AI changed what a data center has to do. A rack of web servers draws a few kilowatts. A rack of modern GPUs can draw many times that, and it runs hot around the clock. If your facility was designed for the old load, your AI project will stall on power, cooling or both, long before it stalls on software.
This guide explains what an AI data center is, which requirements matter most, and how to choose a provider you can trust with expensive hardware and sensitive data.
What is an AI data center?
An AI data center is a facility built to host high-density compute for training and running machine learning models. It differs from a traditional enterprise data center in four ways:
- Power density. GPU clusters need far more power per rack.
- Cooling. Air alone stops working at high density. Liquid cooling becomes standard.
- Network. Training and inference move large volumes of data, and users expect low latency.
- Resilience and control. AI workloads often hold proprietary models and regulated data, so uptime and location both matter.
AI data centers and HPC (high-performance computing) environments overlap heavily. Both depend on the same things: dense power, serious cooling and fast connectivity.
HPC: the foundation of AI infrastructure
HPC is the discipline of running very large computations across tightly coupled hardware. Scientific simulation, financial modeling, genomics and AI training all use it.
MedOne has operated HPC environments for over 15 years. Its data centers support up to 200kW of power per rack, with several cooling approaches to match the hardware:
- Direct liquid cooling (DLC) brings coolant to the chip. It is the usual choice for current GPU servers.
- Immersion cooling submerges hardware in a dielectric fluid for the highest densities.
- In-row cooling places cooling units between racks to handle hot spots.
- Rear door cooling removes heat at the back of the rack and suits mixed environments.
The right method depends on your servers. A good provider helps you choose, and does not push one design.
SLA: what 99.999% means for AI workloads
An SLA is a written commitment, not a marketing number. MedOne's service level agreement is 99.999%, supported by Tier-III and Tier-IV class infrastructure in its data centers in Israel. At that level, permitted downtime is about five minutes per year.
For AI this matters more than it does for ordinary hosting. A training run that lasts days or weeks can lose progress if power or cooling drops. Inference services that sit behind a product need to stay on. When you read any SLA, check four things:
- What it covers. Power, cooling, network, or all three.
- How it is measured. Monthly or yearly, and who records it.
- What happens on a miss. Credits, remedies and escalation.
- What stands behind it. Redundant power paths, backup generation and tested failover.
MedOne facilities are built underground and designed to run at full capacity for 72 consecutive hours without outside support. That kind of survivability is part of what makes the SLA credible.
Location, latency and interconnection
AI services are only as fast as the network that reaches them. Israel is a connectivity bridge between the Middle East and Western Europe, and MedOne sits at the center of that traffic.
MedOne operates a carrier-neutral interconnection hub with direct access to network operators, global content and cloud providers, and the submarine cable landing stations and national fiber networks that serve Israel. Carriers can deploy diverse networks through multiple entry points in each facility. Customers can connect across MedOne sites over a redundant topology, with low-latency private links to partners and public cloud.
For AI teams this means three practical benefits: lower latency to users and data sources, private paths to the hyperscalers for hybrid designs, and more than one route out if a link fails.
Data sovereignty and security
Many AI projects use data that cannot leave the country: patient records, financial data, government and defense workloads. Hosting in Israel under local control keeps the data, and the models trained on it, inside Israeli jurisdiction.
MedOne serves government ministries, financial institutions, telecom operators and global technology companies. Its sovereign cloud and colocation services give you a choice: run your own GPU hardware in colocation, use managed infrastructure, or combine them in a hybrid design. In all cases you keep control over where your data sits and who can reach it.
How to choose an AI data center provider
Use this checklist when you compare providers.
- Power per rack, today. Ask for the density they deliver now, in kW per rack, and the contracted capacity you can grow into. Plans for future capacity do not run your cluster.
- Cooling options. Confirm direct liquid, immersion or rear door cooling is available for your hardware, and ask how it is operated and monitored.
- SLA and track record. Read the SLA terms. Ask how long the provider has run high-density environments.
- Network and ecosystem. Look for carrier neutrality, multiple carriers on site, cloud on-ramps and subsea access.
- Sovereignty and compliance. Check location, certifications and who can access your environment. MedOne works to nine security standards.
- Resilience of the building itself. Underground, hardened sites with long autonomous runtime protect against physical risk, not only electrical faults.
- Hybrid flexibility. Your needs will change. A provider with colocation, private cloud and public cloud connectivity lets you move workloads without moving buildings.
- Service and people. Ask who answers at 3 a.m. and how fast. Remote hands and an experienced operations team save projects.
Planning your move
Start with your workload: how many GPUs, what power per rack, which cooling method, and which data must stay in Israel. Bring those numbers to a provider early. Power and cooling are the long lead items, and a short design conversation saves months.
For more on power, cooling and real-world limits, read How to Choose an AI Ready Data Center in Israel in 2026 and AI Infrastructure in Israel: Why Most Data Centers Aren't Ready.
FAQ
What makes a data center "AI-ready"?
High power per rack, liquid cooling, fast low-latency networking and strong resilience. MedOne supports up to 200kW per rack with direct liquid, immersion, in-row and rear door cooling.
What is the difference between HPC and an AI data center?
HPC is the broad practice of running large computations on tightly coupled hardware. An AI data center applies that infrastructure to model training and inference. The facility needs are nearly the same.
What SLA should I expect?
For production AI, look for 99.999% backed by Tier-III or Tier-IV class infrastructure, with clear measurement and remedies. MedOne's SLA is 99.999%.
Can I keep AI data in Israel?
Yes. Hosting in MedOne's Israeli facilities keeps your data and models in-country, with carrier-neutral connectivity to global clouds when you need it.
Can I mix colocation and cloud?
Yes. MedOne supports hybrid designs that connect colocation, sovereign cloud and public cloud through private interconnection.