Cloud & DevOps

Depends on your stack, your team's experience, your customers' compliance requirements, and any existing enterprise agreements. AWS has the broadest service catalog and deepest documentation. Azure wins for Microsoft-heavy organizations and enterprise compliance. GCP leads on data, analytics, and Kubernetes-native workloads. We've shipped on all three. We help you decide based on real constraints — not on which cloud we happen to prefer.

Engagement cost ranges from $5K/month (part-time retainer for platform maintenance) to $30K+/month (full platform team for high-scale operations). Most clients save 20–40% on their cloud bill within the first 90 days of FinOps work — right-sizing, reserved capacity, killing unused resources, restructuring data egress. The retainer often pays for itself in cloud spend reduction alone, before counting any reliability or velocity gains.

Almost always yes. Most cloud bills have 25–50% waste — over-provisioned instances, unused storage, abandoned environments, missed reserved capacity, inefficient data egress, and old architectural decisions nobody's revisited. We start with a cost audit (usually 1–2 weeks) and deliver a prioritized list of changes with estimated savings. Implementation follows the list.

Yes. We engineer the technical controls — IAM, encryption at rest and in transit, audit logging, network segmentation, secrets management, vulnerability scanning, access reviews — that compliance frameworks require. We work alongside your compliance team or auditor; we don't run the audit, but we build the infrastructure to pass it. We've helped clients reach SOC 2 Type II, HIPAA, and PCI DSS posture.

Probably less often than the industry implies. Kubernetes earns its complexity when you have multi-service architectures, polyglot stacks, complex scaling requirements, or multi-environment orchestration needs. For most small-to-mid teams running a handful of services, ECS, Cloud Run, App Engine, or even serverless is simpler, cheaper, and faster to operate. We help you choose based on actual operational maturity, not aspirational architecture.

Yes. Common patterns: on-prem to AWS/Azure/GCP, AWS to Azure (or vice versa) for enterprise consolidation, monolith to microservices, VMs to containers, legacy CI to GitHub Actions/ArgoCD. Migrations are always preceded by an audit — current architecture, cost baseline, dependency map, downtime tolerance, data residency requirements — so the cutover is planned, not improvised.

Observability stack: Datadog, New Relic, Grafana + Prometheus, or whatever your team can actually operate. We instrument metrics, logs, traces (OpenTelemetry where it fits), and surface them in dashboards that drive action. For incident response, we set up PagerDuty/Opsgenie alerting tied to SLOs, on-call runbooks, and post-incident review templates. Optionally, IDT engineers can join your on-call rotation under retainer.

Serverless (Lambda, Cloud Functions, Cloud Run) wins for spiky workloads, event-driven architectures, and small teams who don't want to operate infrastructure. Containers (Kubernetes, ECS, AKS) win for long-running services, complex networking, GPU workloads, or teams that need consistent dev-to-prod environments. Most production systems use both — serverless for the edges, containers for the core. We architect each workload to its actual access pattern.