AWS vs Google Cloud vs Azure: Cloud Platform Comparison for 2026

Choosing between AWS, Google Cloud, and Azure is one of the most consequential infrastructure decisions your organization will make. The cloud platform you select affects cost, performance, lock-in risk, and your team's productivity for years. This guide provides a comprehensive comparison to help you choose the right platform for your enterprise software needs in 2026.

Market Share Overview

The cloud infrastructure market remains dominated by three players, with significant differences in their respective positions:

| Provider | Market Share (2026) | Primary Strength | Typical Customer Profile | |----------|---------------------|------------------|--------------------------| | AWS | 32-35% | Breadth of services, enterprise maturity | Startups to Fortune 500, most industries | | Azure | 23-26% | Microsoft ecosystem integration, enterprise sales | Enterprises with Microsoft stack, hybrid cloud | | Google Cloud | 10-12% | Data analytics, AI/ML, Kubernetes-native | Data-heavy companies, AI startups, Kubernetes users |

AWS has the longest track record and largest service catalog. Azure excels when you already run Windows Server, Active Directory, Office 365, or Dynamics. Google Cloud leads in data analytics (BigQuery), AI/ML (Vertex AI), and Kubernetes (GKE is often considered the reference implementation).

The gap between AWS and the others has narrowed. Azure's growth has been driven by enterprise customers standardizing on Microsoft. Google Cloud has gained ground in data and AI workloads. For most organizations, the decision is less about raw capability and more about fit with your stack, team skills, and commercial terms.

Pricing Models Compared

All three providers use pay-as-you-go pricing with committed use discounts. Understanding the nuances matters for budgeting:

| Pricing Aspect | AWS | Google Cloud | Azure | |----------------|-----|--------------|-------| | On-demand default | Per-second (60s min) | Per-second (60s min) | Per-second (60s min) | | Reserved instances | 1-3 year commits, up to 72% savings | Committed use discounts, up to 57% | 1-3 year commits, up to 72% | | Spot/preemptible | Spot Instances | Preemptible VMs | Spot VMs | | Free tier | 12 months free for many services | Always-free tier (limited) | 12 months free, always-free for some | | Pricing complexity | High (many options) | Moderate | High (similar to AWS) | | Calculator | AWS Pricing Calculator | Google Cloud Pricing Calculator | Azure Pricing Calculator |

Practical tip: Run parallel cost estimates for your workload across all three platforms. Real-world savings of 20-40% are common when switching providers, though migration costs must be factored in.

Reserved instance strategy: Committing to 1-3 years can cut compute costs by more than half. Start with a smaller commitment and expand as you understand your usage. Avoid over-committing in the first year when workloads are still evolving.

Compute Options

Compute is the foundation of any cloud workload. Here's how the options compare:

| Service | AWS | Google Cloud | Azure | |---------|-----|--------------|-------| | Virtual machines | EC2 | Compute Engine | Virtual Machines | | Managed Kubernetes | EKS | GKE | AKS | | Serverless containers | Fargate | Cloud Run | Container Instances | | Managed serverless | Lambda | Cloud Functions | Azure Functions | | Batch processing | Batch | Cloud Batch | Batch |

Kubernetes and Container Orchestration

For teams building microservices architectures, Kubernetes is often the orchestration layer of choice:

| Factor | AWS EKS | Google GKE | Azure AKS | |--------|---------|------------|-----------| | Kubernetes pedigree | Certified K8s | Original K8s creator | Certified K8s | | Autopilot/managed | EKS Fargate (limited) | GKE Autopilot (mature) | AKS (standard) | | Control plane cost | $0.10/hr per cluster | Free (in standard tier) | Free | | Ease of use | Good | Best | Good | | Multi-cloud portability | Standard | Standard | Standard |

Recommendation: If Kubernetes is central to your strategy, GKE often delivers the smoothest experience. For AWS-native workloads, EKS integrates well. Azure AKS shines when you need tight integration with Azure AD, Azure DevOps, and Microsoft security tools.

Serverless containers: Cloud Run (GKE) and Azure Container Instances offer the simplest path from container image to running service. AWS Fargate removes node management for ECS and EKS but has more configuration overhead. For simple microservices, Cloud Run's scale-to-zero and pay-per-request model is hard to beat.

Managed Databases

Each provider offers managed relational and NoSQL databases with different strengths:

| Database Type | AWS | Google Cloud | Azure | |---------------|-----|--------------|-------| | PostgreSQL/MySQL | RDS | Cloud SQL | Azure Database for PostgreSQL/MySQL | | NoSQL document | DocumentDB (MongoDB-compatible) | Firestore | Cosmos DB | | Wide-column | DynamoDB | Bigtable | Cosmos DB | | In-memory cache | ElastiCache (Redis/Memcached) | Memorystore | Azure Cache for Redis | | Data warehouse | Redshift | BigQuery | Synapse Analytics |

Database Comparison Highlights

| Factor | AWS | Google Cloud | Azure | |--------|-----|--------------|-------| | PostgreSQL managed | RDS, Aurora | Cloud SQL (simple, robust) | Azure Database (flexible) | | Best data warehouse | Redshift (mature) | BigQuery (serverless, scales to petabytes) | Synapse (SQL Server integration) | | Globally distributed NoSQL | DynamoDB Global Tables | Firestore (automatic) | Cosmos DB (multi-region) | | Open source fidelity | Some forks (DocumentDB) | High (Cloud SQL = standard Postgres) | High for Postgres/MySQL |

For SaaS architecture involving multi-tenancy or complex data models, BigQuery excels at analytics; Aurora and Cosmos DB offer strong transactional capabilities.

Migration consideration: Moving databases between clouds is non-trivial. PostgreSQL on RDS, Cloud SQL, or Azure Database can be migrated with logical replication or dump/restore, but plan for downtime or complex cutover. Cosmos DB, DynamoDB, and Firestore are proprietary; migrating away requires significant re-architecture.

AI and Machine Learning Services

AI/ML has become a key differentiator. All three providers offer managed ML platforms:

| Capability | AWS | Google Cloud | Azure | |------------|-----|--------------|-------| | Managed ML platform | SageMaker | Vertex AI | Azure Machine Learning | | Pre-trained models/APIs | Rekognition, Comprehend, Transcribe | Vision, Natural Language, Speech-to-Text | Cognitive Services | | Generative AI | Bedrock (multiple models) | Vertex AI (Gemini, etc.) | Azure OpenAI (OpenAI partnership) | | MLops maturity | Good | Strong | Good | | Custom model training | SageMaker | Vertex AI | Azure ML |

AI/ML Comparison

| Factor | AWS | Google Cloud | Azure | |--------|-----|--------------|-------| | Generative AI variety | Bedrock: Claude, Llama, others | Vertex AI: Gemini, open models | Azure OpenAI: GPT-4, others | | Ease of getting started | Moderate | Good | Good (if using OpenAI) | | Enterprise AI features | SageMaker Studio | Vertex AI Studio | Azure AI Studio | | Cost structure | Per inference, per training hour | Similar | Similar |

Google Cloud benefits from Google's AI research leadership. Azure has a strong position through its OpenAI partnership. AWS offers the broadest model selection through Bedrock.

Vendor lock-in: Generative AI APIs can create lock-in. Consider abstraction layers (LangChain, LlamaIndex) or multi-provider fallbacks if you want flexibility to switch models or clouds. For most applications, picking one provider and committing is simpler; revisit if regulatory or cost pressures emerge.

Serverless Computing

Serverless removes infrastructure management for event-driven and request-based workloads:

| Aspect | AWS Lambda | Google Cloud Functions | Azure Functions | |--------|-------------|------------------------|-----------------| | Max timeout | 15 minutes | 9 minutes (gen2: 60 min) | 10 minutes (default), configurable | | Cold start | Moderate | Moderate | Moderate | | Supported languages | Node, Python, Go, Java, .NET, Ruby | Node, Python, Go, Java, .NET, PHP, Ruby | Node, Python, C#, Java, PowerShell | | Pricing model | Per request + duration | Per request + GB-seconds | Per execution + GB-seconds | | Integrations | 200+ services | 90+ triggers | 100+ bindings |

All three support HTTP-triggered functions suitable for APIs. For high-throughput, event-driven architectures, the differences are often marginal; choose based on your existing cloud footprint.

Content Delivery Network (CDN)

| Provider | CDN Service | Edge locations | Key Features | |----------|-------------|----------------|--------------| | AWS | CloudFront | 400+ points of presence | Deep S3 integration, Lambda@Edge | | Google Cloud | Cloud CDN | Global (built on Google's network) | Backed by Load Balancers, low latency | | Azure | Azure CDN | 100+ edge locations | Standard/Premium tiers, Verizon partnership |

For static assets, video streaming, and API acceleration, all three deliver solid performance. CloudFront has the widest adoption; Cloud CDN benefits from Google's backbone; Azure CDN suits Microsoft-centric deployments.

Enterprise Features

| Feature | AWS | Google Cloud | Azure | |---------|-----|--------------|-------| | Enterprise support | Well-established | Growing | Strong (Microsoft relationship) | | Compliance certifications | Extensive | Extensive | Extensive | | Hybrid cloud | Outposts, Snow family | Anthos | Arc, Stack | | Identity integration | IAM, Cognito | IAM, Identity Platform | Azure AD (excellent for enterprises) | | Government cloud | GovCloud | Assured Workloads | Azure Government |

Enterprise consideration: If your organization already runs Microsoft 365, Active Directory, and Windows workloads, Azure's integrated identity and management often reduces friction. AWS remains the default for many enterprises due to maturity. Google Cloud appeals to data-centric and cloud-native organizations.

Startup Credits and Programs

| Provider | Program | Typical Offer | Requirements | |----------|---------|---------------|--------------| | AWS | AWS Activate | $1,000-$100,000 credits | Startup, accelerator/VC backed | | Google Cloud | Google for Startups | $2,000-$200,000+ credits | Startup, accelerator/VC backed | | Azure | Microsoft for Startups | $1,000-$150,000+ credits | Startup, accelerator/VC backed |

All three offer substantial credits for qualifying startups. Google Cloud's program is often the most generous for early-stage companies. Check current terms as programs evolve.

When to Choose Each Platform

Choose AWS when:

• You need the broadest service catalog
• Your team has existing AWS expertise
• You're building a diverse stack with many integrated services
• Enterprise compliance requirements favor AWS's track record
• You want maximum third-party tool and partner ecosystem support

Choose Google Cloud when:

• Data analytics and BigQuery are central
• You're building AI/ML-heavy applications
• Kubernetes is your primary orchestration layer
• You value simplicity and developer experience
• You're a startup with credits available

Choose Azure when:

• You run Microsoft 365, Active Directory, or Dynamics
• Your organization has existing enterprise agreements with Microsoft
• Hybrid cloud with on-premises Windows is important
• You need tight integration with .NET and Microsoft developer tools
• Compliance or procurement prefers Microsoft

Multi-Cloud Considerations

Some organizations spread workloads across multiple clouds for redundancy, compliance, or vendor leverage. Multi-cloud increases operational complexity: different APIs, different billing, different security models. Unless you have specific requirements (e.g., geographic redundancy, regulatory separation), starting with a single primary cloud is usually simpler. Revisit multi-cloud when scale, compliance, or commercial terms justify the overhead. Most teams are better served mastering one cloud deeply before expanding.

Making the Decision

There is no universally "best" cloud. The right choice depends on your workload, team skills, existing investments, and strategic priorities. Use our Tech Stack Recommender to explore infrastructure and framework options that align with your project goals.

Need help selecting and implementing a cloud strategy? Our team at ZTABS designs and builds scalable enterprise software on AWS, Google Cloud, and Azure. We can assess your requirements, compare costs, and recommend the optimal platform.

Get a free consultation.