AI Inference Doesn’t Belong in the Cloud –Alone

Over the past year, we’ve seen a meaningful shift in how infrastructure teams approach AI inference. While cloud training remains essential, serving AI models from hyperscale regions is often overkill or simply not practical. Here’s why metro-edge infrastructure is becoming a go-to strategy for high-performance inference, and how we’re helping teams get there.

Make AI Work Where It Actually Happens

AI inference doesn’t belong in a data center 2,000 kilometers away. It belongs where your users are—where the action is. That’s why more teams are shifting inference to the metro edge. Less lag, lower cost, better control. It just makes sense.

Why Metro Edge? Because It Works Better—for the Business

• Ultra-low Latency Without the Complexity
  Inference workloads thrive on fast, predictable response times. Whether it’s delivering personalized recommendations, powering fraud detection, or enabling real-time video analytics, speed matters. Metro-edge colocation gives you sub-10ms latency from major European cities, with none of the operational overhead of far-edge deployments. Your teams stay focused on building, not babysitting hardware.
  
  
• Data Sovereignty Without Guesswork
  The further AI stretches into regulated sectors—finance, healthcare, government—the more critical it becomes to control where data lives. Colocation restores physical and jurisdictional control over your infrastructure. You know where your data is. You know who can access it. And you can prove compliance, whether you’re answering to GDPR, national regulations, or enterprise clients with strict location requirements.
  
  
• Cloud Costs Add Up. Fast.
  Running inference 24/7 in the cloud can look simple on paper, but operationally, it’s another story. Between escalating GPU prices, data egress fees, and bandwidth costs, the economics often break down at scale. Metro-edge colocation gives you predictable cost structures and the ability to right-size your infrastructure over time. Add in high-density racks and Remote Hands, and you can build real capacity—without building a local team.

A Pragmatic Model: Train in the Cloud, Deploy at the Edge

Metro edge is not a niche trend. It’s a logical step in AI infrastructure evolution. Train large models in centralized cloud environments where elasticity is king. But when it comes to serving those models, where milliseconds matter and regulations apply, the edge is the right tool for the job.

What Can You Learn From This?

If you’re building AI infrastructure, look beyond the hype and assess where inference actually needs to happen. Think in terms of latency zones. Consider data boundaries. Track your real cloud costs. Then design a hybrid architecture that gives you both reach and control.

How Kolo can help

Kolo is a Northern European colocation platform with facilities in the Netherlands, Denmark, and Sweden. We off

• Metro colocation in NL, DK, and SE—carrier-dense, ISO-certified, and interconnected
• High-density racks designed for GPUs and accelerators
• Remote Hands in all locations
• Access to low-cost, renewable energy (especially in Sweden)
• Private connectivity to clouds, carriers, and ecosystem partners

We help infrastructure teams deploy AI where it performs best—and where the economics and compliance make sense.

Read about our AI-solutions.