Azure vs AWS: Choose the Right Cloud Computing Platform for Your Business

Amazon Web Services (AWS) and Microsoft Azure are two of the most established cloud computing platforms, helping businesses run applications, store data, and scale infrastructure without maintaining all their own physical servers. 

AWS, provided by Amazon, is a strong fit for cloud-native applications and startups that need flexible infrastructure and a broad range of services. Azure, provided by Microsoft, is well suited for Microsoft-centric enterprise environments and organizations that need hybrid cloud capabilities.

In this blog, we provide a detailed comparison of Azure vs AWS, covering their features, compute and storage capabilities, pricing models, security, performance, scalability, hybrid-cloud support, ease of use, and common business use cases.

Azure vs AWS: Quick Side-by-Side Comparison

Microsoft Azure and Amazon Web Services (AWS) are established cloud platforms with different strengths across infrastructure, pricing, services, and enterprise integration.

Category	AWS	Azure
Provider	Amazon Web Services	Microsoft
Market Position	Largest cloud provider by market share	Major enterprise-focused cloud platform
Compute	Amazon EC2	Azure Virtual Machines
Object Storage	Amazon S3	Azure Blob Storage
Identity Management	AWS Identity and Access Management (IAM)	Microsoft Entra ID
Database Options	Amazon RDS, Aurora, DynamoDB, Redshift	Azure SQL Database, Cosmos DB, Azure Database for PostgreSQL
Data Analytics	Amazon EMR, Redshift, Athena, Glue	Synapse Analytics, Data Factory, HDInsight
Artificial Intelligence	Amazon SageMaker, Bedrock, Rekognition	Azure Machine Learning, Azure AI Services, Azure OpenAI Service
Hybrid Cloud	AWS Outposts and related services	Azure Arc, Azure Stack, and hybrid services
Developer Tools	CloudFormation, CodePipeline, CodeBuild	Azure DevOps, GitHub, Visual Studio integration
Best Suited For	Cloud-native workloads, startups, global applications, and specialized infrastructure	Microsoft-centric enterprises, regulated industries, and hybrid environments

Both providers offer similar core capabilities, but their service design, pricing structure, ecosystem, and integration options differ.

Advantages and Disadvantages of Azure and AWS

The following advantages and disadvantages are based on the platform analysis of AWS and Azure, along with reviewer feedback from the Amazon EC2 vs Azure Virtual Machines comparison on G2.

Platform	Advantages	Disadvantages
AWS	Large service catalog, strong global infrastructure, broad open-source support, and flexible deployment options	Pricing can be difficult to manage, services have a steep learning curve, and advanced configurations require expertise
	Mature cloud-native ecosystem and extensive third-party support	Less natural integration for organizations heavily dependent on Microsoft technologies
Azure	Strong Microsoft integration, hybrid-cloud capabilities, enterprise identity management, and familiar developer tools	Some services can be complex to configure, pricing varies across products, and platform-specific knowledge may be required
	Useful for Windows Server, Microsoft 365, Active Directory, and SQL Server environments	Some workloads may require additional planning when integrating non-Microsoft technologies

Your choice should be the one that fits your organization’s existing infrastructure, workforce, applications, and long-term cloud strategy.

AWS vs Azure: Pricing and Cost Comparison

AWS and Azure both use consumption-based pricing, but the final cost depends on services, regions, licensing, usage patterns, discounts, and data transfer requirements.

1. Compute Pricing

AWS and Azure both offer on-demand virtual machines and discounted options for customers willing to commit to longer usage periods.

Pricing Model	AWS	Azure
On-Demand Compute	Pay for usage by the hour or second, depending on the service	Pay for usage by the hour or second, depending on the service
Long-Term Discounts	Reserved Instances and Savings Plans can reduce costs for predictable workloads	Reserved VM Instances and Azure Savings Plans support committed workloads
Short-Term Discounted Compute	Spot Instances can provide discounts for interruptible workloads	Azure Spot Virtual Machines offer discounted pricing for interruptible workloads
Licensing Advantage	Supports Linux and commercial licensing models	Azure Hybrid Benefit can reduce costs for eligible Windows Server and SQL Server licenses

AWS Reserved Instances and Savings Plans are useful for predictable workloads. Azure Hybrid Benefit is especially relevant for enterprises that already own qualifying Microsoft licenses.

2. Free Tiers and Trial Credits

Both providers offer free usage options for new customers, but the exact services, credits, and limits vary by account eligibility and current provider terms.

Free Tier Factor	AWS	Azure
Trial Credit	Promotional credits may be available for eligible new customers and accounts	Eligible new customers may receive promotional account credits
Free Services	Selected services and usage limits, including eligible EC2, Lambda, and S3 usage	Selected services with monthly limits and promotional credits
Best Suited For	Developers testing infrastructure and cloud-native applications	Teams evaluating Microsoft-based cloud services and enterprise workloads

Free tiers are useful for testing, but they should not be used as the main basis for estimating production costs.

3. Storage Pricing

Storage prices vary by region and access tier. Azure Blob Storage uses Hot, Cool, Cold, and Archive tiers, while AWS S3 offers Standard, Intelligent-Tiering, Infrequent Access, and Glacier storage classes.

Storage Type	AWS	Azure
Standard Object Storage	S3 Standard starts at approximately $0.023 per GB per month in selected US regions	Azure Blob Hot storage is approximately $0.0184 per GB per month for the supplied pricing tier
Frequently Accessed Data	S3 Standard	Blob Hot tier
Infrequently Accessed Data	S3 Standard-IA and One Zone-IA	Blob Cool and Cold tiers
Archive Storage	Glacier and Glacier Deep Archive	Blob Archive tier
Lowest Supplied Archive Price	Glacier Deep Archive: approximately $0.00099 per GB per month in selected regions	Blob Archive: approximately $0.002 per GB per month in the supplied pricing region

The supplied Azure pricing lists the following approximate storage rates for the first 50 TB per month:

Azure Storage Tier	Approximate Price Per GB/Month
Premium	$0.15
Hot	$0.0184
Cool	$0.01
Cold	$0.0036
Archive	$0.002

Azure storage costs can also include write operations, list operations, data retrieval, replication, and early-deletion charges.

For example, Cool storage may incur retrieval charges of approximately $0.01 per GB, while Archive retrieval may cost approximately $0.10 per GB. Minimum storage durations may also apply to Cool, Cold, and Archive tiers.

Before selecting a platform, review the data warehouse cost factors that affect storage, processing, data movement, and infrastructure expenses.

4. Example Monthly Workload Costs

The following examples use the supplied pricing references and should be treated as illustrative estimates rather than universal production costs.

Service	AWS Example	Azure Example
Virtual Machines	Costs vary by EC2 instance family, operating system, region, and usage	A supplied example lists five B2ats v2 virtual machines running for 30 days at approximately $71.28
Serverless Compute	Lambda pricing depends on requests and compute duration	Azure Functions is listed at approximately $0.20 per million executions
Object Storage	S3 Standard starts at approximately $0.023 per GB per month in selected US regions	Azure Blob Hot storage is listed at approximately $0.0184 per GB per month in the supplied pricing
Cool or Archive Storage	Glacier classes reduce storage costs for infrequently accessed data	Azure Cool, Cold, and Archive tiers reduce storage costs as access frequency decreases

Actual costs depend on region, workload duration, redundancy, support plans, and additional service charges.

5. Data Transfer and Egress

Data transfer costs can affect the total bill, particularly for applications that frequently move data between regions or cloud providers.

Transfer Type	AWS	Azure
Inbound Data Transfer	Generally free for many services	Generally free
Internet Egress	Pricing depends on service, region, and usage volume	Limited free outbound allowance may apply before region-dependent charges
Cross-Region Transfer	Rates vary by region and service	Rates vary by region, redundancy configuration, and service
Multi-Cloud Transfer	Can become expensive when moving large datasets between providers	Can become expensive when moving large datasets between providers

Businesses should include network transfer, replication, backup, and cross-region traffic when calculating the total cost of ownership.

6. Which Platform Is Cheaper?

There is no universal winner on pricing.

• AWS may offer better flexibility for customers that optimize workloads through Savings Plans, Reserved Instances, Spot Instances, and custom instance families. 
• Azure can be more cost-effective for organizations with existing Microsoft licenses or large investments in Windows Server and SQL Server.

For a reliable comparison, businesses should model their actual workloads instead of comparing individual service prices in isolation.

AWS vs Azure: Compute Capabilities

AWS and Azure both provide virtual machines, containers, serverless computing, and specialized hardware for demanding workloads.

Compute Capability	AWS	Azure
Virtual Machines	Amazon EC2	Azure Virtual Machines
Serverless	AWS Lambda	Azure Functions
Kubernetes	Amazon EKS	Azure Kubernetes Service
Container Services	Amazon ECS and EKS	Azure Container Instances and AKS
Specialized Processors	AWS Graviton and Nitro-based infrastructure	Multiple CPU and GPU instance families
High-Performance Computing	Broad HPC, GPU, FPGA, and specialized instance options	HPC and GPU-enabled virtual machines

• AWS EC2 offers a large selection of instance families for general-purpose computing, memory-intensive applications, storage workloads, GPUs, and high-performance computing.
• Azure provides comparable virtual machine options through A-series, D-series, E-series, H-series, NV-series, and ND-series instances.

For serverless applications, AWS Lambda has a broad ecosystem of integrations and runtime options. Azure Functions is particularly convenient for applications built around Microsoft services, Visual Studio, and Azure Event Grid.

Both platforms support auto-scaling, load balancing, multiple availability zones, and container orchestration.

Bottom line: AWS is a strong choice when instance variety and infrastructure flexibility matter most. Azure is a strong option for Microsoft-based applications and enterprise workloads.

AWS vs Azure: Storage Options

AWS and Azure both provide object, file, block, and archival storage services.

Storage Category	AWS	Azure
Object Storage	Amazon S3	Azure Blob Storage
Block Storage	Amazon Elastic Block Store (EBS)	Azure Managed Disks
File Storage	Amazon Elastic File System (EFS), FSx	Azure Files
Archive Storage	S3 Glacier, Glacier Deep Archive	Blob Archive tier
NoSQL Storage	DynamoDB	Azure Cosmos DB and Table Storage
Data Lake Integration	S3 with analytics and lake services	Blob Storage and Azure Data Lake Storage

Amazon S3 provides multiple storage classes, including Standard, Intelligent-Tiering, Standard-IA, One Zone-IA, Glacier Instant Retrieval, Glacier Flexible Retrieval, and Glacier Deep Archive.

Azure Blob Storage provides Hot, Cool, Cold, Archive, and Premium tiers. Azure also offers File Storage, Table Storage, Queue Storage, and Data Lake Storage capabilities.

Takeaway:

• AWS may be preferable for teams that need a wide range of specialized storage options. 
• Azure may be more convenient for organizations that want storage services closely connected with Microsoft applications and data platforms.

Organizations planning analytical platforms can compare available data warehouse tools before deciding how cloud storage should support reporting and analytics.

AWS vs Azure: Cloud Services and Features

AWS and Azure both provide hundreds of cloud services across infrastructure, databases, analytics, networking, security, artificial intelligence, and developer tools.

Category	AWS	Azure
Data Warehousing	Amazon Redshift	Azure Synapse Analytics
Data Integration	AWS Glue	Azure Data Factory
Business Intelligence	Amazon QuickSight	Power BI
Machine Learning	Amazon SageMaker	Azure Machine Learning
Generative AI	Amazon Bedrock	Azure OpenAI Service
Relational Databases	Amazon RDS, Aurora	Azure SQL Database
NoSQL Databases	DynamoDB	Cosmos DB
Internet of Things	AWS IoT Core	Azure IoT Hub
Monitoring	Amazon CloudWatch	Azure Monitor
Infrastructure Automation	CloudFormation	Azure Resource Manager and Bicep

AWS has a broad service catalog that supports many specialized workloads. It is often selected by organizations that want granular control over infrastructure and cloud-native architectures.

Azure provides strong integration across Microsoft products, including Power BI, Microsoft 365, Windows Server, SQL Server, GitHub, and Visual Studio.

Databases

AWS supports multiple database engines through Amazon RDS, Aurora, DynamoDB, ElastiCache, and other managed services.

Azure provides Azure SQL Database, Azure Database for PostgreSQL, Azure Database for MySQL, Cosmos DB, and other database services.

Takeaway:

• AWS may offer more flexibility for organizations running different database engines. 
• Azure is often convenient for teams already using SQL Server and Microsoft database technologies.

Artificial Intelligence and Machine Learning

AWS provides machine learning and artificial intelligence services through SageMaker, Bedrock, Rekognition, Comprehend, and Polly.

Azure provides Azure Machine Learning, Azure AI Services, and Azure OpenAI Service. Its integration with OpenAI models is a major differentiator for organizations building generative AI applications.

Takeaway:

• AWS is a strong option for service variety and cloud-native flexibility. 
• Azure is attractive for Microsoft integration, enterprise analytics, and generative AI use cases.

Organizations planning AI and analytics platforms should also consider the need for a data warehouse when determining where structured business data should be stored.

AWS vs Azure: Security and Compliance

Both AWS and Azure use a shared responsibility model. The provider secures the underlying cloud infrastructure, while customers are responsible for securing their applications, configurations, identities, and data.

Security Category	AWS	Azure
Identity Management	AWS IAM	Microsoft Entra ID
Key Management	AWS Key Management Service	Azure Key Vault
Threat Detection	Amazon GuardDuty	Microsoft Defender for Cloud
Security Monitoring	CloudTrail, Security Hub, CloudWatch	Microsoft Sentinel, Azure Monitor
Network Security	Security Groups, Network ACLs, AWS WAF	Network Security Groups, Azure Firewall, Azure WAF
Compliance	Broad global compliance portfolio	Broad global compliance portfolio with strong Microsoft enterprise integration

AWS IAM provides fine-grained access policies across AWS resources. Azure uses Microsoft Entra ID for identity, single sign-on, role-based access control, and integration with Microsoft 365 and on-premises Active Directory.

AWS KMS and CloudHSM support encryption and key management. Azure Key Vault provides similar capabilities for keys, certificates, and secrets.

Takeaway:

• Azure can be especially useful for organizations with existing Microsoft identity systems. 
• AWS provides extensive policy-level control for organizations with complex cloud-native security architectures.

Neither platform should be considered automatically secure without proper configuration, monitoring, access controls, and governance.

AWS vs Azure: Performance and Scalability

AWS and Azure are designed to support workloads ranging from small applications to global enterprise systems.

Performance Factor	AWS	Azure
Global Infrastructure	Large network of regions, availability zones, and edge locations	Extensive global infrastructure with strong enterprise and hybrid coverage
Availability	Multi-AZ architecture and regional redundancy	Availability Zones, Availability Sets, and regional redundancy
Auto-Scaling	EC2 Auto Scaling and application services	Azure Virtual Machine Scale Sets and autoscaling services
Hardware Options	Graviton, Nitro, GPUs, FPGAs, and specialized accelerators	Broad CPU, GPU, and accelerated computing options
Edge Delivery	CloudFront and Local Zones	Azure Front Door and Edge Zones
Reliability	Mature infrastructure and service-level options	Mature infrastructure and enterprise-grade availability options

AWS has a strong reputation for infrastructure diversity and global edge delivery. Its Graviton processors and Nitro architecture are designed to improve performance and efficiency for selected workloads.

Azure also provides high-performance computing, GPU-enabled virtual machines, FPGA capabilities, and regional deployment options.

Performance depends on the workload, instance type, application design, region, network architecture, and configuration.

Takeaway:

• AWS may be preferable for organizations that need broad hardware and edge options. 
• Azure can be a strong choice for Microsoft workloads, hybrid applications, and specific AI or enterprise compute requirements.

Organizations designing analytical infrastructure should also review building a data warehouse to understand how compute, storage, and data pipelines fit together.

AWS vs Azure: Support and Documentation

Both providers offer extensive documentation, training resources, support plans, and developer communities.

Support Area	AWS	Azure
Documentation	AWS Documentation, developer guides, API references, whitepapers, and Knowledge Center	Azure Documentation and Microsoft Learn
Training	AWS Skill Builder and certification programs	Microsoft Learn and Azure certifications
Community	Large global developer and partner community	Strong Microsoft developer and enterprise community
Enterprise Support	AWS Enterprise Support and technical account management options	Azure support plans, Professional Direct, and Microsoft enterprise support
Partner Ecosystem	Large network of consultants and cloud partners	Strong Microsoft partner and managed service provider network

AWS documentation is extensive and includes detailed technical references for nearly every service. Azure provides guided learning paths through Microsoft Learn, making it useful for beginners and teams already familiar with Microsoft technologies.

The quality of support often depends on the subscription level, region, partner relationship, and internal cloud expertise.

AWS vs Azure: Hybrid and Multi-Cloud Capabilities

Azure has traditionally placed a strong emphasis on hybrid-cloud deployments.

Azure Arc allows organizations to manage resources across on-premises infrastructure, Azure, and other cloud platforms through a centralized control plane. Azure Stack services also extend selected Azure capabilities into private data centers and edge environments.

AWS provides hybrid capabilities through AWS Outposts, AWS Local Zones, and other services that extend AWS infrastructure beyond traditional cloud regions.

Hybrid and Multi-Cloud Capability	AWS	Azure
Hybrid Infrastructure	AWS Outposts and related services	Azure Arc, Azure Stack, and Azure Stack HCI
Multi-Cloud Management	Integration with external environments through partners and migration tools	Azure Arc provides centralized management across environments
On-Premises Integration	Strong support for enterprise infrastructure and edge deployments	Strong integration with Windows Server, Active Directory, and Microsoft applications
Best Suited For	Organizations extending AWS workloads into physical locations	Organizations managing hybrid infrastructure across Azure, on-premises, and other clouds

Takeaway:

• Azure is often the better fit for enterprises with significant on-premises Microsoft infrastructure.
• AWS is a strong option for organizations that want to extend cloud-native workloads into local environments while maintaining AWS operational models.

Teams managing multiple data environments can also review data warehouse vs data lake to understand how different storage architectures can work together.

AWS vs Azure: Ease of Use and Learning Curve

Both platforms offer web consoles, command-line tools, software development kits, infrastructure automation, and managed services.

Factor	AWS	Azure
Beginner Experience	Broad options but can feel complex	Familiar for Microsoft users
Developer Experience	Strong command-line and infrastructure-as-code tooling	Strong integration with Visual Studio, GitHub, and Azure DevOps
Learning Resources	Extensive documentation, certifications, and community support	Microsoft Learn, certifications, tutorials, and guided modules
Administration	Requires knowledge of many AWS services and configurations	Benefits from Microsoft ecosystem familiarity
Best For	Teams comfortable with cloud-native infrastructure	Teams already invested in Microsoft technologies

Takeaway:

• AWS provides extensive configuration options, but its large service catalog can make the platform difficult for new users to navigate. Understanding which service to choose and how services connect often requires training and practical experience.
• Azure can feel more familiar to organizations already using Windows Server, Microsoft 365, SQL Server, Visual Studio, or GitHub. Microsoft Learn also provides structured learning paths for developers, administrators, and data professionals.

For first-time cloud users, the easier platform is often the one that matches their existing technical background.

When to Use Microsoft Azure

1. Microsoft-Centric Enterprise Environments

Azure is a natural fit for organizations already using Windows Server, Microsoft 365, SQL Server, Active Directory, Power BI, or Visual Studio. Azure services can connect these systems with fewer architectural changes. Existing Microsoft licenses may also qualify for Azure Hybrid Benefit savings.

2. Hybrid Cloud and On-Premises Infrastructure

Organizations with data centers, private infrastructure, or regulatory requirements may benefit from Azure Arc and Azure Stack. These services help teams manage selected resources across on-premises systems, Azure, and other environments.

3. Regulated Industries and Enterprise AI

Healthcare, financial services, government, and other regulated organizations can use Azure’s identity, compliance, governance, and security services. Azure is also a strong option for organizations building generative AI applications through Azure OpenAI Service and related Azure AI services.

Organizations planning enterprise analytics should review data warehouse best practices to improve governance, reliability, and operational consistency.

When to Use AWS

1. Cloud-Native Applications and Startups

AWS is often suitable for startups and engineering teams building applications directly in the cloud. Its broad selection of infrastructure, databases, containers, serverless services, and developer tools gives teams flexibility as their architecture evolves.

2. Global Applications and High Availability

AWS is a strong choice for applications that require global reach, distributed infrastructure, and multiple availability zones. Services such as EC2, Auto Scaling, Elastic Load Balancing, Route 53, and CloudFront support applications that need high availability and low-latency access across regions.

3. Specialized Infrastructure and Data Workloads

AWS offers a broad selection of compute, storage, analytics, machine learning, and high-performance infrastructure services. Organizations working with large-scale analytics, scientific computing, machine learning, and specialized hardware may benefit from AWS’s service variety and infrastructure options.

Teams building analytical systems can compare data warehouse modeling approaches before selecting their cloud data architecture.

How Hevo Supports Cloud Data Extraction Across Azure and AWS

Cloud platforms are often used alongside multiple data sources, including databases, SaaS applications, APIs, files, and business systems. Moving that data reliably into a warehouse or analytics platform can become difficult when pipelines are built and maintained manually.

Hevo helps teams automate data integration across cloud environments by connecting source systems with destinations such as cloud data warehouses and storage platforms.

Organizations can use Hevo to move data into Azure-based destinations such as Azure SQL Database, Azure Synapse Analytics, and Azure Blob Storage. AWS environments can connect with destinations such as Amazon Redshift, Amazon S3, and other supported analytical systems.

Hevo also helps reduce manual pipeline maintenance through automated schema handling, monitoring, scheduling, and data movement workflows. For organizations using Azure, AWS, or both platforms together, automated data integration can help maintain consistent access to operational and analytical data.

Understanding the difference between a data warehouse vs database can further help teams choose the right destination for operational reporting, analytics, and business intelligence.