When teams outgrow a single cloud, the instinct is often to “lift and duplicate” existing stacks. That approach typically results in silos, inconsistent controls, and runaway spend. A successful multi-cloud architecture needs shared guardrails, consistent automation, and a rational workload placement strategy.
1. Start with business drivers
Every multi-cloud initiative should answer why. Common drivers include regulatory requirements, data residency, vendor negotiation leverage, or best-of-breed service selection. Documenting these drivers guides the rest of the design, especially when trade-offs appear later.
2. Standardize the landing zones
Create a landing zone blueprint that can be stamped out per cloud provider:
- Identity & Access: Centralize identity through Azure AD/Okta + SCIM, federate into each cloud, and enforce least privilege with policy-as-code.
- Networking: Use hub-and-spoke in each provider, connect hubs via encrypted transit (VPN or Direct Connect + ExpressRoute) with unified CIDR allocation.
- Security Controls: Baseline GuardDuty/Security Center/SCC findings into a single SIEM and normalize severity levels.
- Observability: Ship logs/metrics/traces to a provider-agnostic platform (Elastic, Datadog, Grafana LGTM) for consistent SLO tracking.
3. Codify everything
Terraform with provider-specific modules is still the most portable approach. I maintain a module catalog with identical inputs/outputs per cloud so platform teams can swap providers without rewriting pipelines. Combine it with Atlantis or Spacelift to introduce drift detection and policy gates.
module "landing_zone_aws" {
source = "git::ssh://git.example.com/platform/landing-zone.git//aws"
org_id = var.org_id
audit_account_id = data.aws_organizations_organization.org.master_account_id
networking = {
primary_region = "us-east-1"
transit_gateway_cidr = "10.0.0.0/16"
}
guardrails = {
enable_config_recorder = true
enable_cloudtrail = true
allowed_regions = ["us-east-1", "eu-west-1"]
}
}
4. Placement strategy
Not every workload belongs everywhere. I categorize workloads into:
- Anchor workloads that stay on a primary cloud but integrate with services elsewhere.
- Portable workloads (containers, serverless) that can run on any provider thanks to Kubernetes, Nomad, or Crossplane.
- Edge/latency-sensitive workloads where deployment is decided per region/provider closest to customers.
A placement matrix—based on compliance, latency, data gravity, and unit economics—removes emotion from these decisions.
5. Governance and FinOps
Multi-cloud without FinOps is chaos. Establish a unified tagging taxonomy, export cost data nightly, and feed it into a single reporting tool. I automate right-sizing recommendations and savings-plan purchases with event-driven Lambdas/Azure Functions.
Final thoughts
Multi-cloud is not about sprinkling workloads everywhere. It’s about building a platform with consistent controls, automation, and visibility—so teams focus on delivering value rather than wrestling with provider quirks. With the right reference architecture, expanding into a new region or provider becomes a non-event rather than a multi-quarter project.