair completion.

• Around-the-clock Certified Engineering Support: Under applicable service tiers, engineers certified by the OEM provide continuous 24/7 onsite coverage, backed by dedicated Tier 2 specialists, who respond to unexpected incidents within minutes.
• AI Assisted Failure Prediction and Proactive Inspections: Scheduled health assessments and intelligent failure prediction use AI to detect early signs of hardware degradation, enabling preventive maintenance before service disruptions occur, and reducing reliance on reactive repairs.

“The value of a compute asset is not defined by its scale on day one, but by how reliably it delivers capacity hour after hour throughout its operational lifecycle. KAYTUS AI Managed Service turns hardware recovery from an uncertain wait into a measurable service commitment.”
— Caesar, Head of Services, KAYTUS.

4. Proven in Production: Lower Downtime and SLA Risk for Global CSP.

KAYTUS AI Managed Onsite Service delivered the following operational results at a leading global cloud service provider’s AI data center, supporting more than 100 racks and thousands of accelerators:

Metric	Result
Average handling time	Reduced from 48 hours to 12 hours per failed node
Productive compute time	Achieved a 50% increase, driven primarily by reduced downtime and accelerated recovery, which increased productive operating time for training and inference workloads.
SLA compensation	Reduced downtime and fewer workload restarts lowered the customer’s potential liability for SLA compensation by several million dollars.

AI Managed Service is expanding globally, with current coverage in key European markets, including the United Kingdom, Germany, France, the Netherlands, Finland, Poland, and Iceland, as well as Japan and South Korea in the Asia Pacific region. Deployment models and service levels can be tailored to each customer’s infrastructure scale and operational needs. Service procedures are designed to support local compliance obligations, including applicable GDPR requirements.

Modern AI infrastructure delivers lasting value through sustained performance and reliable service. KAYTUS AI Managed Service can integrate with KAYTUS’s KSManage intelligent operations platform, combining onsite OEM expertise with operations management powered by AI, to improve operational predictability, and support measurable service levels that can be formalized in contractual commitments.

Learn more at: https://www.kaytus.com/about/contact/

About KAYTUS

KAYTUS is a leading provider in AI infrastructure and liquid cooling solutions, delivering a diverse range of innovative, open, and eco-friendly products for cloud, AI, edge computing, and other emerging applications. With a customer-centric approach, KAYTUS is agile and responsive to user needs through its adaptable business model. Discover more at KAYTUS.com and follow us on LinkedIn and X.

Media Contacts: media@kaytus.com