Containerization and Kubernetes
Move From Fragile Infrastructure to Container-Native Architecture



Client Results and Success

Production-Ready Kubernetes Architecture
The platform was designed to support scalable production deployments with minimal resource consumption, enabling faster environment provisioning and operational stability.
3
environments K8s setup
95%
environments K8s setup
35%
savings over managed Kubernetes alternatives
Our Containerization and Kubernetes Services
- Workload assessment and container migration planning, from legacy services to greenfield architecture.
- Dockerfile optimisation, image hardening, and registry management for secure, lean container builds.
- Service decomposition guidance for teams transitioning toward a container-native architecture.
- Local development environment alignment, so that what runs on a developer's machine behaves the same way in production.

- Cluster architecture for production-grade workloads across AWS EKS, Google GKE, Azure AKS, and self-managed environments.
- Namespace strategy, RBAC configuration, and multi-tenancy design for teams and services sharing cluster resources.
- Helm chart development and management for standardised, version-controlled release packaging.
- Ingress configuration, internal service routing, and traffic management built for reliability at scale.

- Horizontal and vertical autoscaling configured to workload behaviour — not arbitrary thresholds.
- Pod disruption budgets, resource limits, and liveness and readiness probes that keep services stable under pressure.
- Node pool management, cluster autoscaling, and cost optimisation aligned to real usage patterns.
- Documented runbooks, on-call escalation paths, and operational procedures your team can own without us.

- End-to-end observability across your container layer — metrics, logs, and distributed traces surfaced where they matter.
- Alerting is configured to signal meaningful events, not noise, with escalation routing built into the workflow.
- Automatic rollback on failed deployments, with post-incident review tooling to prevent recurrence.
- Prometheus, Grafana, Datadog, and other tooling are integrated into your existing operational stack.

Containerization And Kubernetes Across Every Industry
Infrastructure That Holds Up When Traffic Doesn't Behave
Release Confidence Across Every Environment
Infrastructure Costs Proportional to Real Demand
Engineering Time Back Where It Belongs
Kubernetes and Containerization Across Every Industry
Kubernetes Engineering from a Team That Has Operated It in Production Across 1000+ Projects
Our Offerings in DevOps Consulting and Services
Our Latest Thinking

From Kickoff to Delivery: How GeekyAnts Onboards Engineering Teams and Ensures Continuity
This blog explains how GeekyAnts onboards engineering teams through structured ramp-up, knowledge transfer, replacement planning, and continuity practices.

Dedicated AI Product Pod vs. In-House Hiring vs. Staff Augmentation: A Cost, Control, and Delivery Risk Comparison
Compare dedicated AI product pods, in-house hiring, and AI staff augmentation across costs, delivery ownership, management effort, and risk to choose the right model for your product roadmap.

How GeekyAnts Manages AI Models After Launch to Maintain Security, Control, and Flexibility
AI models don’t stay predictable forever. Discover how GeekyAnts’ documented projects keep model changes in check, catch performance issues, protect sensitive data, and switch providers when requirements change or issues arise.

How GeekyAnts Evaluates AI Systems for Production Readiness
This blog explains how GeekyAnts evaluates AI systems for accuracy, security, bias, cost, and performance before production, supported by real project examples.

How GeekyAnts Selects AI Tools and Model Providers While Managing Data Security and Client Requirements
Explore the AI tools and model providers used in GeekyAnts’ documented projects, including Claude Code, Gemini, OpenAI, and Ollama. Learn how tool selection, data retention, subprocessors, hosting options, and client requirements shape AI adoption.

How GeekyAnts Uses AI in Software Engineering: From Requirements to Release
This blog explains how GeekyAnts uses AI across software development while maintaining human oversight, quality checks, and engineering accountability.





