Hello everyone. I've been researching for weeks, reading every comparison and blog post I can find, and I'm hoping to get some detailed feedback on a specific cost and operational analysis before we commit. Our team is planning to deploy a dedicated data science platform for internal use, serving about 25 data scientists and engineers. The workloads are a mix of scheduled batch training jobs, some long-running model APIs, and numerous experimental JupyterHub pods. Data gravity is on Google Cloud Platform (BigQuery, Cloud Storage), but the compute costs there are giving us pause.
We have a detailed prototype running on Google Kubernetes Engine (GKE) in the us-central1 region. With the node pools we need (a mix of standard and high-memory machines, with attached GPUs for specific workloads) and factoring in control plane costs, network egress to the internet, and managed storage, our projected monthly bill is substantial. A colleague with a similar workload mentioned they've had success with a self-managed Kubernetes cluster on Hetzner bare-metal servers, citing dramatically lower hardware costs for equivalent CPU/memory/GPU resources.
My background is in ERP and supply chain systems, where operational reliability and predictable costs are paramount. The potential savings with Hetzner are compelling, but I am deeply cautious about the operational trade-offs. I have a list of specific concerns that I haven't seen fully addressed in a side-by-side comparison for a use case like ours.
First, regarding upgrades: GKE's automated, rolling master and node upgrades are a significant attraction. For a DIY cluster on Hetzner, using a tool like kubeadm or Rancher, what is the real-world time investment and risk for upgrading a production cluster (say, from 1.27 to 1.28)? How do you manage the etcd database upgrades and validate component compatibility without a managed control plane?
Second, networking and load balancing: GKE's integration with Cloud Load Balancing is seamless for exposing services. On Hetzner, we would likely use the Cloud Controller Manager and the Hetzner Cloud CSI driver. Has anyone run into limitations with their load balancers (cost, performance, features) compared to a cloud-integrated solution? Are there hidden complexities in managing ingress controllers and network policies without a unified cloud network fabric?
Third, operational overhead at our scale: We have some DevOps expertise, but the team is small. Beyond the obvious costs of self-patching nodes and monitoring, I'm thinking about disaster recovery. In GKE, we'd rely on managed backups. On DIY, we'd need to design and test etcd recovery procedures. For those running similar mid-sized, stateful clusters on bare metal or Hetzner, how many hours per week does routine maintenance and vigilance consume?
The cost analysis is not just about the invoice from the cloud provider. It must include the fully burdened cost of the engineers who keep it running. I am trying to quantify the trade-off: the higher certainty of GKE's bill versus the potential savings of Hetzner offset by increased internal labor cost and risk. Any detailed experiences, especially from teams who have run data science workloads on both types of platforms, would be incredibly valuable to my decision-making process.