Haink builds the delivery backbone: infrastructure as code, GitOps, Kubernetes platforms, observability and golden-path developer experiences — so teams ship faster with higher reliability. For AI workloads we run the GPU scheduling and MLOps runtime too.
Terraform and Pulumi for reproducible, reviewable, version-controlled infrastructure.
ArgoCD/Flux pipelines with progressive delivery and fast, safe rollbacks.
Multi-tenant, autoscaling Kubernetes with sane defaults and cost controls.
Metrics, logs and traces (Prometheus/Grafana/OpenTelemetry) with SLOs and alerting.
Golden paths and internal platforms that make the right way the easy way for your teams.
Schedulers, queues and MLOps runtime for training and inference on the GPUs we supply.
Typical stack:
Production work delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
Every system we ship comes with infrastructure as code, GitOps, observability and security gates from day one — including the GPU scheduling and MLOps runtime for AI workloads.
Metrics, logs, traces and service-level objectives wired in from the start, so teams move quickly without trading away reliability.
Platform engineering builds an internal platform and golden paths so product teams ship without reinventing infrastructure each time. It pays off once you have several teams or services and want consistent, reliable delivery.
Yes — multi-tenant, autoscaling Kubernetes with IaC, GitOps delivery, observability and cost controls, on the cloud or on-premises.
Yes. We implement CI/CD with GitOps, automated testing, security gates and progressive delivery, tailored to your stack.
Yes — GPU scheduling, queues and MLOps runtime for training and inference, sized to the hardware we can also supply.
All three. We deliver on your cloud accounts, on-premises, or hybrid, with the same IaC and GitOps practices.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap — with right-sized GPU hardware if AI workloads are involved.