Cloud & DevOps
Cloud infrastructure, CI/CD pipelines, containerization, and zero-downtime deployments on AWS. Infrastructure as code with Terraform, container orchestration with Kubernetes, and monitoring that catches issues before your users do.
Modern software needs infrastructure that scales automatically, deploys without downtime, and recovers from failures without human intervention. We build this reliability into your stack from day one using infrastructure as code (Terraform), containerized services (Docker + Kubernetes), and CI/CD pipelines that automatically test, build, and deploy every merge to production.
Our DevOps practice covers the full infrastructure lifecycle: environment provisioning across development, staging, and production; secret management with AWS Secrets Manager or HashiCorp Vault; SSL/TLS certificate automation via Let's Encrypt or ACM; centralized log aggregation with CloudWatch or the ELK stack; metrics dashboards for application and infrastructure health; and alerting that pages your team when something goes wrong — not after users complain.
Every deployment uses blue-green or canary strategies so your users never experience downtime during releases. Blue-green maintains two identical environments and switches traffic after health checks pass. Canary routes a small percentage of traffic to the new version first, monitoring error rates and latency before rolling out fully. Both strategies include automatic rollback on failure.
We also optimize cloud costs — right-sizing instances, implementing auto-scaling policies, using spot instances for non-critical workloads, and setting up cost monitoring dashboards so you're never surprised by your AWS bill.
How It Works
Assessment
Audit current infrastructure and identify bottlenecks
IaC Setup
Terraform modules, environment provisioning
CI/CD Pipeline
Automated testing, building, and deployment
Monitoring
Logging, metrics dashboards, and alerting
Optimization
Cost analysis, auto-scaling, and performance tuning
Technology Stack
Frequently Asked Questions
AWS is our primary cloud platform — we have the deepest production experience with services like EC2, ECS/Fargate, Lambda, RDS, S3, CloudFront, SQS, and IAM. Most of our clients run on AWS because of its maturity, breadth of services, and strong enterprise support. That said, we also work with Google Cloud Platform (GCP) and Microsoft Azure when client requirements, existing infrastructure, or compliance needs dictate a different provider. For on-premise or hybrid requirements, we deploy and manage self-hosted Kubernetes clusters on bare metal or private cloud infrastructure, as well as Red Hat OpenShift for enterprises that need its built-in security policies, developer workflows, and enterprise support. For simpler deployments where managed platforms make more sense, we use Vercel for Next.js projects, Railway for quick backend deployments, and Fly.io for globally distributed services. The key advantage of our infrastructure-as-code approach using Terraform is provider portability — the same workflow and practices apply regardless of whether you're running on a public cloud, self-hosted K8s, or OpenShift, and migrating between environments is a configuration change rather than a rewrite.
We use blue-green or canary deployment strategies depending on the application's architecture and risk tolerance. Blue-green deployments maintain two identical production environments (blue = current, green = new). Traffic switches to the green environment only after automated health checks confirm the new version is healthy — if anything fails, traffic instantly routes back to blue with zero user impact. Canary deployments take a more gradual approach: we route 5-10% of traffic to the new version first, monitoring error rates, latency percentiles, and business metrics for a configurable window (typically 10-30 minutes). Only after the canary passes all checks does traffic gradually shift to the new version. Both strategies are fully automated through CI/CD pipelines using GitHub Actions or GitLab CI, with automatic rollback triggered by health check failures, error rate spikes, or latency threshold breaches. We also implement database migration strategies (expand-contract pattern) to ensure schema changes don't break either version during deployment transitions.
Every project gets a comprehensive observability stack covering four pillars: logging, metrics, tracing, and alerting. Centralized logging aggregates application and infrastructure logs into CloudWatch Logs, the ELK stack (Elasticsearch, Logstash, Kibana), or Grafana Loki — with structured JSON logging for easy filtering and search. Application metrics track request latency (P50, P95, P99), error rates, throughput, and custom business metrics through CloudWatch Metrics or Prometheus with Grafana dashboards. Infrastructure monitoring covers CPU utilization, memory usage, disk I/O, network throughput, and container health for every running service. Uptime checks ping critical endpoints every 60 seconds with alerting via PagerDuty, OpsGenie, or Slack when failures are detected. We also set up anomaly detection for traffic spikes, automated scaling policies to handle them, and cost monitoring dashboards with budget alerts so cloud spend never surprises you.