Hybrid Cloud Architecture: Patterns, Connectivity, and Management
#cloud#hybrid-cloud#architecture#kubernetes
Hybrid cloud connects on-premises infrastructure with public cloud services. It is not a transitional state -- for many organizations, hybrid is the long-term architecture, driven by data gravity, compliance, investment protection, or performance requirements.
Why Hybrid Cloud
| Driver | Description |
|---|---|
| Data gravity | Large datasets are expensive and slow to move; compute goes to the data |
| Compliance | Regulations require certain data to remain on-premises or in-country |
| Investment protection | Recent hardware investments that still have useful life |
| Latency requirements | Some workloads need sub-millisecond access to on-premises systems |
| Cloud bursting | Handle peak demand in the cloud while running baseline on-premises |
| Disaster recovery | Use cloud as a DR target for on-premises workloads |
Architecture Patterns
Burst to Cloud
Run baseline workloads on-premises, scale to cloud during peak demand.
- Requires workload portability (containers or compatible APIs)
- Networking must handle dynamic routing between environments
- Best for: seasonal peaks, batch processing, CI/CD pipelines
Edge + Cloud
Process data at the edge or on-premises, aggregate and analyze in the cloud.
- IoT and manufacturing scenarios
- Reduces data transfer costs and latency
- Cloud handles historical analysis, ML training, dashboards
Disaster Recovery
On-premises primary with cloud-based DR.
| DR Strategy | RTO | RPO | Cost |
|---|---|---|---|
| Backup & restore | Hours | Hours | Low |
| Pilot light | Minutes | Minutes | Medium |
| Warm standby | Minutes | Seconds | Medium-High |
| Active-active | Seconds | Near-zero | High |
Development in Cloud, Production On-Premises
Use cloud for dev/test environments to avoid on-premises capacity constraints.
- Faster environment provisioning
- Lower cost for ephemeral workloads
- Risk: environment drift between cloud dev and on-prem prod
Connectivity Options
| Option | Bandwidth | Latency | Cost | Setup Time |
|---|---|---|---|---|
| Site-to-site VPN | Up to 1.25 Gbps | Variable (internet) | Low | Hours |
| AWS Direct Connect | Up to 100 Gbps | Consistent, low | High | Weeks |
| GCP Cloud Interconnect | Up to 100 Gbps | Consistent, low | High | Weeks |
| Azure ExpressRoute | Up to 100 Gbps | Consistent, low | High | Weeks |
| SD-WAN | Varies | Optimized | Medium | Days |
Connectivity Best Practices
- Redundant connections (two VPN tunnels or two Direct Connect links)
- Separate connections for production and non-production traffic
- Monitor bandwidth utilization and plan for growth
- Encrypt all traffic, even over dedicated connections
Consistent Management
Kubernetes Everywhere
| Platform | Provider | What It Does |
|---|---|---|
| Anthos | Run GKE on-premises, on AWS, on Azure | |
| Azure Arc | Microsoft | Manage on-premises and multi-cloud Kubernetes from Azure |
| EKS Anywhere | AWS | Run EKS on your own infrastructure |
| Rancher | SUSE | Multi-cluster Kubernetes management, any infrastructure |
| OpenShift | Red Hat | Enterprise Kubernetes with consistent experience everywhere |
Infrastructure as Code
Terraform manages both cloud and on-premises resources through providers:
- Cloud resources via AWS, GCP, Azure providers
- On-premises via vSphere, Nutanix, bare-metal providers
- Single workflow for planning, reviewing, and applying changes
Observability
Unified monitoring across environments is critical:
- Datadog, Grafana Cloud, New Relic -- SaaS-based, agents on all environments
- Prometheus + Thanos/Cortex -- self-hosted, federated across clusters
- OpenTelemetry -- vendor-neutral instrumentation standard
Data Gravity and Placement
Data gravity is the principle that applications and services tend to move toward large datasets:
- Evaluate where the majority of data is produced and consumed
- Calculate data transfer costs for different placement options
- Consider data replication strategies (active-passive, active-active)
- Plan for data sovereignty and regulatory constraints per region
Common Pitfalls
| Pitfall | Impact | Mitigation |
|---|---|---|
| Treating hybrid as temporary | Under-investment in connectivity and tooling | Plan for long-term hybrid |
| Inconsistent security policies | Gaps between on-prem and cloud controls | Unified policy framework |
| Manual operations | Configuration drift, slow response | IaC and GitOps everywhere |
| Ignoring data transfer costs | Budget overruns | Model data flows, cache locally |
| Siloed teams | Cloud team vs on-prem team conflicts | Unified platform engineering team |