Compare cloud infrastructure options for distributed applications

Cloud infrastructure for distributed applications encompasses the compute, storage, networking, database, and messaging services that cloud providers offer to build systems spanning multiple servers, availability zones, and regions. In 2026, the cloud market is a three-way race: AWS holds approximately 31% market share with the broadest service catalog (200+ services), Azure holds 24% with the strongest enterprise integration and Microsoft 365 ecosystem, and GCP holds 12% with the best data analytics, Kubernetes, and ML tooling. In system design interviews, you do not need to know every service on every platform. You need to know the equivalent services across providers for the components you use in your design, why you would choose one over another, and how to articulate the trade-off between managed convenience and vendor lock-in. Saying "I would use DynamoDB for this key-value store" is good. Saying "I would use DynamoDB because we need single-digit millisecond reads at any scale with zero operational overhead—the trade-off is AWS lock-in versus Cassandra's portability" is what earns points.

Key Takeaways

• AWS has the broadest service catalog and largest global infrastructure (39 regions, 123 availability zones). It is the safest default in interviews because it is the most widely recognized and has the most complete documentation.
• Azure dominates enterprise environments and hybrid cloud. If your target company uses Microsoft 365, Azure Active Directory, or has on-premises infrastructure, Azure is the natural choice.
• GCP excels at data analytics (BigQuery), Kubernetes (GKE is the reference implementation), and ML/AI (Vertex AI, TPUs). If the system design involves heavy data processing or ML pipelines, GCP services are often best-in-class.
• In interviews, name specific services with reasoning. "I would use SQS" is weaker than "I would use SQS for the task queue because it is fully managed, scales automatically, and integrates natively with Lambda for serverless processing."
• Multi-cloud is reality—89% of enterprises use multiple providers in 2026. Mentioning multi-cloud trade-offs (operational complexity vs vendor flexibility) signals production awareness.

Service-by-Service Comparison

Compute

Interview insight: GKE is the most mature managed Kubernetes offering—Google created Kubernetes and GKE reflects that heritage. EKS is the most widely deployed. AKS integrates best with Azure DevOps and enterprise identity. For serverless containers, Cloud Run's scale-to-zero and per-request billing make it the simplest option for deploying containerized services without managing infrastructure.

Databases

Interview insight: DynamoDB is the default key-value store in interviews—single-digit millisecond reads, automatic sharding, and zero operational overhead. Cloud Spanner is unique: globally distributed SQL with strong consistency, using synchronized atomic clocks (TrueTime). Mention Spanner when a problem requires global SQL consistency—no AWS or Azure equivalent matches its consistency guarantees at global scale.

Messaging and Streaming

Interview insight: For Kafka-based designs, AWS MSK (Managed Streaming for Kafka) eliminates cluster management. GCP Pub/Sub is simpler than Kafka (no partitions to manage) but lacks replay and ordering guarantees that Kafka provides. SQS is the simplest queue for decoupling services—no configuration, scales automatically, pay per message.

Storage

Interview insight: S3 is the industry standard for object storage. All three providers offer identical storage tiers (hot, cool, archive) with comparable pricing. CloudFront has the most edge locations (600+), providing the lowest latency for global content delivery.

Networking

Interview insight: GCP's global HTTP load balancer is unique—a single anycast IP distributes traffic globally across regions without per-region configuration. AWS and Azure require separate load balancers per region. Mention this when designing multi-region architectures on GCP.

How to Choose a Cloud Provider in System Design Interviews

Default to AWS

AWS is the safest choice in interviews because it has the broadest service catalog, the most documentation, and the highest interviewer familiarity. Every component you need—compute, storage, database, messaging, CDN, monitoring—has a mature AWS service. If you are unsure which provider to reference, use AWS service names.

Choose GCP When

The system involves heavy data analytics (BigQuery processes petabytes in seconds). The architecture is Kubernetes-native (GKE is the reference implementation). ML/AI inference or training is a core component (Vertex AI, TPUs). Global SQL consistency is required (Cloud Spanner with TrueTime). The team prioritizes developer experience and simpler pricing (sustained-use discounts apply automatically).

Choose Azure When

The company uses Microsoft 365 or Azure Active Directory. Hybrid cloud with on-premises integration is a requirement (Azure Arc is the strongest hybrid solution). The industry is enterprise-heavy: financial services, healthcare, government—where Azure compliance certifications are most comprehensive. The interviewer or company explicitly operates on Azure.

Discuss Multi-Cloud When

The problem requires geographic optimization (use the provider with the closest region to each user base). Vendor lock-in risk is explicitly raised by the interviewer. The system integrates services from multiple providers (BigQuery for analytics + AWS for compute). Multi-cloud adds operational complexity—acknowledge this trade-off: "Multi-cloud eliminates single-provider dependency but doubles the operational surface area for monitoring, security, and deployment."

Managed Services vs Self-Hosted: The Trade-Off

A recurring interview discussion: should you use a managed service (DynamoDB, RDS, MSK) or self-host the equivalent (Cassandra, PostgreSQL, Kafka on EC2)?

Managed services advantages: Zero operational overhead (patching, scaling, backups handled by the provider). Faster time to market. Built-in monitoring and alerting. SLA-backed availability.

Self-hosted advantages: No vendor lock-in. Full configuration control. Potentially cheaper at massive scale (Netflix-level). Portability across clouds.

The decision framework: At small-to-medium scale (teams under 20 engineers), managed services almost always win because the engineering time saved exceeds the pricing premium. At massive scale (hundreds of engineers, thousands of nodes), self-hosted becomes cost-effective because a dedicated platform team amortizes operational cost across the organization.

Interview application: "I would use DynamoDB over self-managed Cassandra. Our team is 8 engineers, and the operational cost of running Cassandra—monitoring, rebalancing, patching—exceeds the DynamoDB pricing premium. At Uber's scale with a dedicated database team, self-managed Cassandra makes sense. At our scale, managed is cheaper in total cost of ownership."

Cloud Pricing: What to Know for Interviews

You do not need to memorize prices. You need to understand the pricing models and when each saves money.

On-demand: Pay per hour/second with no commitment. Most expensive per unit but zero risk. Use for unpredictable workloads and development environments.

Reserved / Committed use: 1–3 year commitments for 30–72% discounts. Use for predictable, always-on workloads (production databases, core API servers).

Spot / Preemptible: 60–90% discount for interruptible capacity. Use for fault-tolerant batch processing, ML training, and CI/CD jobs.

GCP sustained-use discounts: Applied automatically—no commitment required. GCP reduces prices as monthly usage increases. This makes GCP 5–10% cheaper than AWS/Azure for steady-state workloads without the commitment risk of reserved instances.

Data transfer costs: The hidden cost. All providers charge for data leaving their network (egress). Cross-region and internet egress costs 0.01–0.09/GB. Co-locate services in the same region and use CDNs to minimize transfer costs.

Hybrid and Multi-Cloud Considerations

Hybrid cloud connects on-premises infrastructure with cloud services. Azure Arc is the strongest hybrid solution—it extends Azure management to any infrastructure, including on-premises servers and even resources running on AWS or GCP. AWS Outposts brings AWS services to your data center but is less flexible. Google Distributed Cloud extends GCP to edge and on-premises locations.

When hybrid matters in interviews: If the system design involves regulated industries (banking, healthcare, government) where some data must remain on-premises for compliance reasons, mention hybrid architecture: "Patient health records stay on-premises for HIPAA compliance. The analytics pipeline runs on GCP BigQuery, consuming anonymized data synced from the on-premises database."

AI and ML Infrastructure

AI workloads are increasingly common in system design interviews. Each provider has distinct strengths.

AWS: SageMaker for end-to-end ML lifecycle. Bedrock for managed foundation model access. Trainium/Inferentia chips for cost-effective training and inference.

Azure: Azure OpenAI Service provides exclusive access to GPT models. Azure ML for enterprise ML workflows. Tight integration with Microsoft Copilot across the enterprise stack.

GCP: Vertex AI for unified ML platform. TPUs (Tensor Processing Units) for cost-effective large-model training. BigQuery ML for SQL-based model training directly on your data warehouse.

Interview application: "For the recommendation engine's ML pipeline, I would use GCP's Vertex AI for model training on TPUs—approximately 30% cheaper than GPU-based training on AWS for transformer models—and deploy the serving endpoint behind Cloud Run for auto-scaling inference."

For structured practice incorporating cloud infrastructure decisions into complete system design solutions, Grokking the System Design Interview covers cloud service selection as a core component of every design. For advanced cloud patterns including multi-region deployment, cloud-native cost optimization, and production-scale infrastructure decisions, Grokking the Advanced System Design Interview builds the depth required for L6+ interviews. The system design interview guide provides the end-to-end framework for approaching any system design problem.

Frequently Asked Questions

Which cloud provider should I reference in system design interviews?

AWS by default—it has the broadest recognition and most complete service catalog. Use GCP when the problem involves data analytics, Kubernetes, or ML. Use Azure when the target company operates on Microsoft technologies. Always explain why you chose the provider, not just name services.

Do I need to know all three cloud providers?

No. Know one deeply (AWS is the safest bet) and understand the high-level strengths of the other two. Knowing that Spanner provides global SQL consistency and BigQuery handles petabyte analytics is sufficient—you do not need to know every GCP service.

What is the difference between DynamoDB and Cloud Spanner?

DynamoDB is a key-value/document store optimized for single-digit millisecond reads with automatic sharding. Cloud Spanner is a globally distributed relational database with strong consistency achieved through synchronized atomic clocks. Choose DynamoDB for simple key-value access patterns. Choose Spanner when you need global SQL consistency with relational queries.

Should I use managed services or self-hosted in my design?

Managed services for teams under 20 engineers—the engineering time saved exceeds the pricing premium. Self-hosted at massive scale (hundreds of engineers) where a dedicated platform team amortizes operational cost. In interviews, justify your choice with team size and total cost of ownership.

What is multi-cloud and should I mention it in interviews?

Multi-cloud uses services from multiple providers (89% of enterprises do this in 2026). Mention it when the problem involves geographic optimization, vendor lock-in concerns, or best-of-breed service selection. Always acknowledge the trade-off: reduced lock-in at the cost of increased operational complexity.

How do cloud pricing models differ?

On-demand (most expensive, zero commitment), reserved/committed (30–72% discount, 1–3 year lock-in), and spot/preemptible (60–90% discount, interruptible). GCP uniquely offers sustained-use discounts that apply automatically without commitments. Know these models for cost-optimization discussions.

Which cloud has the best Kubernetes support?

GKE (Google Cloud). Google created Kubernetes and GKE is the reference implementation. EKS (AWS) is most widely deployed. AKS (Azure) integrates best with Azure DevOps and enterprise identity. For interviews, GKE is the most technically advanced; EKS is the most commonly used.

What is vendor lock-in and how do I discuss it?

Vendor lock-in occurs when your architecture depends on provider-specific services that have no portable equivalent. DynamoDB, Lambda, and Step Functions create AWS lock-in. Mitigate by using open-source alternatives (Kafka instead of Kinesis, PostgreSQL instead of Aurora) when portability matters. In interviews, frame it as a trade-off: managed convenience vs portability.

How many AWS services should I know for interviews?

Know 15–20 core services across compute (EC2, Lambda, ECS, EKS), storage (S3, EBS), database (RDS, DynamoDB, ElastiCache), messaging (SQS, SNS, Kinesis), networking (ALB, Route 53, CloudFront, API Gateway), and monitoring (CloudWatch). This covers every component in a typical system design answer.

Does the cloud provider choice affect my interview score?

Not directly—interviewers evaluate your reasoning, not your provider preference. However, using provider-specific service names (DynamoDB, CloudFront, SQS) instead of generic terms (NoSQL database, CDN, message queue) demonstrates practical cloud experience that interviewers notice and reward.

TL;DR

AWS (31% market share, 200+ services, 39 regions) is the default for interviews—broadest catalog, most documentation, highest interviewer familiarity. Azure (24%) dominates enterprise and hybrid cloud with Azure Arc and Microsoft 365 integration. GCP (12%) leads in data analytics (BigQuery), Kubernetes (GKE), and ML (Vertex AI, TPUs). In interviews, name specific services with reasoning: "DynamoDB for single-digit ms key-value reads with zero ops overhead" beats "a NoSQL database." Know equivalent services across providers for every component: compute (EC2/VMs/Compute Engine), databases (DynamoDB/Cosmos DB/Firestore), messaging (SQS/Service Bus/Pub/Sub), and storage (S3/Blob/Cloud Storage). Use managed services for teams under 20 engineers; self-hosted at massive scale. GCP's sustained-use discounts apply automatically; AWS/Azure require reserved instance commitments for comparable savings. Multi-cloud is reality (89% of enterprises) but adds operational complexity—acknowledge the trade-off.