SystimaNX works across AWS, Azure, and GCP with the same engineering habits: declarative desired state, audited changes, and reconciliation you can observe. The portfolio story is not “one abstraction to hide all clouds”—it is portable discipline with provider-native anchors where they reduce risk.
Portable layer. Repository layout, promotion rules, policy-as-code guardrails, and service identity contracts travel well. Teams should be able to read a PR and understand blast radius without opening three consoles.
Provider-native layer. Load balancers, managed databases, identity integrations, and regional resilience primitives differ. We embed those in thin adapters or clearly named modules so the core application chart stays boring.
GitOps mechanics. Whether you use Flux, Argo CD, or managed equivalents, success metrics are the same: drift visibility, time-to-rollback, and correlation between merge events and cluster state. Controllers should be observable first-class workloads.
Multi-cloud reality. Most organizations are multi-cloud by accident—acquisitions, data residency, or best-of-breed AI APIs—rather than by strategy. We design for that messiness: explicit trust boundaries, least-privilege automation roles, and tagging that makes chargeback honest.
Proof beats roadmap slides. We like to land a narrow vertical—one service type, one environment ladder—then expand once metrics on deploy frequency and incident rate move in the right direction.
Related: DevOps consulting, networking & cloud, and more resources.
A worked example: promoting a service across three clusters
Picture a payments-adjacent service that needs to run in an AWS EKS cluster for the primary region, an Azure AKS cluster for a data-residency requirement in the EU, and a GCP GKE cluster because a recent acquisition already standardized on it. The Helm chart is one artifact, versioned once. What changes per cluster is a small overlay: ingress class, storage class name, and a handful of annotations that wire up the provider's load balancer controller. Everything else—liveness probes, resource requests, PodDisruptionBudgets, NetworkPolicy—stays identical because behavior under load should not depend on which cloud is hosting it.
The promotion pipeline reads the same way in all three environments: a merge to the release branch updates an image tag in a values file, the GitOps controller in each cluster picks up the change independently, and reconciliation converges within its own SLA. We deliberately avoid a central orchestrator that pushes to all three clusters in lockstep—that turns a multi-cloud setup into a single point of failure with three names. If the AWS cluster's controller is degraded, the Azure and GCP rollouts should not care.
Where this breaks in practice is secrets. Each provider has its own preferred secret store—Secrets Manager, Key Vault, Secret Manager—and teams are tempted to write three different integrations. We standardize instead on a single External Secrets Operator deployment per cluster, configured with a provider-specific backend but an identical CRD contract in the application manifests. The application chart never knows which cloud's secret store is behind the ExternalSecret object it references.
Where multi-cloud GitOps commonly goes wrong
The most frequent failure we see is policy drift between clusters that started identical and diverged silently over eighteen months, because each cluster had a different on-call engineer patching it under pressure. OPA Gatekeeper or Kyverno policies need the same GitOps treatment as application code: versioned, reviewed, and diffed across clusters on a schedule, not just applied once at bootstrap and forgotten.
The second failure is treating cost tagging as an afterthought. When three clouds each apply their own default tags plus whatever a team remembered to add manually, finance ends up reconciling spend by hand every month. We enforce tag schemas through admission policy—if a workload manifest lacks the required cost-center and environment labels, it gets rejected at the cluster, not flagged in a spreadsheet later.
The third is assuming GitOps controllers themselves are maintenance-free. Flux and Argo CD both need upgrades, and a controller upgrade in one cluster occasionally changes reconciliation behavior in ways that only show up under specific CRD versions. We stagger controller upgrades across clusters the same way we stagger application rollouts, with the least critical environment going first.
Choosing what to standardize versus what to leave native
Teams new to multi-cloud often ask for a checklist: what should always be identical across clouds, and what is fine to diverge? Our rule of thumb is that anything a developer touches weekly—chart structure, environment variable naming, health check paths, log format—should be identical, because inconsistency there taxes every engineer, every day. Anything an infrastructure specialist touches quarterly—the exact managed database flavor, the specific autoscaling algorithm, the load balancer's health check protocol—can differ, because the people dealing with it already carry provider-specific context.
We also weigh the cost of an abstraction against the cost of divergence. A thin Terraform module that wraps three different managed Postgres offerings behind one interface is worth building if you provision databases often and the differences are mostly cosmetic. It is not worth building if you provision a new database twice a year and the three providers have meaningfully different backup, failover, and read-replica semantics that developers need to reason about directly. Forcing an abstraction over meaningfully different primitives just moves the complexity into the abstraction's edge cases, where it is harder to debug.
In practice, this means our multi-cloud GitOps repos have a consistent top-level structure—apps, clusters, policies—but the contents of the clusters directory look different for AWS, Azure, and GCP once you open them up. That asymmetry is intentional. Portability is a property of interfaces and workflows, not of every file looking the same.
One more thing worth saying plainly: the payoff of this discipline is not felt on day one. The first cluster you stand up this way often takes longer than a quick-and-dirty, cloud-specific setup would. The payoff shows up on the third cluster, when a new engineer can read the same repository structure they already know and be productive within a day instead of a week, and when an audit request like "show me every service with database access in the EU" is a grep across a consistent repo layout instead of a week of screenshots pulled from three different consoles.
We also tell clients up front that this approach has a floor cost: someone has to own the shared chart and the policy bundle, and that ownership does not disappear once the pattern is established. Treat it as a small, permanent platform commitment rather than a one-time migration project, and budget for it the same way you would budget for keeping CI infrastructure healthy. Teams that skip this step tend to watch their "portable" repository slowly become three repositories again, just with more git history, and none of the promised leverage left to show for it.