Design multi-cloud strategies with workload distribution, disaster recovery, and vendor independence
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Design AWS cloud architectures with AI. Visualize EC2, S3, RDS, Lambda, and all AWS services with professional diagrams
Build Azure cloud architectures with AI assistance. Visualize VMs, Storage Accounts, Azure SQL, Functions, and more
Design Google Cloud architectures with AI. Visualize Compute Engine, Cloud Storage, BigQuery, and GCP services
Common questions about multi-cloud architecture diagram generator
Avoid vendor lock-in, leverage best-of-breed services (AWS for compute, GCP for data, Azure for Microsoft integration), meet data residency requirements, improve disaster recovery. Show tradeoffs vs complexity.
Use VPN tunnels, cloud interconnects, or SD-WAN. Show global load balancers routing to nearest cloud, DNS-based traffic management, and secure connectivity between cloud VPCs.
Use Kubernetes for compute abstraction, Terraform for infrastructure as code, cloud-agnostic data formats. But recognize some cloud-native services (like managed databases) may not have perfect equivalents.
Replicate via object storage (S3, Blob, GCS), use multi-cloud databases (Cockroach DB, MongoDB Atlas), or event-driven sync. Visualize data gravity and latency considerations.
Increased management overhead, data transfer costs between clouds, need for cloud-agnostic tooling and expertise. Show when multi-cloud makes sense (DR, compliance) vs added complexity.
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