Cloud
Google Cloud
Google's cloud for compute, data, Kubernetes and AI. Google Cloud is Google's public cloud platform, providing compute, storage, networking, managed databases and data analytics. It is known for Google Kubernetes Engine, BigQuery and Vertex AI, which hosts Gemini and other models. It has regions across Europe, including Belgium, the Netherlands and Germany.
When it is the right choice
- You want a strong managed Kubernetes platform (GKE)
- Your workload depends on large-scale analytics in BigQuery
- You want Gemini, Claude and open models through Vertex AI in one project
- Not ideal if your team's skills and existing contracts are all on another cloud; migration has a real cost
How we use it
- Build projects, networks and GKE clusters with Terraform, applied through CI/CD
- Set up IAM, organisation policies and logging as a reviewed baseline
- Deploy AI services on Vertex AI with tracing and cost alerts per feature
- Tune committed use, autoscaling and storage classes from usage data
Kubernetes
Terraform
Gemini
ClaudeServices
Where it fits in our work
From the blog
CI/CD for Models and PromptsEvaluation gates, model registries, canary releases and rollback: applying CI/CD discipline to prompts and models in LLM applications.
Hosted LLM APIs or Self-Hosted Open Models?GPT, Claude and Gemini APIs against self-hosted open models, compared on cost, privacy, latency and the engineering effort each one needs.
GPU Nodes on Kubernetes Without Wasting MoneyNode pools, MIG, time-slicing, scale to zero and spot instances: practical ways to keep Kubernetes GPU costs under control for AI workloads.
Related articles
CI/CD for Models and PromptsEvaluation gates, model registries, canary releases and rollback: applying CI/CD discipline to prompts and models in LLM applications.Hosted LLM APIs or Self-Hosted Open Models?GPT, Claude and Gemini APIs against self-hosted open models, compared on cost, privacy, latency and the engineering effort each one needs.GPU Nodes on Kubernetes Without Wasting MoneyNode pools, MIG, time-slicing, scale to zero and spot instances: practical ways to keep Kubernetes GPU costs under control for AI workloads.Questions
Google Cloud: frequently asked
Does Google Cloud have a region in Ireland?
Not at the time of writing, but it has several EU regions nearby. We pick the region that meets your latency and data residency needs.
Is GKE a good choice for GPU workloads?
Yes, GKE supports NVIDIA GPU node pools and autoscaling. GPU availability varies by region, so we check capacity before committing.
Running Google Cloud, or deciding whether to? Tell us what you have; the first consultation is free.
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