Cloud computing has transformed how organisations deploy and manage IT infrastructure. Instead of purchasing and maintaining physical servers on-site, businesses can spin up virtual machines in a public cloud and pay only for the resources they consume. Three leading services dominate this infrastructure-as-a-service space: Amazon EC2 on Amazon Web Services, Azure Virtual Machines on Microsoft Azure, and Google Compute Engine on Google Cloud Platform.
Each provider offers on-demand compute capacity, but they differ in philosophy, instance catalogues, pricing strategies, regional footprints, and integrations with their broader ecosystems. Understanding these differences is essential for architects, developers, and decision makers who must match workloads to the most suitable platform. Amazon EC2 was launched in 2006 and set the template for large-scale cloud infrastructure. Its guiding principle is maximum flexibility: customers choose from dozens of instance families optimised for general purpose, compute, memory, storage, or accelerators, then fine-tune storage options, networking bandwidth, and security rules.
EC2 emphasises both breadth and depth, operating the largest set of global regions and continually releasing new instance generations based on the latest CPUs and GPUs. Pricing is equally flexible, featuring On-Demand, Reserved, Savings Plan, and Spot purchasing models. These options allow businesses to trade commitment for cost savings or bid on unused capacity at steep discounts. Microsoft introduced Azure Virtual Machines in 2010, building on its enterprise software pedigree. Organisations already invested in Windows Server, Active Directory, SQL Server, or .NET frameworks often find Azure’s ecosystem familiar and tightly integrated. Azure VMs support a comparable range of instance families, yet they layer in hybrid-cloud tools such as Azure Arc and Azure Stack to extend governance and operational consistency across on-premises and cloud resources.
Licensing programs like Azure Hybrid Benefit let customers reuse existing Windows licences, lowering total cost of ownership for Microsoft-centric workloads. Google Compute Engine focuses on performance efficiency and developer-friendly tooling. It pioneered per-second billing to reduce waste on short-lived tasks and introduced sustained-use discounts that automatically lower costs for instances running continuously. Compute Engine also offers custom machine types, enabling users to define exact vCPU and memory ratios rather than selecting from fixed sizes. Live migration technology keeps VMs running during host maintenance, minimising downtime for critical services. Backed by Google’s high-bandwidth global network, Compute Engine is well-suited to data analytics, rendering, and machine-learning workloads. All three services provide fundamental capabilities: launching Linux or Windows instances, attaching block and object storage, configuring security groups or firewalls, and scaling capacity up or down in response to demand.
Yet several core dimensions reveal important contrasts. Instance catalogues vary: AWS leads in variety, while Google’s custom types offer precision sizing, and Azure aligns naming and capabilities with its Windows heritage. Pricing strategies differ: AWS presents the widest array of purchase models, Azure adds licence-reuse incentives, and Google emphasises fine-grained billing with automatic discounts. Global infrastructure footprints diverge: AWS maintains the most regions and availability zones, Azure is second and heavily invested in hybrid connections, while Google concentrates on network performance in fewer but strategically located regions. Service integration also matters. EC2 pairs naturally with services such as S3 for storage, RDS for relational databases, Lambda for serverless functions, and CloudWatch for monitoring. Azure VMs benefit from proximity to Azure SQL Database, Azure Monitor, and native security tools integrated with Microsoft Defender. Compute Engine sits alongside BigQuery for analytics, Cloud Storage for unstructured data, and Kubernetes Engine for container orchestration. Choosing a platform therefore affects not only compute instances but the entire application architecture. From a usability perspective, each cloud offers a web console, command-line interface, and application programming interface. AWS provides a highly granular but sometimes complex management experience.
Azure’s portal emphasises guided wizards and integrates seamlessly with Visual Studio and PowerShell. Google Cloud’s console offers a streamlined interface with quick-start templates and integrated Cloud Shell for immediate command-line access. Security and compliance are priorities across all providers. Identity and access management systems control permissions; encrypted storage and key-management services protect data; and distributed denial-of-service defences shield public endpoints. Where they differ is in breadth of regulatory certifications: AWS and Azure cover the most industry and government standards, while Google emphasises privacy features and default encryption. When mapping workloads to platforms, target use cases clarify the strengths of each service. EC2 is ideal for businesses needing extensive instance catalogues, global latency optimisation, and deep integrations with a broad suite of services. Azure VMs excel for organisations entrenched in Microsoft software or requiring seamless extension of on-premises environments.
Compute Engine stands out for projects demanding flexible resource sizing, cost-efficient continuous workloads, or tight coupling with data analytics and machine-learning pipelines. In summary, while AWS EC2, Azure Virtual Machines, and Google Compute Engine all deliver virtual machines on demand, they differ in instance selection, pricing mechanics, regional presence, and ecosystem connections. Evaluating these foundational characteristics sets the stage for deeper analysis of performance benchmarks, cost optimisation tactics, and operational tooling, which will be explored in the subsequent parts of this comparison.
In-Depth Feature Comparison – AWS EC2 vs Azure Virtual Machines vs Google Compute Engine
Each cloud provider offers a variety of virtual machine configurations to match specific workload needs. The classification of instances or virtual machines and their customization capabilities vary across platforms.
AWS EC2 offers a wide selection of instance types grouped into categories such as General Purpose, Compute Optimized, Memory Optimized, Accelerated Computing, and Storage Optimized. These families include multiple instance sizes, allowing users to scale resources. EC2 also supports bare-metal instances and the use of AWS-designed Graviton processors for improved workload performance.
Azure Virtual Machines organizes offerings into categories like B-series for burstable workloads, D-series for balanced performance, F-series for compute optimization, and NV-series for GPU-based processing. It also supports custom images and hybrid environments through licensing options.
Google Compute Engine provides predefined machine types such as N1, N2, and E2, along with the flexibility of custom machine types where users can specify exact vCPU and memory configurations. This allows for precise resource allocation tailored to specific workloads.
Performance and Compute Efficiency
Performance differs based on instance types, hardware, and provider optimizations.
AWS EC2 uses various processors including Intel, AMD, and its own Graviton chips. Its Nitro System boosts efficiency by offloading storage and networking. High-performance instances support applications requiring parallel processing and GPU compute.
Azure Virtual Machines support Intel and AMD chips with optimized networking. Specific instances are optimized for workloads like SAP or AI. Users can access fast disk options and accelerate performance with features like Azure Accelerated Networking.
Google Compute Engine provides sustained usage discounts and allows live migrations during maintenance without downtime. Its infrastructure supports compute-optimized machines, making it suitable for high-performance and latency-sensitive applications.
Storage and Disk Options
Storage impacts speed, availability, and cost. Each platform offers several storage types for different use cases.
AWS EC2 supports Amazon EBS with options like gp3, io2, and st1, plus local instance storage. It also provides Amazon EFS for file storage and scalable elastic volumes that can be resized dynamically.
Azure offers Managed Disks in Standard HDD, Standard SSD, Premium SSD, and Ultra Disk. It also includes Ephemeral OS disks for temporary data and shared disks for clustered applications.
Google Compute Engine provides Persistent Disks, Local SSDs for low-latency use cases, and regional disks for availability. Custom storage configurations and snapshots allow for flexibility and redundancy.
Networking and Connectivity
Each platform has a comprehensive networking suite for security, performance, and integration.
AWS uses Virtual Private Cloud (VPC) with features like Elastic IP, Security Groups, Network ACLs, and Direct Connect for hybrid connectivity. Load balancing is built into the service with support for scaling and high availability.
Azure uses Azure Virtual Network with subnets, route tables, and network security groups. Load balancing and private connections via ExpressRoute support secure hybrid environments.
Google offers Virtual Private Cloud with global reach, Cloud NAT, and Private Google Access. It provides global load balancing and peering across projects and networks.
Security and Compliance
Security offerings include identity management, encryption, and compliance tools.
AWS supports IAM, KMS, and security groups. It offers DDoS protection, encryption in transit and at rest, and maintains extensive compliance certifications.
Azure uses Active Directory, Security Center, and Key Vault for key management. It supports disk encryption and integrates with compliance tracking tools to meet international standards.
Google provides IAM, customer-managed encryption keys, and Shielded VMs. Confidential computing and secure boot features enhance security for sensitive workloads.
Automation and DevOps Support
Automation tools help manage resources at scale and implement CI/CD pipelines.
AWS offers CloudFormation, Systems Manager, and CodePipeline. It supports infrastructure as code and auto-scaling across regions and instance types.
Azure provides Resource Manager templates, Automation, and Azure DevOps tools. It also supports GitHub Actions and hybrid management for on-premises and cloud environments.
Google supports Deployment Manager and third-party tools like Terraform. Cloud Build and Scheduler enable automation, while CI/CD integration with repositories supports efficient development workflows.
User Interface and Management Tools
Ease of use depends on management portals, command-line interfaces, and APIs.
AWS offers a detailed web console, CLI, and SDKs. CloudWatch and Trusted Advisor provide monitoring and optimization.
Azure includes an intuitive portal, CLI, and PowerShell support. Its dashboard and logging tools provide a central view of operations.
Google features a clean interface, gcloud CLI, and API access. Built-in monitoring, alerting, and tracing tools help manage cloud applications efficiently.
This part examined the technical strengths and configurations of AWS EC2, Azure Virtual Machines, and Google Compute Engine. AWS provides broad instance options and customization. Azure emphasizes hybrid integration and enterprise tooling. Google leads in flexibility, live migration, and granular pricing.
Pricing Models and Cost Efficiency – AWS EC2 vs Azure Virtual Machines vs Google Compute Engine
One of the biggest considerations when choosing a cloud provider is the cost. Each cloud provider uses a different pricing model based on compute usage, storage, networking, and value-added services. These pricing structures may seem complex at first, but they generally fall into a few categories: on-demand, reserved instances, and spot or preemptible pricing.
AWS EC2 Pricing Model
Amazon EC2 offers multiple pricing strategies to help customers balance cost and flexibility. The most popular models are:
On-demand pricing: This is the simplest model where you pay for compute capacity by the second or hour without long-term commitments. It is suitable for short-term or unpredictable workloads. While flexible, it is often the most expensive.
Reserved instances: These provide significant discounts in exchange for a commitment to use a specific instance type in a region over a one- or three-year term. They are ideal for predictable workloads with consistent usage.
Savings plans: Introduced to simplify reserved pricing, AWS savings plans offer flexibility across instance families and regions in exchange for a usage commitment. It reduces complexity while still offering cost savings.
Spot instances: These offer the lowest pricing, allowing users to bid on unused EC2 capacity. These instances can be terminated by AWS when demand increases. They are well-suited for fault-tolerant and batch-processing jobs.
Dedicated hosts: These provide physical servers dedicated to your use, which can be useful for licensing scenarios and compliance requirements.
AWS also has tiered pricing for storage and offers free tiers for limited usage, which is helpful for startups or individuals testing the service.
Azure Virtual Machines Pricing Model
Azure pricing is also based on a pay-as-you-go model, allowing customers to only pay for what they use. The common pricing strategies are:
Pay-as-you-go: You pay by the second for the VM usage. It is useful for development, testing, or workloads with changing requirements.
Reserved virtual machine instances: Customers receive discounts up to 72 percent by committing to one or three years of use. Azure gives flexibility in payment options (full upfront, partial upfront, or monthly payments).
Spot VMs: Azure offers spot pricing for interruptible workloads at significantly lower prices. Spot VMs are ideal for non-critical applications where cost savings are more important than uptime.
Hybrid benefit: Azure offers a cost advantage if you already own Windows Server or SQL Server licenses through the Azure Hybrid Benefit program.
Azure’s pricing also includes dev/test pricing for MSDN subscribers and discounts through enterprise agreements. Their pricing calculator helps estimate cost based on usage and region.
Google Compute Engine Pricing Model
Google uses a straightforward pricing structure with several built-in discounts that do not require long-term commitments:
Sustained use discounts: Automatically applied when instances run for a significant portion of the month. Discounts increase with usage duration.
Committed use contracts: Like reserved instances in AWS or Azure, users can commit to use a specific number of vCPUs and memory for a one- or three-year term to receive discounts.
Preemptible VMs: These are short-lived instances offered at a steep discount, similar to AWS Spot instances. They are perfect for batch jobs, CI/CD workloads, and stateless applications.
Custom machine types: Users can create a VM with the exact number of vCPUs and memory needed. This helps optimize pricing by avoiding over-provisioning.
Per-second billing: Google charges for compute resources by the second after the first minute. This granular billing results in cost savings for workloads that do not run for full hours.
Google also provides $300 in free credits to new users and has sustained competitive on-demand pricing, especially for general-purpose E2 instances.
Comparative Pricing Analysis
To get a clearer view, let’s consider a few common usage scenarios and estimate costs across AWS, Azure, and Google. Pricing varies by region, so we’ll use US East for consistency.
Use case 1: General-purpose VM, 2 vCPU, 8 GB RAM, Linux, 730 hours/month
AWS EC2 t3.large (on-demand): approximately $70–$75/month
Azure D2s v3 (on-demand): approximately $80–$85/month
Google E2-standard-2 (on-demand with sustained use discount): approximately $60–$65/month
Use case 2: Reserved instance, same configuration, one-year term
AWS EC2 t3.large (reserved, no upfront): around $50–$55/month
Azure D2s v3 (reserved, one year): around $55–$60/month
Google E2-standard-2 (committed use): around $45–$50/month
Use case 3: Preemptible/Spot VM, same configuration
AWS EC2 t3.large (spot): around $20–$25/month
Azure D2s v3 (spot): around $18–$22/month
Google E2-standard-2 (preemptible): around $15–$20/month
From these comparisons, it is evident that Google Compute Engine typically offers the most competitive pricing for general-purpose instances, especially when taking advantage of sustained use and preemptible instances. AWS and Azure remain competitive, with AWS providing more advanced savings plans and Azure offering more enterprise-friendly pricing options.
Hidden Costs and Additional Considerations
When comparing cloud providers based solely on compute cost, it is easy to overlook associated expenses. These can include:
Data transfer: AWS charges for outbound data transfers, while inbound is free. Azure and Google also charge for outbound traffic but often include allowances. Google’s egress costs are usually slightly lower.
Storage: The type and size of storage (standard, SSD, provisioned IOPS) significantly affect monthly bills. AWS EBS, Azure Managed Disks, and Google Persistent Disks all have different pricing and performance tiers.
Snapshots and backups: Each provider charges for snapshot storage differently. Azure includes some built-in backup features with virtual machines, while AWS and Google treat backups as separate storage volumes.
Networking: Load balancers, NAT gateways, and VPN connections add to the cost. Each provider has its own pricing tiers for these services.
Support plans: AWS, Azure, and Google all offer multiple support levels. These are billed separately and can significantly affect monthly cloud costs for enterprises.
Billing Transparency and Tools
Each cloud provider has its own cost estimation and monitoring tools:
AWS offers the AWS Pricing Calculator, Cost Explorer, and Budgets tools to help customers track and forecast spending.
Azure provides a similar calculator and Azure Cost Management tools integrated directly into the Azure Portal.
Google includes its Cloud Pricing Calculator and Billing Reports for real-time visibility. Google’s console is often praised for its simplicity and cost transparency.
Choosing the Right Provider Based on Cost
When cost is the determining factor, businesses must weigh both pricing and operational fit. For startups or businesses with fluctuating workloads, Google’s per-second billing and custom machine types offer the flexibility to scale while keeping costs down. Larger organizations with predictable workloads may find AWS or Azure reserved pricing or savings plans more beneficial over time.
Each platform offers free usage tiers, making it easier to trial services before committing. This can be a good way to test performance and pricing models with your specific workload before going all-in.
Pricing among AWS EC2, Azure Virtual Machines, and Google Compute Engine varies depending on how you use the service. While on-demand costs are similar, the way each provider handles discounts, flexibility, and long-term commitments is quite different. Google stands out with its sustained use discounts and custom machines. AWS has the most mature cost-saving models with reserved instances and savings plans. Azure offers integrated licensing benefits and hybrid incentives, making it attractive to enterprises already invested in Microsoft tools.
Performance, Scalability, and Ecosystem Comparison of AWS EC2, Azure Virtual Machines, and Google Compute Engine
When it comes to selecting a cloud provider, performance is a major concern. It determines how fast and efficiently your applications will run, how well your system responds to user demands, and how resilient your infrastructure is under pressure. AWS EC2, Azure Virtual Machines, and Google Compute Engine all deliver solid performance, but there are differences in how they handle workloads, infrastructure choices, and the flexibility of resources.
AWS EC2 offers a wide selection of instance types optimized for compute, memory, storage, and GPU workloads. Performance is consistent and enhanced by innovations like Nitro hypervisor, Elastic Block Store for high-speed disk access, and dedicated networking resources. AWS also supports dedicated bare metal instances for performance-sensitive applications. Their P-series and Inf1 instances are tailored for AI and machine learning performance, and C-series instances are best suited for high compute tasks.
Azure Virtual Machines also provide a diverse set of VMs optimized for general purpose, compute-intensive, memory-optimized, storage-optimized, GPU, and HPC workloads. Azure uses its own hypervisor and offers accelerated networking to boost throughput and reduce latency. The platform also integrates deeply with services like Azure SQL, Azure Synapse, and Azure Kubernetes Service, which can provide performance advantages for specific use cases.
Google Compute Engine offers predefined and custom VM types with balanced price-to-performance ratios. Performance on GCE is consistent and supported by live migration, custom VMs, and tight integration with Google’s backend infrastructure. Google has also made strides in AI and ML workloads with their A2 and TPU-based machines. GCE’s custom VMs allow users to fine-tune resources and avoid over-provisioning, giving it an edge in cost-performance optimization.
Scalability and Flexibility
All three providers support auto-scaling, but they implement it differently.
AWS EC2 Auto Scaling enables automatic adjustments to the number of instances in a group based on policies you define. It’s highly configurable and integrates with load balancers and Amazon CloudWatch. This allows businesses to respond quickly to demand changes without manual intervention.
Azure Virtual Machines support VM Scale Sets, which allow you to deploy and manage a group of load-balanced VMs. It automatically increases or decreases the number of VM instances in response to demand or a defined schedule. Azure integrates this with other Microsoft services for easier management in hybrid environments.
Google Compute Engine provides managed instance groups, which can automatically scale VM instances up or down based on load. These groups integrate well with Google Cloud Load Balancing and Stackdriver monitoring tools. Google’s global infrastructure enables smooth scaling across multiple regions.
In terms of scaling flexibility, Google’s use of custom VMs and automated live migration gives it a strong advantage for dynamic workloads. AWS’s broad global infrastructure and mature auto-scaling capabilities make it the most reliable for scaling at a global enterprise level. Azure’s scaling shines when used in a Microsoft-heavy ecosystem, especially in hybrid setups.
Deployment and Management Ease
Ease of use is an often underestimated factor that can greatly influence how quickly you can bring systems online, debug problems, and optimize your environment.
AWS provides the AWS Management Console, AWS CLI, and extensive SDKs for major programming languages. CloudFormation allows you to define infrastructure as code. The learning curve can be steep for beginners, but it’s widely supported by documentation and community resources.
Azure has the Azure Portal, Azure CLI, and support for PowerShell scripting. Azure Resource Manager (ARM) templates allow for automated deployments. Its UI is polished and user-friendly, especially for those with Windows Server or Microsoft 365 backgrounds.
Google Compute Engine uses the Google Cloud Console and gcloud CLI. Deployment Manager helps define resources as templates. The Google UI is considered clean and intuitive, particularly for developers familiar with Kubernetes and modern container architectures. GCE is often praised for its simplified VM provisioning experience.
All three providers support APIs, Terraform, Ansible, and other DevOps tools. AWS and Azure have broader enterprise integrations, while Google emphasizes modern workflows with containers, DevOps pipelines, and managed services like Cloud Run and Cloud Functions.
Ecosystem and Integration
AWS has the largest ecosystem of services, with deep integration across compute, storage, analytics, networking, and AI/ML. EC2 integrates with services like S3, RDS, CloudFront, Lambda, and SageMaker. The ecosystem is highly mature and supported by a vast community of partners and developers.
Azure Virtual Machines integrate with a full Microsoft stack including Azure Active Directory, SQL Server, Dynamics, and SharePoint. Azure has an edge in hybrid cloud thanks to Azure Stack, which extends cloud capabilities to on-premises data centers. Enterprises heavily invested in Microsoft products often find Azure a smoother fit.
Google Compute Engine integrates with Google Cloud’s data and AI services like BigQuery, Vertex AI, Firebase, and Kubernetes Engine. Its ecosystem is ideal for startups, developers, and companies building data-heavy or microservices-based applications. Google’s focus on open-source and containerized workloads sets it apart.
Security and Compliance
Security is another critical area. All three platforms offer built-in tools for identity and access management, encryption, compliance, and monitoring.
AWS provides IAM, KMS for encryption, VPC for networking, GuardDuty for threat detection, and Inspector for vulnerability scanning. AWS has certifications including ISO, SOC, PCI DSS, HIPAA, and FedRAMP.
Azure offers Azure Security Center, Azure Sentinel for SIEM, and deep integration with Microsoft Defender. Azure supports a large number of regulatory and compliance standards. Azure’s role-based access control and policy definitions are integrated with Microsoft Identity services.
Google offers Identity and Access Management, Cloud Armor for DDoS protection, and confidential VMs for data-in-use encryption. Its Chronicle security analytics and Zero Trust approach are aligned with enterprise security practices. Google also complies with major standards such as GDPR, ISO/IEC, and HIPAA.
All three providers have strong security postures, but each takes a slightly different approach depending on their broader ecosystems. AWS leads in sheer breadth of tools and global compliance. Azure is strong in enterprise-grade identity management. Google is highly focused on secure-by-design infrastructure with simplified controls.
Support and Documentation
Support levels vary by provider, with different pricing tiers:
AWS offers Basic (free), Developer, Business, and Enterprise support plans. These scale based on usage and offer 24/7 support and account managers at higher tiers.
Azure provides Developer, Standard, and Professional Direct support plans. Microsoft also offers Premier Support for enterprises.
Google Cloud has Basic, Role-Based, Enhanced, and Premium Support tiers. Their customer success services provide architectural guidance and operations support.
In terms of documentation, AWS has extensive coverage, followed closely by Azure. Google is known for developer-friendly and concise documentation, especially around APIs and SDKs.
When it comes to performance, scalability, and the surrounding ecosystem, each provider brings something unique to the table.
AWS EC2 stands out with its comprehensive instance offerings, global infrastructure, and mature ecosystem. It’s ideal for businesses that need flexibility, enterprise-grade support, and long-term scalability.
Azure Virtual Machines appeal to businesses embedded in the Microsoft ecosystem. Its seamless integration with Windows Server, hybrid tools, and enterprise apps make it a strong choice for regulated industries and on-prem to cloud migrations.
Google Compute Engine offers excellent price-performance, especially for custom VMs and short-term projects. With robust AI/ML tools and container support, it is favored by startups, data-focused teams, and developers building modern cloud-native applications.
Choosing the right service often comes down to business priorities, existing tech stack, and budget. Companies should evaluate their current infrastructure, performance needs, compliance requirements, and long-term goals before selecting a platform.
Final Thoughts
Choosing between AWS EC2, Azure Virtual Machines, and Google Compute Engine ultimately depends on your specific business needs, technical requirements, budget constraints, and existing infrastructure. All three platforms are powerful and widely adopted, but they each bring unique strengths that make them better suited for different use cases.
AWS EC2 is the most mature and feature-rich option among the three. With the widest selection of instance types, global availability, robust networking, and tight integration with hundreds of services, EC2 excels in flexibility and scalability. It’s particularly well-suited for enterprises that demand performance at scale, complex architectures, or global distribution. Its broad support ecosystem and extensive documentation also make it a reliable choice for long-term infrastructure investment.
Azure Virtual Machines offer a natural fit for organizations that already rely heavily on Microsoft technologies. Its seamless integration with tools like Active Directory, Windows Server, SQL Server, and the broader Power Platform makes it a go-to choice for hybrid environments and enterprise IT departments. Azure also shines in compliance-heavy sectors like finance and healthcare, where regulatory support and identity management are priorities.
Google Compute Engine is a strong competitor, especially for those with developer-heavy teams or cloud-native projects. With advanced capabilities in AI, machine learning, containerization, and data analytics, GCE appeals to startups and tech-focused businesses that prioritize innovation and customization. Its flexible pricing and custom machine types allow for fine-tuned cost control and performance optimization.
When evaluating which provider is right for your business, consider the following:
- Your existing technology stack and how easily it integrates with the cloud provider
- The level of control and customization you require
- The anticipated scale of your infrastructure and how easily the platform can grow with you
- Compliance and regulatory requirements specific to your industry
- Budget constraints and long-term cost management strategies
- Support needs and the availability of technical assistance or managed services
Each of these providers has made significant investments in expanding their capabilities, improving performance, and simplifying management. Ultimately, the best choice may not be just one provider but a multi-cloud approach, allowing you to leverage the unique advantages of each platform where they fit best.
By understanding the differences in performance, pricing, scalability, ecosystem, and support, you can make an informed decision that aligns with your business strategy and technical roadmap. The cloud journey is not only about choosing the right platform—it’s about building an architecture that is agile, efficient, and future-ready.