Container orchestration and event-driven compute that scales to zero and beyond.
Kubernetes and serverless are not competing technologies — they are complementary tools for different workloads. We design and operate both: Kubernetes for stateful, long-running services that need fine-grained control, and serverless for event-driven, unpredictable workloads that should never incur idle costs.
Discuss Your ProjectWorkloads scale from zero to thousands of replicas in seconds based on real traffic.
Serverless eliminates idle compute costs for workloads with variable traffic.
Network policies, RBAC, and namespace isolation keep workloads contained.
Workload analysis to determine the right balance of Kubernetes vs serverless.
Cluster setup, networking, RBAC, and service mesh configuration.
GitOps pipelines, Helm releases, and serverless function deployments.
Observability, auto-scaling tuning, and proactive capacity planning.
Cloud & DevOps
Faster releases, fewer incidents, and infrastructure that scales itself.
Cloud & DevOps
Version-controlled, reproducible infrastructure — no more snowflake servers.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our Kubernetes & Serverless service.
Both, used strategically. Kubernetes for persistent services that need fine-grained networking, stateful workloads, and predictable resource allocation. Serverless for event-driven processors, scheduled jobs, and APIs with highly variable traffic patterns. We help you draw the right boundary.
Managed Kubernetes (EKS, AKS, GKE) removes the control-plane burden. We layer on GitOps with ArgoCD, comprehensive observability, and documented runbooks so your team can operate clusters confidently without needing deep Kubernetes internals knowledge.
Cost depends on cluster size and workload patterns. We design cost-optimised clusters using Karpenter for just-in-time node provisioning and spot instances for non-critical workloads — typically 30–50% cheaper than naively managed clusters.
Docker packages applications into containers. Kubernetes orchestrates those containers across a cluster of machines — handling scheduling, scaling, networking, and self-healing. You need both: Docker to build the images, Kubernetes to run them at scale.
We configure rolling updates with readiness probes, PodDisruptionBudgets, and pre-stop hooks. Combined with ArgoCD canary or blue-green strategies, production deployments are seamless even for traffic-sensitive services.
Standard Lambda functions are limited to 15 minutes. For longer workloads we use Step Functions for orchestrated workflows, AWS Batch for compute-heavy jobs, or Fargate for containerised long-running tasks — all without managing servers.
Network policies to restrict pod-to-pod communication, RBAC with least-privilege service accounts, secrets managed via external providers, container image scanning, and admission controllers like OPA Gatekeeper that enforce policies before workloads deploy.
Our team will scope your requirements and come back with a clear proposal within 48 hours.