Why your Kubernetes clusters and GPUs should live under one roof

The world remains abuzz with AI hype, but the reality is that most modern applications aren’t purely AI workloads. The average company will have web services, APIs, databases, and background jobs running alongside its machine learning inference or training components. An architecture question everyone faces: should your Kubernetes cluster and GPU compute live in the same data center, or can you split them across providers?

If you’re building anything beyond a pure AI research project, the answer is clear: a unified solution in co-location wins.

The hidden cost of distance

When your application calls an AI service hundreds or thousands of times per day, network latency compounds fast. A recommendation engine that adds 50ms of cross-datacenter latency to every product page load directly impacts conversion rates. A customer service chatbot that takes an extra 100ms to respond feels sluggish. These aren’t theoretical concerns; they’re revenue impacts.

Beyond latency, there’s bandwidth cost. Hyperscale cloud providers charge egress fees that can blindside engineering teams. Send training data to a remote GPU cluster? That’s egress. Stream model checkpoints back? More egress. Push inference results to your application tier? Egress again. These charges accumulate into five or six-figure annual bills for data-intensive AI workloads.

What is the Kubernetes-first cloud advantage?

Civo pioneered the Kubernetes-first cloud model, launching production-ready clusters in under 90 seconds. This architectural choice matters when you’re running hybrid workloads. Your CPU-bound services (web apps, APIs, microservices) and GPU-intensive AI components can share the same cluster, orchestrated by the same control plane, with native networking between them.

The alternative, managing GPU instances separately from your Kubernetes cluster, creates operational complexity. You’re now maintaining two infrastructure stacks, two deployment pipelines, and two monitoring systems. When your AI service needs to scale up, you’re coordinating across platforms instead of letting Kubernetes handle it natively.

Zero egress changes everything

Civo doesn’t charge for data transfer. Period. This isn’t a promotional offer or a tier-limited feature; it’s an architectural and customer-centric philosophy. AI innovation is driven by data, and data needs to flow freely between your application components and your GPU compute without financial penalty.

For teams running continuous training pipelines or high-volume inference, this eliminates an entire category of cost optimization work. You can architect for performance and developer experience instead of designing around egress fees.

Built for production AI workloads

With NVIDIA A100, H100, and B200 GPUs available directly within the same platform that runs your Kubernetes clusters, you’re getting enterprise-grade GPU compute with the operational simplicity of a unified cloud provider. Deploy your web application, your inference service, your training jobs, and your data pipelines all within the same region, on the same platform, managed through familiar tools like Terraform, Helm, or the Civo CLI.

The Kubernetes GPU operator integrates seamlessly, giving you container-native GPU scheduling without vendor-specific tooling. Your team writes Kubernetes manifests, not bespoke deployment scripts for multiple clouds.

The bottom line

If your application has both standard compute needs and AI components that need to work together, infrastructure fragmentation is a self-imposed tax. Co-locating your Kubernetes cluster and GPU compute in a unified platform eliminates latency, removes egress fees, simplifies operations, and lets your team focus on building products instead of managing multi-cloud networking.

Civo offers this unified model with transparent pricing, deployment speeds under 90 seconds, and zero egress fees. For teams building the next generation of AI-powered applications, that’s not just convenience, it’s a competitive advantage.

You can do things the way the hyperscalers have had it done for a long time (and pay for it), you can leverage a neo-cloud and maintain two separate data centers, or you can choose Civo and have everything you need under one roof.