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Cloudstride

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Services

We work inside the project, not next to it. Five areas, two of them our core for years, three the direction we are heading.

Core offer

Kubernetes and GitOps platforms for AI teams

Platforms where several teams run models, pipelines and services safely side by side.

Typical situations

  • Several data science teams share clusters and need clear boundaries.
  • Deployments are still manual and should become traceable through Git.
  • Workloads need to move between clouds, or between cloud and your own data center.

Technologies: Kubernetes · Helm · Kustomize · Terraform · ArgoCD · Multi-Tenancy · AWS · GCP · Azure

Core offer

MLOps and data pipelines

From notebook to reliable, monitored operation of training, pipelines and models.

Typical situations

  • Models run, but nobody knows exactly which data they were trained on.
  • Pipelines break silently and results arrive late.
  • Compute should be available on demand without costs getting out of hand.

Technologies: Kubeflow · MLflow · Spark · Databricks · Dagster · Prometheus · Grafana

Growth area

Agentic AI on Kubernetes

Running agents and their tooling on your own infrastructure, traceable and secured.

Typical situations

  • An agent prototype needs to go into production, with access control and logging.
  • Internal systems should become reachable for agents through MCP.

Technologies: Kubernetes · MCP · Go · Python · ArgoCD

Growth area

Local and self-hosted AI

Models and agents on-premises or in EU clouds, when data should not leave the building.

Typical situations

  • Data must not go to US providers, yet AI should still be usable.
  • GPU compute should be shared and allocated on demand.

Technologies: Kubernetes · vLLM · Ollama · GPU scheduling · Hetzner · EU clouds

Advice and own tooling

Protection for agentic AI

Advice and our own tooling so agents only see what they need to see. No compliance audits.

Typical situations

  • Agents work with client data; names should not reach the cloud.
  • API keys are scattered across developer machines.

Technologies: Pseudonymization · Bitwarden Secrets Manager · External Secrets Operator

How an engagement works

  1. A conversation. You tell us what it is about. We say plainly whether we are a good fit, and if not, why.
  2. A short workshop, if needed. If the project is still vague, we use a short session to work out together what should be built and what should not. It helps both sides decide; it is not a product of its own.
  3. Hands-on work. We work inside your team and your repositories, remote or on site, billed by the hour or by the day.

We do not sell training or packaged offerings. Rates are discussed in the first conversation.

For protecting agentic AI we give advice and build tooling. Legal reviews and compliance audits are not part of it.