Cloud-Native Infrastructure
Infrastructure that's versioned, tested, and deployed like application code — because your platform is only as reliable as what's underneath it.
When You Need This
Your infrastructure is managed by clicking through cloud consoles. Environment drift between staging and production causes "works on my machine" issues at the infrastructure level. Scaling requires manual intervention, deployments involve SSH-ing into servers, and disaster recovery is a Google Doc that nobody has tested. You need infrastructure that's reproducible, version-controlled, self-healing, and observable — infrastructure that a team can operate without hero knowledge.
Pattern Overview
Cloud-native infrastructure treats infrastructure as code (IaC), runs workloads in containers orchestrated by Kubernetes (or managed equivalents), deploys through GitOps pipelines, and uses managed services where the operational trade-off is favorable. The pattern covers multi-region deployment for availability, horizontal pod autoscaling for elasticity, service mesh for inter-service communication, and comprehensive observability. The goal isn't "running on cloud" — it's building infrastructure that's automated, reproducible, and resilient by default.
Reference Architecture
The architecture spans three planes. The control plane manages infrastructure provisioning through Terraform/Pulumi, runs GitOps controllers (ArgoCD/Flux), and handles secrets management (Vault/AWS Secrets Manager). The workload plane runs application containers in Kubernetes clusters (EKS, GKE, or AKS) with pod autoscaling, service mesh (Istio/Linkerd), and ingress management. The observability plane collects metrics (Prometheus), logs (Loki/CloudWatch), traces (Jaeger/Datadog), and alerts (PagerDuty/OpsGenie).
- IaC Foundation: Terraform or Pulumi modules that define every resource — VPCs, subnets, security groups, IAM roles, databases, caches, queues. Modularized by concern (networking, compute, data, observability) with environment-specific variable files
- Kubernetes Cluster: Multi-AZ deployment with node pools sized for workload types (general, compute-optimized, GPU). Namespace-per-environment or namespace-per-team isolation. Pod disruption budgets, resource quotas, and network policies
- GitOps Pipeline: ArgoCD or Flux watches a Git repository for manifests. Application deployments are pull requests — reviewed, approved, and automatically synced. Rollback is a
git revert - Observability Stack: Prometheus + Grafana for metrics, Loki or ELK for logs, Jaeger or Datadog for distributed tracing. SLO-based alerting that pages on customer impact, not resource utilization
Design Decisions & Trade-offs
System Architecture Overview
Technology Choices
| Layer | Technologies |
|---|---|
| Compute | Kubernetes (EKS, GKE, AKS), ECS Fargate, Cloud Run |
| IaC | Terraform, Pulumi, AWS CDK |
| GitOps | ArgoCD, Flux, GitHub Actions |
| Networking | Istio, Linkerd, AWS App Mesh, Nginx Ingress, Cert-Manager |
| Observability | Prometheus, Grafana, Datadog, Loki, Jaeger, PagerDuty |
When to Use / When to Avoid
| Use When | Avoid When |
|---|---|
| Running 5+ services that need independent scaling and deployment | You have a single application that can run on a PaaS (Vercel, Railway, Render) |
| Multiple teams contribute to shared infrastructure | Your team is < 3 engineers — Kubernetes operational burden will dominate |
| You need multi-region deployment for availability or compliance | The project is an MVP that doesn't need HA or complex orchestration |
| Compliance requires reproducible, auditable infrastructure | Cost optimization is critical and the workload fits serverless economics |
Our Approach
MW delivers infrastructure as a product, not a one-time setup. We provide Terraform modules with CI/CD pipelines that plan, review, and apply infrastructure changes through pull requests — the same workflow your developers use for application code. Our Kubernetes deployments include production-grade defaults: pod disruption budgets, resource limits, network policies, and automated certificate rotation. We hand off with operational runbooks, Grafana dashboards, and on-call escalation policies so your team can operate the infrastructure independently.
Related Blueprints
- Cloud Migration & Cost Optimization — Migrating from on-prem or legacy cloud to cloud-native
- Multi-Region High-Availability Architecture — Active-active and active-passive multi-region patterns
- CI/CD Pipeline Modernization — GitOps pipeline design and implementation
- Hybrid Cloud for Regulated Industries — Cloud-native patterns with on-prem compliance constraints
- GPU Cluster Orchestration for AI Workloads — Kubernetes with GPU node pools for ML training
Related Case Studies
- GPU Infrastructure — RunPod and custom GPU cluster orchestration for AI workloads
- Video Encoding Platform — Containerized encoding pipelines with autoscaling
Related Architecture Patterns
Explore more design patterns and system architectures
Security-First Architecture
Security isn't a feature you add after launch. It's an architectural property — either the system was designed for it, or it wasn't.
Serverless-First Architecture
Pay for what you use, scale to zero when you don't, and stop managing servers entirely — but know when the economics stop working.
On-Off Scaling Architecture
Don't pay for idle GPUs. Provision compute just-in-time, process the workload, and tear it down — turning capital expense into a per-job operating cost.
Need Help Implementing This Architecture?
Our architects can help design and build systems using this pattern for your specific requirements.
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