Infrastructure that ships faster and breaks less.

Manual deployments, undocumented infrastructure and no observability are not technical debt — they are operational risk.

Infrastructure that exists only in someone's head is infrastructure that breaks at 2am on a Friday when that person is unavailable. A deployment process that requires a senior engineer to SSH into a production server is a deployment process that will eventually cause an outage. An application with no monitoring is an application where users discover bugs before the engineering team does. PROPELOO builds infrastructure where deployments are automated git pushes, where every resource is defined in version-controlled Terraform, where alerts fire before users notice problems, and where any engineer on the team can understand and modify the infrastructure from documentation alone. That is not a nice-to-have — it is the difference between a team that ships confidently and a team that is afraid to deploy.

The full cloud and DevOps engineering stack.

Infrastructure engineering is not one discipline. It is six — each with distinct tooling, failure modes and best practices.

System Layers

• Infrastructure as Code: Terraform, Pulumi or CDK — all cloud resources version-controlled, reviewable and reproducible
• Container Orchestration: Kubernetes, ECS or App Runner — container scheduling, scaling, health management and service mesh
• CI/CD Pipeline: GitHub Actions, ArgoCD, GitLab CI — automated test, build, security scan and deploy on every push
• Observability Stack: Metrics, logs, traces, alerts — full visibility into system behaviour before users notice problems
• Security & Compliance: IAM least-privilege, secrets management, network segmentation, compliance automation

Core Technical Capabilities

• Infrastructure as Code

  All cloud resources defined in Terraform or Pulumi — VPCs, subnets, security groups, RDS, Elasticache, EKS clusters, IAM roles. No manually created resources. Every change reviewed via pull request before apply.

• Kubernetes Engineering

  EKS/GKE/AKS cluster design, workload manifests, Helm chart development, RBAC configuration, network policies, HPA/VPA autoscaling, PodDisruptionBudgets and cluster upgrade strategy.

• CI/CD Pipeline Design

  GitHub Actions or GitLab CI pipelines: automated test, lint, security scan (Trivy, Snyk), Docker build, image push and GitOps deployment via ArgoCD or Flux. Every commit to main automatically deployed to staging. Production deployments require approval.

• Observability & Monitoring

  Prometheus + Grafana for metrics, OpenTelemetry for distributed tracing, centralised logging (Datadog, CloudWatch, Loki), alerting with PagerDuty integration and SLO/SLA monitoring dashboards.

• Cloud Cost Optimisation

  Right-sizing analysis, Reserved Instance and Savings Plans purchasing strategy, spot instance usage for non-critical workloads, S3 lifecycle policies, RDS parameter tuning and monthly cost attribution by team/service.

• GitOps & Deployment Automation

  ArgoCD or Flux for Kubernetes GitOps — git as the single source of truth for cluster state. Blue/green and canary deployment strategies. Automated rollback on health check failure. Zero-downtime deployment patterns for stateful and stateless services.

How we think about cloud infrastructure.

The best infrastructure is the infrastructure that engineers forget about because it just works. Every manual step in your deployment process is a future incident waiting to happen.

• Everything in code, nothing in console

  Infrastructure created manually in the AWS console cannot be reproduced, reviewed, rolled back or audited. Terraform-managed infrastructure can be destroyed and recreated identically in 20 minutes, reviewed in a pull request before changes are applied, and audited via git history. The console is for exploration. Everything that runs in production must be in code.

  Axiom:

• Observability is not optional for production

  An application without monitoring is not in production — it is in a state where users are your monitoring system. Metrics, structured logs and distributed traces must be in place before the first production user. The cost of setting up observability before you need it is trivial. The cost of debugging a production incident without it is enormous.

  Axiom:

• Kubernetes is not always the answer

  Kubernetes is the right choice for complex microservices systems that need sophisticated scheduling, autoscaling and deployment strategies. It is not the right choice for a three-service application that could run on ECS Fargate with 90% less operational complexity. We recommend the simplest infrastructure that meets the requirements — not the most impressive-looking architecture diagram.

  Axiom:

• Security is built in, not bolted on

  IAM roles with least-privilege access, network segmentation with private subnets, secrets in Secrets Manager (never in environment variables committed to code), VPC flow logs and CloudTrail enabled from day one. Retrofitting security onto an infrastructure that was built without it is expensive and rarely complete.

  Axiom:

The infrastructure decisions that matter.

These choices determine operational complexity, cost and reliability for the lifetime of the system.

• Kubernetes vs managed container services?

  Impact: Kubernetes is justified for complex systems with many services, sophisticated deployment requirements or teams with existing K8s expertise. For most applications under 10 services, ECS Fargate or Cloud Run provides sufficient capability with significantly lower operational overhead.

  • EKS/GKE/AKS — full Kubernetes, maximum flexibility, significant operational overhead
  • ECS Fargate — AWS-managed containers, simpler than K8s, less flexible, lower ops burden
  • App Runner / Cloud Run — fully managed, zero cluster management, limited configuration
  • Lambda (serverless) — zero infrastructure, cold start latency, vendor lock-in

• IaC tool?

  Impact: Terraform is the industry standard with the largest module ecosystem and community. Choose Pulumi if your team strongly prefers general-purpose languages over HCL. CDK only if you are AWS-exclusive and want native TypeScript.

  • Terraform — most widely adopted, large module ecosystem, HCL syntax
  • Pulumi — general-purpose language (TypeScript/Python), steeper learning curve, more flexible
  • AWS CDK — TypeScript/Python, AWS-only, tight AWS integration
  • Helm + Kustomize — Kubernetes-specific, not full IaC

• CI/CD system?

  Impact: GitHub Actions for CI (test/build/scan) + ArgoCD for CD (Kubernetes deployment) is the most common production pattern and the one we recommend for Kubernetes workloads. ECS workloads: GitHub Actions end-to-end.

  • GitHub Actions — tight GitHub integration, large marketplace, per-minute billing
  • GitLab CI — self-hosted option, strong security features, integrated with GitLab SCM
  • ArgoCD (GitOps) — Kubernetes-native, git as source of truth, excellent visibility
  • CircleCI / BuildKite — specialised CI, good performance, additional cost

• Observability stack?

  Impact: Datadog is the fastest path to production observability with minimal setup. At scale (>50 hosts), the cost justifies evaluating Prometheus + Grafana + Loki. Start with Datadog, optimise cost later when you have real usage data.

  • Datadog — fully managed, excellent UX, expensive at scale ($10-30/host/month)
  • Prometheus + Grafana — open source, self-hosted, setup complexity, no per-host cost
  • AWS CloudWatch — native AWS integration, sufficient for simple workloads, poor UX
  • OpenTelemetry + backend of choice — vendor-neutral instrumentation, flexibility

• Multi-cloud vs single cloud?

  Impact: Multi-cloud active-active is justified only for systems where a single cloud region outage is unacceptable and the engineering team is large enough to maintain two complete infrastructure stacks. For most applications, single cloud with multi-region is the correct resilience strategy.

  • Single cloud (AWS) — simplest, deepest service integration, vendor lock-in risk
  • Single cloud (GCP) — strongest data/ML services, Kubernetes-native (GKE)
  • Multi-cloud active-active — highest resilience, 2-3x operational complexity
  • Multi-cloud active-passive (DR) — resilience without double complexity, slower failover

• Deployment strategy?

  Impact: Blue/green is the simplest zero-downtime strategy with fast rollback. Canary is appropriate for high-traffic services where even a 5-minute bad deploy is unacceptable. Feature flags solve the problem at the product layer and are the most flexible option.

  • Rolling update — gradual replacement, zero downtime, rollback takes time
  • Blue/green — instant cutover and rollback, doubles infrastructure cost temporarily
  • Canary — gradual traffic shift, catches issues with small blast radius, complex routing
  • Feature flags — decouple deploy from release, application-level control

What PROPELOO builds.

• Infrastructure from Scratch

  Complete AWS/GCP infrastructure setup in Terraform — VPC design, EKS/ECS cluster, RDS, Elasticache, CI/CD pipeline, observability stack and security baseline for a new product.

• Infrastructure Audit & Remediation

  Review of existing cloud infrastructure for security gaps, cost inefficiency, reliability risks and missing observability. Prioritised remediation roadmap with Terraform migration for manually-created resources.

• Kubernetes Migration

  Migration from ECS, Heroku or bare EC2 to EKS — including workload containerisation, Helm chart development, CI/CD pipeline update and monitoring setup.

• CI/CD Pipeline Build

  End-to-end CI/CD from zero — GitHub Actions test/build/scan pipeline, Docker registry, ArgoCD GitOps deployment, environment promotion and production approval gates.

• Observability Implementation

  Full observability stack setup — Prometheus + Grafana or Datadog, distributed tracing with OpenTelemetry, structured logging, SLO dashboards and PagerDuty alerting.

• Cost Optimisation

  Cloud cost analysis, right-sizing recommendations, Reserved Instance purchasing strategy, spot instance configuration and monthly cost attribution dashboards — typically 20-40% cost reduction.

The infrastructure stack.

Each layer has specific tooling requirements. The combination determines reliability, observability and operational burden.

• Cloud Platforms

  Stack: AWS (primary), GCP, Azure, Cloudflare (edge/CDN/DNS)

• IaC & Config

  Stack: Terraform, Pulumi, Helm, Kustomize, AWS CDK

• Containers & Orchestration

  Stack: Docker, Kubernetes (EKS/GKE), ECS Fargate, ArgoCD, Flux

• CI/CD

  Stack: GitHub Actions, GitLab CI, Tekton, AWS CodePipeline

• Observability

  Stack: Datadog, Prometheus, Grafana, OpenTelemetry, Loki, Jaeger, PagerDuty

• Security

  Stack: AWS IAM, HashiCorp Vault, AWS Secrets Manager, Trivy, Falco, OPA/Gatekeeper

Infrastructure security is the foundation of application security.

Every layer of the infrastructure stack has security requirements that must be designed in, not added later.

• IAM Least Privilege

  Every service, Lambda function and CI/CD pipeline role has only the permissions it needs to function — not AdministratorAccess because it was easier to configure. IAM policy review is part of every infrastructure change. Service accounts with broad permissions are a critical finding in every cloud security audit.

• Network Segmentation

  Private subnets for databases and internal services — no direct internet access. Public subnets only for load balancers. VPC endpoints for AWS service communication without internet traversal. Security groups with minimum required port access. Network ACLs as a second layer of defence. No resources with 0.0.0.0/0 ingress on sensitive ports.

• Secrets Management

  No credentials, API keys or database passwords in environment variables, Dockerfiles, or source code. All secrets in AWS Secrets Manager or HashiCorp Vault, rotated automatically, accessed via IAM role at runtime. Secret scanning in CI pipeline to catch accidental commits.

• Container Security

  Base images from verified sources, minimal base images (distroless where possible), no containers running as root, Trivy vulnerability scanning in CI pipeline, OPA/Gatekeeper policies enforcing container security standards at the Kubernetes admission layer.

• Audit & Compliance

  CloudTrail enabled for all API calls, VPC flow logs for network traffic, S3 access logging, GuardDuty for threat detection, AWS Config for compliance rule evaluation, and centralised log aggregation with retention policies for audit requirements.

• Incident Response

  Runbooks for every alert — not just "alert fires, engineer wakes up and improvises." Documented runbooks covering: how to identify the root cause, immediate mitigation steps, escalation path and post-incident review process. Runbooks in the same repository as the infrastructure code, updated when alerts change.

From assessment to production infrastructure.

1. 01. Infrastructure Assessment

   Review current infrastructure state, identify security gaps, reliability risks, missing observability and cost inefficiencies. Prioritised remediation roadmap.

2. 02. Architecture Design

   Target architecture design — VPC layout, cluster design, service mesh, observability stack selection and security baseline. Documented in Terraform module structure.

3. 03. IaC Foundation

   Core Terraform modules: VPC, subnets, security groups, IAM roles, secrets management, ECR/container registry. All code in version control, reviewed via pull request.

4. 04. Cluster & Services

   EKS/ECS cluster deployment, workload migration, Helm chart development, service discovery, load balancer configuration and autoscaling.

5. 05. CI/CD Pipeline

   GitHub Actions CI pipeline: test, build, scan, push. ArgoCD CD pipeline: GitOps deployment to staging and production with approval gates.

6. 06. Observability

   Metrics, logging, tracing and alerting stack. SLO dashboards. PagerDuty integration. Runbook documentation for every alert.

7. 07. Handoff & Knowledge Transfer

   Architecture documentation, runbook library, on-call training and optional ongoing infrastructure management retainer.

Cloud & DevOps Engineering Engagements

Production infrastructure, Kubernetes platforms and automated CI/CD pipelines delivering high availability.

• Multi-Region Kubernetes Platform & GitOps Pipeline

  Challenge: High-growth SaaS company suffered frequent deployment outages and slow release cycles across US and EU regions.

  Architecture: Infrastructure as Code with modular Terraform, EKS multi-cluster deployment with ArgoCD GitOps, automated progressive delivery via canary releases (Argo Rollouts), and unified Prometheus/Grafana observability.

  Outcome: Deployment frequency increased from bi-weekly to 15+ per day. Production rollback time dropped from 45 minutes to 30 seconds. Zero unplanned deployment downtime over 12 months.

• FinTech Cloud Infrastructure Hardening & SOC 2 Compliance

  Challenge: Regulated payments startup needed SOC 2 Type II compliant cloud architecture before enterprise onboarding.

  Architecture: Immutable infrastructure deployment, automated secrets rotation via HashiCorp Vault, VPC peering with private endpoints, AWS KMS envelope encryption, and automated compliance auditing via AWS Security Hub and Datadog.

  Outcome: Achieved SOC 2 Type II certification with zero exceptions. 100% of infrastructure managed via version-controlled Terraform.

• Cloud Cost Optimization & Serverless Migration

  Challenge: Enterprise platform cloud bill exceeded $85,000/month with 60% idle compute in legacy EC2 clusters.

  Architecture: Re-architected batch processing workloads to event-driven AWS Lambda and Step Functions, implemented Graviton processor instances for remaining containers, and automated EC2 spot and reserved instance lifecycles.

  Outcome: Reduced monthly AWS spend by 58% ($49,000/mo savings) while improving peak processing latency by 35%.

Frequently Asked Questions