Do we need Kubernetes for every self-hosted AI deployment?

No. We size the platform to your team and workload—from lean single-node or Coolify-managed setups for smaller footprints to full GitOps Kubernetes clusters when multi-node GPU scaling and strict multi-tenancy are required.

Should we standardize on Terraform or OpenTofu?

Both work well in production; we help you choose based on licence governance, existing provider modules, and CI/CD integration.

Can you train our ops team during the rollout?

Yes. We pair with your infrastructure engineers on upgrades, failure drills, and capacity planning so you are never locked into an external operator.

Open-source self-hosted AI & platform — agency delivery

Unifying model serving, Kubernetes, and Infrastructure as Code

Running open-weight models in production is rarely bottlenecked by model weights—it is bottlenecked by GPU scheduling, secrets management, network segmentation, and repeatable environment provisioning. We combine vLLM/Ollama serving architectures, Kubernetes platform engineering, and Terraform/OpenTofu automation into a single operable stack.

What we deliver

Self-hosted AI model serving

Production inference lanes with vLLM, Ollama, Llama, and Mistral—isolated from batch workloads with clear latency and cost budgets.

Kubernetes platform engineering

Helm, GitOps release pipelines, GPU node pools, network policies, and secrets management your team can run day to day.

Infrastructure as Code (Terraform & OpenTofu)

Reproducible dev, staging, and production environments with state locking, CI plan/apply reviews, and disaster recovery runbooks.

Engineering standards in production

Platform discipline for long-term ownership

Separate inference and batch paths

Prevent heavy background jobs or fine-tuning runs from degrading interactive user latency.

Full-stack observability

GPU utilisation, token throughput, queue depth, and service SLO alerts wired into Prometheus and Grafana.

Clean operational handover

Documented GitOps repositories, IaC modules, and incident playbooks transferred directly to your internal engineers.

Choosing the right architectural scope

Dedicated layer support

When you specifically need LLM serving setup, a Kubernetes platform audit, or a Terraform-to-OpenTofu migration.

End-to-end AI platform rollout

When hardware sizing, IaC provisioning, Kubernetes baselines, and model serving must ship together for a new environment.

Sovereign enterprise stack

When self-hosted AI is combined with PostgreSQL, Keycloak IAM, and strict European data residency requirements.

Related services & guides

FAQ

Do we need Kubernetes for every self-hosted AI deployment?

No. We size the platform to your team and workload—from lean single-node or Coolify-managed setups for smaller footprints to full GitOps Kubernetes clusters when multi-node GPU scaling and strict multi-tenancy are required.
Should we standardize on Terraform or OpenTofu?

Both work well in production; we help you choose based on licence governance, existing provider modules, and CI/CD integration.
Can you train our ops team during the rollout?

Yes. We pair with your infrastructure engineers on upgrades, failure drills, and capacity planning so you are never locked into an external operator.

Discuss your self-hosted AI & platform project

Scope model serving, Kubernetes baselines, and IaC automation with our engineering team.

Self-hosted AI & platform engineering with clear operational boundaries

Inference serving lanes, GitOps-managed Kubernetes baselines, and Infrastructure as Code—designed for predictable cost, security, and compliance.