Choosing between AWS, Microsoft Azure, and Google Cloud Platform (GCP) isn’t about picking “the best cloud.” It’s about selecting the platform that best fits your architecture, team skills, security posture, latency needs, data strategy, and operating model.

This guide breaks down the most important technical criteria for cloud selection-with practical examples and clear comparisons-so engineering and platform teams can make a decision that holds up in production.

Quick summary: AWS vs. Azure vs. GCP at a glance

If you want the short, high-signal view:

• AWS: Broadest service catalog and ecosystem, mature tooling, strong defaults for scale and reliability. Often the “safe” choice for diverse workloads.
• Azure: Deep integration with Microsoft products (Entra ID/Azure AD, Windows Server, SQL Server, .NET, Microsoft 365), strong enterprise governance, excellent for hybrid.
• GCP: Standout strengths in data analytics, Kubernetes-first culture, and developer-friendly primitives. Great fit for cloud-native platforms and data-heavy products.

1) Core compute: VMs, autoscaling, and workload fit

Compute is usually where cloud evaluations begin-because nearly everything depends on it.

Virtual machines and instance families

All three clouds provide a wide range of VM families optimized for:

• General purpose
• Compute optimized
• Memory optimized
• Storage optimized
• GPU/accelerator workloads

Technical selection criteria:

• Instance variety and availability in the regions you need
• Autoscaling maturity (scale-out speed, policies, metrics)
• Spot/Preemptible pricing mechanics and interruption handling
• GPU availability and quotas (common bottleneck for AI workloads)

Where each often shines:

• AWS: Massive breadth of instance types and strong autoscaling patterns across many services.
• Azure: Strong enterprise VM offerings, especially in Microsoft-centric environments.
• GCP: Often praised for clean VM experience and strong performance options; popular for scalable stateless services paired with GKE.

2) Managed Kubernetes and containers: EKS vs AKS vs GKE

If you’re building modern platforms, Kubernetes is often the center of gravity.

Kubernetes services

• AWS: Elastic Kubernetes Service (EKS)
• Azure: Azure Kubernetes Service (AKS)
• GCP: Google Kubernetes Engine (GKE)

Key technical criteria:

• Cluster upgrade experience and version support windows
• Node pool flexibility and autoscaling behavior
• Networking model (CNI, IP planning, ingress patterns)
• Add-on ecosystem (service mesh, policy, observability)
• Identity and access integration (IAM/Entra ID)

Typical differences in the field:

• GKE is frequently considered the most “Kubernetes-native,” reflecting Google’s long history with Kubernetes.
• EKS integrates deeply with AWS IAM and the AWS ecosystem; great when your platform uses many AWS-native services.
• AKS is particularly compelling when identity, governance, and tooling are already standardized on Microsoft.

3) Serverless: Lambda vs Azure Functions vs Cloud Functions/Run

Serverless can reduce operational overhead and accelerate delivery-but the developer experience and integration patterns vary.

Serverless options:

• AWS: Lambda, API Gateway, Step Functions, EventBridge
• Azure: Functions, Logic Apps, Event Grid
• GCP: Cloud Functions, Cloud Run, Eventarc/Workflows

Selection criteria:

• Cold-start characteristics and runtime support
• Eventing ecosystem and integration breadth
• Observability and local development workflow
• Vendor lock-in risk (how portable your functions are)

Rule of thumb:

• If you want container-based serverless with strong portability, Cloud Run is a frequent favorite.
• If you want a rich eventing and orchestration toolbox, AWS is often hard to beat.
• If you’re deeply invested in Microsoft stack + enterprise workflows, Azure Functions + Logic Apps can be very effective.

4) Storage: Object, block, file-and lifecycle controls

Storage decisions influence performance, cost, durability, and governance.

Object storage

• AWS: S3
• Azure: Blob Storage
• GCP: Cloud Storage

All three are highly durable and support lifecycle policies, encryption, and tiering.

Technical criteria:

• Lifecycle and retention controls (WORM, legal hold)
• Event triggers (object-created events to pipelines)
• Cross-region replication and consistency expectations
• Data egress patterns (often a big cost driver)

Block and file storage

Block storage and managed file shares matter for stateful apps, databases, and legacy workloads.

Technical criteria:

• IOPS/throughput scaling model
• Snapshot/backup mechanics
• Multi-zone resiliency options
• File protocol support (SMB/NFS) and IAM integration

5) Databases and data services: relational, NoSQL, and analytics

Cloud selection gets serious when data enters the picture.

Managed relational databases

You’ll find managed offerings for MySQL, PostgreSQL, SQL Server, and more.

Selection criteria:

• High availability architecture (multi-zone, failover behavior)
• Read replicas and scaling limits
• Backup point-in-time restore (PITR) and retention
• Maintenance windows and upgrade control

NoSQL and real-time data

Each provider offers strong options for key-value, document, and wide-column patterns.

Selection criteria:

• Latency requirements and global replication
• Consistency model (strong vs eventual)
• Change streams / event integration

Analytics and warehouse ecosystems

This is where GCP often stands out, especially for teams building modern analytics stacks and ML pipelines—particularly when adopting platforms like Google BigQuery for scalable analytics without managing infrastructure.

Selection criteria:

• Separation of storage and compute (cost control)
• Streaming ingestion options
• Governance, lineage, and catalog tooling
• Integration with ML tooling and notebooks

6) Networking: VPC/VNet design, private connectivity, and traffic controls

Networking choices are long-lived. A cloud that fits your desired network topology can save months of refactoring.

Key technical criteria:

• Virtual network primitives and routing flexibility
• NAT, load balancing options (L4/L7), and TLS termination
• Private connectivity to on-prem or colocation (VPN, dedicated links)
• Firewalling, WAF, DDoS protections
• Multi-region architectures and latency planning

Common scenario fits:

• If you’re building a hybrid enterprise network tied to Windows + Microsoft security tooling, Azure is often a natural fit.
• If your architecture depends on a broad set of mature networking patterns and third-party integrations, AWS is typically strong.
• If you want streamlined cloud-native networking paired with Kubernetes-first designs, GCP is frequently chosen.

7) Identity, security, and governance: IAM, policies, and compliance

Security isn’t a single feature-it’s a system of controls.

What to compare:

• Identity and access management model (roles, policies, conditions)
• MFA and SSO integrations
• Secrets management and key management services
• Policy-as-code and guardrails
• Audit logging completeness and retention
• Compliance offerings relevant to your industry (SOC 2, HIPAA, PCI, ISO, etc.)

Practical decision point

• If your organization is standardized on Microsoft identity (Entra ID, conditional access), Azure can simplify governance and lifecycle management.
• If you need broad IAM flexibility and mature ecosystem patterns, AWS is often favored.
• If you prioritize clean security posture management with cloud-native developer workflows, GCP can be very effective-especially in platform teams with strong automation culture.

8) Observability: logging, metrics, tracing, and incident response

“Can we see what’s happening?” is a make-or-break platform question.

Technical criteria:

• Unified correlation across logs/metrics/traces (see metrics, logs, and traces as a unified view of modern observability)
• Cardinality limits and pricing behavior
• Alerting sophistication and routing
• Integration with OpenTelemetry and third-party tools
• Cross-account/project/subscription visibility

Real-world angle:

A platform that’s slightly more expensive but drastically better for debugging can reduce downtime and engineering hours-often outweighing pure infrastructure savings.

9) CI/CD and Infrastructure as Code (IaC)

Most teams rely on external CI/CD (GitHub Actions, GitLab CI, CircleCI), but cloud-native options can speed up platform bootstrapping.

Compare:

• Native build/release services and container registries
• Terraform support maturity (providers are strong across all three)
• Policy and compliance automation (OPA, policy-as-code integrations)
• Golden path templates for internal developer platforms

Decision tip:

Choose the cloud that best aligns with your operating model (central platform team vs decentralized product teams) and your ability to enforce guardrails through automation.

10) Reliability and global architecture: regions, multi-zone, DR

Your target availability and recovery objectives (RTO/RPO) should shape cloud selection as much as features.

Technical criteria:

• Regional footprint where your customers are
• Multi-zone architecture primitives
• Cross-region replication options for databases and storage
• Disaster recovery patterns and automation
• Limits/quotas management (a frequent hidden constraint)

Example:

A SaaS serving customers across North America and Europe may prioritize multi-region failover and data residency-which changes how you evaluate managed database options and replication features.

11) Cost engineering: pricing is architecture-dependent

Cloud pricing is complex-and easy to misread during evaluation.

What to evaluate technically (not just financially):

• Compute discount models (reserved capacity vs committed use)
• Spot/preemptible strategy feasibility for your workloads
• Data egress realities (inter-region, internet egress, CDN)
• Managed service premiums vs operational savings
• FinOps tooling (cost allocation tags/labels, budgets, anomaly detection)

Practical cost insight

A cloud that is “cheaper per VM” can become more expensive if:

• your architecture moves lots of data cross-region,
• your observability costs explode,
• or your team spends extra time operating services manually.

Cost should be measured as total cost of ownership (TCO): infrastructure + productivity + reliability.

12) Migration and interoperability: avoiding dead ends

Even if you choose one cloud today, future flexibility matters.

Key criteria:

• Kubernetes portability and container standards
• Database portability (Postgres vs proprietary databases)
• Eventing and messaging portability (for example, Apache Kafka for modern data pipelines and real-time streaming systems)
• Identity federation and multi-cloud patterns
• Vendor-managed APIs vs open standards

Balanced approach:

It’s rarely worth forcing extreme portability everywhere. Focus on portability in strategic layers (compute/container, core data store, observability) and accept cloud-native advantages where they clearly deliver ROI.

Common technical questions (featured snippet–friendly)

Which cloud is best for startups?

The best cloud for startups is the one that lets teams ship quickly with minimal operational overhead. Practically:

• Choose GCP if analytics, Kubernetes, and developer ergonomics are central.
• Choose AWS if you need the widest service catalog and long-term flexibility.
• Choose Azure if you’re already committed to Microsoft tools and enterprise buyers.

Which cloud is best for enterprise and hybrid environments?

Azure is often the best fit for enterprise hybrid environments due to tight integration with Microsoft identity, security, and Windows/.NET ecosystems. AWS and GCP can also support hybrid, but Azure commonly reduces friction in Microsoft-standard organizations.

Is AWS always more expensive than GCP or Azure?

No. Cloud cost depends on architecture, discounts, and data transfer patterns. AWS can be cost-effective with reserved capacity and the right managed services, while GCP and Azure can be cost-competitive depending on committed use, workload shape, and egress.

Which cloud is best for AI and machine learning?

All three clouds offer strong AI/ML platforms. The best choice depends on whether your priority is:

• GPU availability and managed ML tooling,
• data pipeline integration,
• or MLOps and model deployment workflows (serverless vs Kubernetes vs managed endpoints).

A practical way to decide: map criteria to your workload

A reliable cloud selection process ties technical criteria to real workload needs:

1. List workload types (APIs, batch, ML inference, analytics, internal tools).
2. Define non-negotiables (regions, compliance, identity, RTO/RPO).
3. Score platform fit across: compute, Kubernetes, networking, data, security, observability, and cost controls.
4. Prototype the riskiest parts (networking + IAM + one production-like service path).
5. Decide based on operational reality, not feature checklists.

Final thoughts: pick the cloud your team can run confidently

AWS, Azure, and GCP can all power world-class products. The winning choice is the platform that matches your technical constraints, supports your team’s strengths, and provides the governance and reliability your business needs-without slowing delivery.

When cloud selection is anchored in technical criteria (not hype), the result is a platform that scales cleanly, stays secure, and remains cost-efficient as your product grows.