Infrastructure and DevOps
Building reliable, scalable infrastructure with modern DevOps practices, platform engineering, and FinOps principles.
Modern Infrastructure in 2025
Infrastructure in 2025 is defined by platform engineering, GitOps workflows, and cost optimization (FinOps). The shift from DevOps to platform engineering reflects a maturity where infrastructure is product-ized for internal developers. Kubernetes remains dominant but is increasingly abstracted through internal developer platforms (IDPs) and managed services.
Modern infrastructure emphasizes observability-first design, infrastructure-as-code (IaC), and declarative configuration. The rise of AI workloads has introduced new challenges around GPU orchestration, model serving, and high-bandwidth networking requirements.
2025 Infrastructure Trends
- Platform Engineering: IDPs with Backstage, Port, or Humanitec
- FinOps: Cost optimization as core engineering practice
- Edge Computing: Cloudflare Workers, AWS Lambda@Edge, Vercel Edge Functions
- GitOps: ArgoCD, FluxCD for declarative deployments
- Service Mesh: Istio, Linkerd for microservices networking
- Observability: OpenTelemetry as standard, unified logging/metrics/traces
Infrastructure Strategy by Stage
Stage 1: MVP / Seed (0-5 engineers)
Goal: Ship fast, minimize operational overhead, stay under $500/month infrastructure costs.
- Hosting: Vercel, Netlify, or Railway (zero-config deploys)
- Database: Supabase, PlanetScale, or Neon (managed Postgres)
- Backend: Serverless functions (no server management)
- Monitoring: Built-in platform monitoring + Sentry for errors
- CI/CD: GitHub Actions with simple build-and-deploy workflows
- Avoid: Kubernetes, custom VMs, self-hosted databases
Stage 2: Series A (5-25 engineers)
Goal: Establish foundations for scale, introduce infrastructure standards, manage growing costs.
- Hosting: AWS ECS/Fargate or GCP Cloud Run (container-based, less complex than K8s)
- IaC: Terraform or Pulumi for reproducible infrastructure
- CI/CD: GitHub Actions with deployment pipelines, automated testing
- Monitoring: Datadog, New Relic, or Grafana Cloud for unified observability
- Secrets: AWS Secrets Manager, HashiCorp Vault, or Doppler
- Database: Managed RDS/Cloud SQL with read replicas for scaling
Stage 3: Series B+ (25-100+ engineers)
Goal: Scale infrastructure and team, platform engineering, multi-region, compliance.
- Orchestration: Kubernetes (EKS, GKE, AKS) with service mesh (Istio/Linkerd)
- Platform: Internal Developer Platform (Backstage + custom plugins)
- GitOps: ArgoCD or FluxCD for declarative, audit-friendly deployments
- Multi-cloud: Strategic use of multiple providers for resilience
- FinOps: Dedicated cost optimization tools (Kubecost, CloudHealth)
- Compliance: SOC2, ISO27001 controls baked into infrastructure
Essential DevOps Practices
CI/CD Pipeline Best Practices
Pipeline Stages: Lint → Test → Build → Security Scan → Deploy → Smoke Tests
Deployment Strategy: Blue-green or canary deployments for zero-downtime
Rollback Plan: Automated rollback on failed health checks (within 5 minutes)
Environment Parity: Staging mirrors production (same infra, scaled down)
Feature Flags: LaunchDarkly or Flagsmith for controlled rollouts
Security Gates: SAST, DAST, dependency scanning in pipeline (Snyk, Trivy)
Infrastructure as Code (IaC)
Tool Choice: Terraform (multi-cloud), Pulumi (type-safe), CDK (cloud-specific)
State Management: Remote state (S3 + DynamoDB) with locking, never local
Module Strategy: Reusable modules for common patterns (VPC, RDS, K8s cluster)
Drift Detection: Automated checks for infrastructure drift (weekly scans)
Policy as Code: OPA or Sentinel for compliance enforcement
Documentation: Self-documenting code with terraform-docs or README generation
Observability Stack
Metrics: Prometheus (open-source) or Datadog (managed)
Logging: ELK Stack, Loki, or managed (CloudWatch, Datadog Logs)
Tracing: OpenTelemetry → Jaeger, Tempo, or managed (Datadog APM)
Alerting: PagerDuty or Opsgenie with smart escalation policies
Dashboards: Grafana for metrics, custom dashboards per service
SLIs/SLOs: Define and track Service Level Objectives (99.9% uptime)
Container and Kubernetes Strategy
When to Use K8s: 25+ engineers, microservices, multi-region, or complex workloads
Managed vs Self-Hosted: Always start with managed (EKS, GKE, AKS)
Resource Management: Set resource requests/limits, use HPA for autoscaling
Security: Network policies, Pod security standards, RBAC, admission controllers
Service Mesh: Consider Istio/Linkerd for microservices (traffic management, mTLS)
Cost Optimization: Spot instances, node autoscaling, pod bin packing
FinOps: Cost Optimization Practices
Cloud costs are a top concern for CTOs in 2025. FinOps is the practice of bringing financial accountability to cloud spending through collaboration between engineering, finance, and business teams.
Cost Visibility
Tag all resources by team, project, environment. Use cloud cost tools (AWS Cost Explorer, GCP Billing, Kubecost for K8s). Review costs weekly, set up anomaly detection alerts.
Right-Sizing
Continuously monitor resource utilization. Downsize over-provisioned instances. Use autoscaling to match capacity with demand. Consider ARM instances (Graviton, Ampere) for 20-40% cost savings.
Reserved Capacity
Purchase reserved instances or savings plans for predictable workloads (30-70% savings). Use spot instances for batch jobs and stateless workloads. Balance flexibility vs. committed spend.
Data Transfer Costs
Minimize cross-region and egress traffic. Use CDNs for static assets. Compress data. Consider VPC peering over public internet. Data transfer can be 20-30% of total cloud costs.
Platform Engineering
Platform Engineering is the discipline of building Internal Developer Platforms (IDPs) that enable product teams to self-serve infrastructure and deploy applications without deep infrastructure knowledge.
What a Good IDP Provides
- Self-service environment provisioning (dev, staging, prod)
- Standardized deployment workflows (git push → production)
- Built-in observability (logs, metrics, traces automatically configured)
- Security and compliance by default (secrets management, network policies)
- Developer portal (Backstage) for service discovery and documentation
- Golden paths: opinionated templates for common use cases
When to Invest in Platform Engineering
Start at 30-50 engineers: Below this, focus on managed services and simple automation.
Team structure: Dedicate 1-2 engineers per 20-30 product engineers. Platform team treats infrastructure as a product with customers (internal developers).
ROI calculation: If developers spend 20% time on infrastructure, and platform reduces this to 5%, that's 15% productivity gain. With 50 engineers, that's 7.5 FTE worth of saved time.
Key Takeaways
- Start simple with managed services, add complexity only when team size and scale demand it
- Infrastructure as Code is non-negotiable: Terraform, Pulumi, or CDK for reproducibility
- Observability must be built in from day one: metrics, logs, traces with OpenTelemetry
- FinOps is critical: tag resources, monitor costs weekly, right-size continuously
- Platform engineering at scale: invest in IDPs when you hit 30-50 engineers
- Kubernetes when necessary: 25+ engineers, microservices, or complex scaling requirements