Kubernetes as the production orchestrator
Kubernetes has become the standard production orchestrator for containerized workloads at scale. For Thoughtwave deployments requiring high availability, horizontal scaling, or integration with a client's existing container platform, Kubernetes is the target deployment model. The complexity and operational burden are real, but for the right workload scale, the reliability and ecosystem benefits justify the investment.
How Thoughtwave delivers on Kubernetes
Our Kubernetes engagements cover:
- Managed Kubernetes deployments — AKS on Azure, EKS on AWS, GKE on GCP depending on the client's primary cloud. Self-managed Kubernetes (kubeadm, kops) is rarely the right choice for enterprise workloads.
- AI model serving at scale — KServe, vLLM on Kubernetes, and Triton Inference Server for production model serving with autoscaling and multi-model routing.
- Helm chart development for client-specific accelerator packaging and deployment versioning.
- GitOps workflows using Argo CD or Flux for declarative deployment management.
- Service mesh (Istio, Linkerd) for clients where mTLS, advanced routing, or observability requirements exceed what plain Kubernetes networking provides.
- Kubernetes-native observability with Prometheus, Grafana, and OpenTelemetry aligned to the client's existing monitoring stack.
For clients running production AI workloads at scale — millions of inference requests per day, multi-region high-availability requirements, or dozens of model variants behind a routing layer — Kubernetes is typically the right operational platform.
Authentication and governance
Kubernetes authentication integrates with the cloud provider's identity model (Entra for AKS, IAM for EKS, GCP IAM for GKE). RBAC aligns to the principle of least privilege per workload and namespace. Network policies, Pod Security Standards, and admission controllers (OPA Gatekeeper, Kyverno) enforce governance at the cluster level.
When Kubernetes earns its complexity budget
For workloads requiring high availability, horizontal scaling across many pods, multi-tenant isolation through namespaces, or integration with an existing Kubernetes platform the client's team already operates, Kubernetes is the right target. For smaller or more stable workloads, Docker Compose or single-server deployments often outperform on total cost of operation. Our recommendations are workload-specific and avoid the common failure mode of over-engineering platform complexity.