Cloud Engineering that doesn't burn the budget.
Cloud is a strategy, not a vendor. We design, migrate, and operate cloud platforms on AWS, Azure, GCP, and multi-cloud — with Infrastructure-as-Code, Kubernetes, FinOps cost optimization, and MLOps for AI workloads. Built for enterprise scale and compliance-heavy industries.
Most cloud bills are 30% waste. We start by fixing that.
Cloud spend has exploded for most enterprises. Half of that is architecture decisions made years ago that no one's revisited. We start with a FinOps audit before recommending a single new service.
Terraform · Pulumi · CDK
100% of infrastructure as code. Versioned, reviewable, repeatable. No 'click-ops' configuration drift.
Cost transparency
Per-team / per-product / per-workload cost attribution. Rightsizing, reserved capacity, savings plans. Typically 20-40% reduction in year one.
Managed + GitOps
EKS, AKS, GKE with Argo CD or Flux. Helm charts. Cluster autoscaling. Multi-tenant by design where needed.
AI workloads first-class
SageMaker, Vertex AI, Azure ML, or self-hosted (Kubeflow, Ray). Model versioning, eval pipelines, drift monitoring.
From migration to MLOps. Six core practices.
On-prem → cloud / cloud → cloud
Strangler-pattern migrations. Lift-and-shift, replatform, refactor — we'll recommend the right strategy per workload.
Terraform · Pulumi · Crossplane
Version-controlled, reviewable, repeatable infra. CI / CD on the infrastructure itself.
EKS · AKS · GKE
Managed Kubernetes with GitOps. Helm-packaged services. Horizontal autoscaling. Cluster security baseline.
GitHub Actions · GitLab · CircleCI
Build, test, deploy pipelines. Multi-environment promotion. Security scanning. Automated rollback.
OpenTelemetry · Datadog · Grafana
Metrics, logs, traces. Per-service dashboards. On-call rotations. SLO / SLI engineering.
Cost transparency, savings
Cost allocation, rightsizing, reserved capacity, savings plans. 20-40% typical reduction year one.
Proof. Not pitch decks.
FinTech super-app cloud architecture.
Multi-region AWS deployment with full PCI-DSS posture. Auto-scaling, blue-green deployments, automated failover. 99.99% uptime. Per-tenant cost attribution. Reference architecture for GCC bank-grade FinTech.
FINOPS30% saved in first 90d
Typical FinOps engagement: discover, rightsize, reserve, automate. 20-40% reduction in cloud spend in first quarter.
MLOPSModel risk + ops
MLOps for production AI workloads. Versioning, eval pipelines, drift detection, replay infrastructure.
Questions buyers actually ask.
AWS, Azure, or GCP?
Depends on existing footprint, compliance posture, and target workload. AWS for breadth, Azure for Microsoft ecosystem and enterprise integration, GCP for AI/ML and data analytics depth. We'll recommend based on your specific shape — and we run multi-cloud regularly.
How much can we realistically save on cloud spend?
Typical first-year FinOps engagement: 20-40% reduction. Bigger savings (40-60%) usually require architectural changes — moving from on-demand to spot, replatforming legacy lift-and-shifts, or shifting to managed services.
Can you handle Kubernetes if we don't have K8s experience in-house?
Yes — we'll architect, deploy, and operate. We also train your team to take over within 6-12 months if you want to bring it in-house.
What about data residency for GCC / EU?
Configurable. AWS / Azure / GCP all have GCC regions (Bahrain, KSA, UAE) and EU regions. We design for the residency you need from day one — not bolt on later.
Audit your cloud spend and architecture.
Free 5-day cloud audit. We benchmark your current spend, architecture, and FinOps maturity, and return a prioritized improvement list.