2018 Cloud Battle: AWS vs Microsoft Azure vs Google Cloud Platform

Cloud computing has fundamentally changed the way organizations approach information technology. Rather than relying exclusively on physical data centers and on-premises servers, companies can now access vast pools of computing resources on demand, paying only for what they use. This model offers unprecedented flexibility, scalability, and efficiency, enabling rapid innovation and cost savings. By 2018, the cloud market was dominated by three major providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Each offers unique strengths and caters to different segments of the market, creating a competitive and dynamic landscape.

Understanding Cloud Computing

At its core, cloud computing is about delivering computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the internet (“the cloud”). This eliminates the need for organizations to own or manage physical infrastructure, instead relying on cloud providers to handle hardware maintenance, upgrades, and scalability. Cloud services are generally categorized into three main types: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

IaaS provides virtualized computing resources such as virtual machines and storage.
PaaS offers a development platform that abstracts infrastructure management.
SaaS delivers fully managed software applications accessible via a web browser.

Cloud computing’s advantages include reduced capital expenditure, increased speed of deployment, global accessibility, and the ability to scale resources in real time based on demand. However, the choice of cloud provider affects the cost structure, available features, security posture, and performance.

The Rise of the Big Three Cloud Providers

By 2018, the market was largely led by AWS, Azure, and Google Cloud Platform. While AWS had a significant early mover advantage and maintained market dominance, Azure and Google Cloud rapidly expanded their offerings and infrastructure, providing strong alternatives for organizations with specific needs.

Amazon Web Services (AWS)

AWS, launched in 2006, pioneered the public cloud computing market. Over the years, it developed an extensive portfolio of cloud services that cover nearly every aspect of computing. AWS provides compute power through services like Elastic Compute Cloud (EC2), scalable storage with Simple Storage Service (S3), and database services such as Amazon RDS for relational databases and DynamoDB for NoSQL. AWS also offers advanced capabilities in analytics, machine learning, Internet of Things (IoT), security, and developer tools.

AWS’s infrastructure is built across numerous regions worldwide, each containing multiple availability zones. This design enhances fault tolerance, disaster recovery, and data sovereignty compliance. AWS’s extensive partner ecosystem and community support add further value for customers, offering third-party integrations, consulting, and managed services.

Microsoft Azure

Microsoft Azure entered the cloud scene later than AWS but quickly gained traction, especially among enterprises with existing Microsoft investments. Azure integrates seamlessly with popular Microsoft software such as Windows Server, SQL Server, Office 365, and Active Directory. This tight integration allows businesses to extend their on-premises data centers into the cloud through hybrid deployments.

Azure offers a broad set of cloud services, including Virtual Machines for compute, Blob Storage for unstructured data, SQL Database for managed relational data, and Cosmos DB for globally distributed NoSQL. Azure also focuses on hybrid cloud and edge computing scenarios, providing tools to bridge cloud and local environments.

Compliance and security are strong points for Azure, with Microsoft investing heavily in certifications and enterprise-grade security features. Its developer tools, including Visual Studio integration and Azure DevOps, enable efficient continuous integration and delivery pipelines.

Google Cloud Platform (GCP)

Google Cloud Platform, while younger than AWS and Azure, capitalizes on Google’s expertise in data management, machine learning, and global networking infrastructure. GCP emphasizes cutting-edge services like BigQuery, a fully managed data warehouse for real-time analytics, and Google Kubernetes Engine (GKE), a leading container orchestration platform.

Google Cloud’s strengths lie in data analytics, artificial intelligence, and open-source technologies. It supports developers with tools such as TensorFlow for machine learning and Cloud Functions for serverless computing. Google’s network backbone ensures low latency and high throughput, benefiting global enterprises and startups alike.

Although GCP’s market share was smaller than AWS and Azure in 2018, its rapid innovation and focus on modern application architectures attracted developers looking to leverage cloud-native technologies.

Comparing Market Presence and Growth in 2018

AWS dominated the cloud infrastructure market in 2018, holding a significant portion of global cloud revenue. Its extensive service catalog, mature ecosystem, and global infrastructure made it the default choice for many organizations.

Azure experienced rapid growth, becoming a major contender due to its enterprise focus and hybrid cloud capabilities. Many businesses that already use Microsoft products found Azure a natural fit, helping Microsoft close the gap with AWS in the cloud race.

Google Cloud Platform positioned itself as the cloud for data-centric and AI-driven workloads. Its investments in data analytics and machine learning differentiated it, even as it expanded its infrastructure and global presence.

Despite these differences, all three providers continually expanded their data center locations and introduced new services in 2018 to meet increasing customer demand and capture more market share.

Key Cloud Services and Use Cases in 2018

The three providers offered a wide array of services targeting diverse use cases, from simple website hosting to complex machine learning pipelines.

Compute Services: AWS EC2, Azure Virtual Machines, and Google Compute Engine provide scalable virtual servers for various workloads. Each supported multiple operating systems, custom configurations, and autoscaling capabilities.
Storage Services: Object storage for unstructured data was handled by AWS S3, Azure Blob Storage, and Google Cloud Storage, offering high durability and availability.
Databases: AWS offered Amazon RDS and DynamoDB, Azure provided SQL Database and Cosmos DB, and Google Cloud supported Cloud SQL and Firestore. Each platform supported relational and NoSQL databases with managed scaling.
Networking: Virtual Private Clouds (VPCs) were available in all three, allowing customers to define isolated networks with subnets, firewalls, and routing controls.
Machine Learning: AWS SageMaker, Azure Cognitive Services, and Google AI Platform enabled developers to build intelligent applications using pre-built APIs and custom models.
Serverless Computing: AWS Lambda, Azure Functions, and Google Cloud Functions offered event-driven, on-demand compute without server management.

These services enabled startups to scale rapidly, enterprises to modernize infrastructure, and developers to build innovative applications faster than ever before.

Pricing Models and Cost Management

Cloud pricing can be complicated due to variable usage patterns and numerous service options. All three providers offered pay-as-you-go pricing, but their discount models and billing increments varied.

AWS introduced Reserved Instances for customers willing to commit to one or three years, lowering costs in exchange for upfront commitment. Spot Instances allowed users to bid for spare capacity at reduced rates, suitable for flexible workloads.

Azure’s pricing was competitive for Windows workloads, often favored by enterprises with Microsoft licensing agreements. Azure also supported hybrid use benefits, enabling license portability from on-premises to the cloud.

Google Cloud stood out with sustained use discounts, automatically reducing costs as virtual machines ran longer within a billing cycle. Custom machine types allowed precise resource allocation, helping avoid paying for unused capacity.

Understanding these pricing nuances was essential for businesses to optimize cloud spending.

Security and Compliance Landscape

Security concerns often top the list of cloud adoption barriers. AWS, Azure, and Google Cloud all invested heavily in securing their infrastructure and providing tools for customer security.

Identity and Access Management (IAM) allowed detailed control over user permissions. Encryption was offered both in transit and at rest. Continuous monitoring and threat detection tools helped identify and mitigate risks.

Compliance with standards like HIPAA, GDPR, SOC 2, and FedRAMP ensured that regulated industries could trust these platforms for sensitive data workloads.

Shared responsibility models clarified which security aspects the provider managed and which remained the customer’s duty.

By 2018, the cloud market was no longer about simply choosing the biggest player but about selecting the platform best aligned with an organization’s technology stack, business goals, and workload needs.

AWS offered the broadest service portfolio and global reach, making it ideal for enterprises seeking mature and comprehensive cloud infrastructure.

Microsoft Azure’s hybrid cloud capabilities and seamless integration with Microsoft products made it attractive to companies with existing Microsoft investments.

Google Cloud Platform’s strengths in data analytics, AI, and modern containerized applications appealed to organizations focused on innovation and data-driven solutions.

Understanding these platforms’ capabilities, pricing models, and security postures was vital for businesses navigating the cloud computing landscape in 2018. The following parts of this series will explore these aspects in greater detail, helping readers make informed decisions in their cloud journey.

Core Infrastructure Services Comparison – Compute, Storage, and Networking

When choosing a cloud provider, the foundational building blocks — compute, storage, and networking — play a crucial role in determining the overall performance, scalability, and cost-effectiveness of cloud solutions. By 2018, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) each offered mature and robust infrastructure services but with unique features and architectural differences that shaped user experiences. In this section, we dive deep into how these providers delivered compute power, managed data storage, and enabled secure and scalable networking.

Compute Services: Virtual Machines and Containerization

Compute services are the backbone of cloud infrastructure, allowing organizations to deploy applications, run workloads, and handle traffic demands dynamically.

AWS Elastic Compute Cloud (EC2)

AWS’s EC2 was among the earliest and most widely adopted Infrastructure as a Service (IaaS) offerings. EC2 instances could be launched in a variety of configurations, supporting multiple CPU architectures, memory sizes, and storage options. AWS offered predefined instance types optimized for general-purpose, compute-intensive, memory-intensive, storage-optimized, and GPU-accelerated workloads.

In 2018, EC2 introduced features like Elastic Network Adapters for high bandwidth and lower latency networking and bare-metal instances for customers needing direct access to hardware. Autoscaling groups allowed applications to scale in or out automatically based on traffic, improving cost-efficiency.

AWS also supported container orchestration through Amazon Elastic Container Service (ECS) and had begun promoting Amazon Elastic Kubernetes Service (EKS), responding to growing demand for container-native architectures.

Microsoft Azure Virtual Machines

Azure Virtual Machines (VMs) competed closely with EC2 by offering a wide range of instance sizes and types. Azure distinguished itself by providing Windows Server VMs with seamless integration for Microsoft workloads, along with Linux distributions for open-source environments.

Azure focused heavily on hybrid scenarios by enabling Azure Stack, allowing customers to run Azure-consistent services from on-premises data centers, which was critical for enterprises with compliance or latency constraints.

Autoscaling and load balancing were built-in features, and Azure’s Marketplace offered numerous pre-configured VM images for rapid deployment.

On the container front, Azure Kubernetes Service (AKS) was introduced to manage Kubernetes clusters easily, alongside Azure Container Instances for serverless container execution.

Google Compute Engine (GCE)

Google Compute Engine emphasized performance, scalability, and innovation. GCE supported custom machine types, allowing users to specify the exact number of virtual CPUs and amount of RAM needed, avoiding the overhead of unused resources.

Google’s global network and live migration of VMs were standout features, reducing downtime during maintenance. Autoscaling and load balancing support fault-tolerant applications.

Google Kubernetes Engine (GKE) was a flagship service, benefiting from Google’s deep involvement with Kubernetes development. GKE provided automatic upgrades, scaling, and a robust security model, making it the go-to choice for containerized applications.

GCE also introduced preemptible VMs, low-cost instances suitable for fault-tolerant batch jobs, similar to AWS Spot Instances.

Storage Solutions: Object, Block, and File Storage

Data storage in the cloud needs to address various needs, from simple file storage to highly performant block storage and scalable object stores.

AWS Storage Services

AWS’s Simple Storage Service (S3) was a pioneering object storage service offering high durability, availability, and scalability. It became the industry standard for storing unstructured data such as backups, media files, and archives. S3’s flexible storage classes, including Standard, Infrequent Access, and Glacier, allowed cost optimization based on access patterns.

For block storage, Amazon Elastic Block Store (EBS) provides persistent storage volumes for EC2 instances with different performance tiers, such as SSD-backed General Purpose and Provisioned IOPS volumes.

AWS also offered Amazon Elastic File System (EFS), a scalable, elastic file storage service designed for Linux-based workloads requiring shared access.

Microsoft Azure Storage

Azure Blob Storage served as Azure’s object storage solution, capable of storing massive amounts of unstructured data. Azure introduced cool and archive tiers for long-term storage at reduced costs.

Azure Disk Storage provides managed block storage for virtual machines, with options for Standard HDD, Standard SSD, and Premium SSD, supporting high IOPS and low latency.

For shared file storage, Azure Files enables fully managed file shares accessible via the SMB protocol, supporting lift-and-shift migrations of legacy applications.

Azure emphasized integration with its hybrid cloud solutions, allowing seamless data replication and backup between on-premises and cloud environments.

Google Cloud Storage

Google Cloud Storage competed with S3 and Blob Storage by offering a unified object storage service with multiple classes: Standard, Nearline, Coldline, and Archive, catering to various latency and access frequency requirements.

Persistent Disks acted as block storage for Google Compute Engine VMs, supporting SSD and standard HDD types with strong performance guarantees.

For shared file storage, Google provided Filestore, designed for high-performance workloads requiring Network File System (NFS) access.

Google’s data storage was tightly integrated with its analytics and AI services, making it easier for users to build intelligent applications on top of stored data.

Networking: Virtual Networks, Load Balancers, and Security

Networking underpins cloud architecture, enabling communication between resources, securing workloads, and delivering traffic efficiently.

AWS Networking

AWS Virtual Private Cloud (VPC) gave users control over their network topology, including IP ranges, subnets, route tables, and gateways. Customers could create isolated environments with private subnets and public-facing components.

Elastic Load Balancers (ELB) distribute incoming traffic across EC2 instances, supporting Classic Load Balancer, Application Load Balancer, and Network Load Balancer, each optimized for different types of traffic and workloads.

AWS provided extensive security tools like Security Groups (stateful firewalls) and Network ACLs (stateless firewalls) to control inbound and outbound traffic.

AWS Direct Connect allowed private, dedicated network connections from customer data centers to AWS, improving bandwidth and security.

Azure Networking

Azure Virtual Network (VNet) parallels AWS VPC, providing logically isolated networks. VNets supported subnets, network security groups for traffic filtering, and route tables.

Azure Load Balancer and Application Gateway deliver availability and web application firewall capabilities.

Azure ExpressRoute enables private connections between on-premises networks and Azure data centers, bypassing the public internet.

Azure’s hybrid cloud networking also includes VPN Gateway and Azure Firewall, enabling complex, secure architectures combining cloud and on-premises resources.

Google Cloud Networking

Google Cloud’s Virtual Private Cloud (VPC) stood out with its global, scalable, and flexible design. Unlike regional VPCs from competitors, Google’s VPC spans regions, allowing resources across multiple regions to connect privately.

Cloud Load Balancing offered global HTTP(S) load balancing with intelligent routing, as well as TCP/SSL proxy load balancing.

Google’s network infrastructure was considered among the fastest and most reliable globally, leveraging private fiber optic cables and edge caching.

Cloud VPN and Dedicated Interconnect provide secure connectivity options for hybrid deployments.

Google’s emphasis on network performance benefited latency-sensitive applications and global services.

Performance and Availability

All three cloud providers are committed to high availability and fault tolerance. AWS Availability Zones, Azure Availability Sets, and Google’s regional resource deployment ensured that failures in one data center did not disrupt applications.

Each platform guaranteed uptime through service-level agreements (SLAs), often targeting 99.9% or higher availability for critical services.

Performance benchmarks varied based on workload type and geographic location, but users reported strong results across all providers, with Google often excelling in network speed, AWS in breadth of services, and Azure in hybrid cloud scenarios.

Cost Considerations

Pricing for core infrastructure services could vary significantly based on instance types, storage classes, and data transfer volumes.

AWS’s broad range of instance types and pricing options gave customers flexibility but also complexity in cost optimization.

Azure’s licensing advantages for Windows Server and SQL Server workloads helped reduce costs for enterprises invested in Microsoft technologies.

Google’s custom machine types and automatic sustained use discounts simplified pricing and reduced costs for steady workloads.

Efficient cloud architects often use cost calculators and monitoring tools provided by these platforms to balance performance needs with budget constraints.

In 2018, AWS, Azure, and Google Cloud Platform each delivered strong compute, storage, and networking foundations critical for modern cloud applications. AWS led with maturity, service variety, and global reach. Azure provided unparalleled hybrid cloud integration and Microsoft-centric solutions. Google Cloud excels in network performance, container orchestration, and data-centric applications.

Understanding the subtle distinctions in infrastructure services helped organizations tailor their cloud strategies for performance, scalability, and cost-effectiveness.

In the next part of this series, we will explore advanced cloud services, including databases, machine learning, security, and management tools, to provide a more comprehensive comparison.

Advanced Cloud Services – Databases, Machine Learning, Security, and Management Tools

As cloud platforms matured by 2018, the competition moved beyond basic infrastructure into advanced services that enable developers and enterprises to build intelligent, secure, and scalable applications faster. AWS, Microsoft Azure, and Google Cloud Platform (GCP) each expanded their portfolios to include managed database options, artificial intelligence capabilities, security services, and cloud management tools. These advanced offerings influenced platform choice based on specific business needs, technical preferences, and ecosystem alignment.

Managed Database Services

Databases remain a core component of cloud applications, and managed services allow users to avoid the operational complexity of installation, scaling, and patching.

AWS Database Portfolio

Amazon Web Services offered a broad spectrum of managed database services covering relational, NoSQL, and data warehousing needs. Amazon Relational Database Service (RDS) supports popular engines like MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server, allowing users to deploy scalable relational databases without administrative overhead.

Amazon Aurora, introduced in previous years, was a high-performance, MySQL- and PostgreSQL-compatible relational database engine designed for the cloud. It provided fault-tolerant storage and replicated across multiple availability zones, delivering improved performance and availability.

For NoSQL, Amazon DynamoDB provided a fully managed, key-value and document database with single-digit millisecond latency, scaling automatically to meet traffic demands.

Amazon Redshift served as the cloud data warehouse solution optimized for large-scale analytic workloads, with fast query performance through columnar storage and massively parallel processing.

Microsoft Azure Database Services

Azure offered multiple managed database services aligned closely with Microsoft’s product ecosystem. Azure SQL Database was the flagship service for relational data, providing a scalable, fully managed version of SQL Server with built-in high availability and automated backups.

Azure also supported MySQL and PostgreSQL as managed services, broadening support for open-source databases.

Cosmos DB was a globally distributed, multi-model NoSQL database supporting document, key-value, graph, and column-family data models. Cosmos DB guarantees single-digit millisecond latencies and comprehensive SLAs covering availability, throughput, consistency, and latency, making it attractive for mission-critical applications.

Azure Data Warehouse (now known as Azure Synapse Analytics) provided cloud-scale analytics similar to Redshift, with integration to Azure Machine Learning and Power BI.

Google Cloud Database Services

Google Cloud Platform offered Cloud SQL for managed MySQL and PostgreSQL, with high availability options and automated backups.

Cloud Spanner was GCP’s flagship database service in 2018, a globally distributed, strongly consistent relational database capable of horizontal scaling, combining traditional relational features with NoSQL scalability — a unique proposition in the market.

Cloud Bigtable provided a managed NoSQL wide-column database suitable for analytics and operational workloads, inspired by Google’s internal Bigtable technology.

BigQuery stood out as a serverless, highly scalable data warehouse optimized for running SQL queries on massive datasets, known for fast performance and integration with Google’s data analytics tools.

Machine Learning and Artificial Intelligence

By 2018, cloud providers aggressively integrated AI and machine learning capabilities to empower users to build smarter applications without deep expertise in data science.

AWS Machine Learning

AWS offered a variety of AI and ML services, including Amazon SageMaker, which allowed developers to build, train, and deploy machine learning models quickly with managed infrastructure.

Other AI services included Amazon Rekognition for image and video analysis, Amazon Lex for conversational interfaces, Amazon Polly for text-to-speech, and Amazon Comprehend for natural language processing.

AWS’s AI offerings are integrated with its data storage and compute services, enabling end-to-end machine learning workflows in the cloud.

Microsoft Azure AI and Machine Learning

Azure Machine Learning Studio provided a drag-and-drop interface to build predictive models without writing code, appealing to business analysts and developers alike.

Azure Cognitive Services includes APIs for vision, speech, language understanding, and decision-making, enabling developers to add AI features such as face recognition, language translation, sentiment analysis, and anomaly detection.

Integration with Microsoft’s development tools and the broader Azure ecosystem enhanced productivity and simplified deployment.

Google Cloud AI

Google Cloud leveraged its leadership in AI research and open-source frameworks like TensorFlow to provide comprehensive machine learning services.

Cloud AutoML enables users with limited ML expertise to train high-quality custom models for vision, language, and translation.

Google Cloud also offered pre-trained APIs like Vision API, Speech-to-Text, Natural Language API, and Translation API.

BigQuery ML allowed data analysts to build and execute machine learning models directly in BigQuery using SQL, bridging data analytics and machine learning seamlessly.

Security and Identity Management

Security remained a top priority as cloud adoption increased, with providers enhancing identity management, encryption, and threat detection capabilities.

AWS Security Services

AWS Identity and Access Management (IAM) provides granular access controls, enabling administrators to define permissions at the user, group, or role level.

AWS Key Management Service (KMS) simplifies encryption key creation and management, integrated across AWS services.

AWS CloudTrail enables logging and monitoring of API calls for auditing and compliance.

AWS Shield offered protection against Distributed Denial of Service (DDoS) attacks, and AWS WAF (Web Application Firewall) helped filter malicious web traffic.

Security Hub, introduced later but worth noting, consolidated security findings across AWS accounts.

Azure Security and Identity

Azure Active Directory (Azure AD) was a core identity and access management solution supporting single sign-on, multi-factor authentication, and integration with on-premises Active Directory.

Azure Security Center provides unified security management and threat protection across Azure and hybrid environments.

Azure Key Vault securely stores encryption keys, secrets, and certificates.

Azure DDoS Protection and Azure Firewall helped protect applications from network threats.

Google Cloud Security

Google Cloud IAM enables fine-grained access control with roles and permissions.

Cloud Security Command Center offered centralized security and risk management, identifying vulnerabilities and threats.

Google’s Data Loss Prevention API helped classify and protect sensitive data.

Cloud KMS managed encryption keys, and Shielded VMs provided enhanced protections against rootkits and bootkits.

Google’s global infrastructure benefited from secure design principles and real-time threat detection.

Cloud Management and Monitoring Tools

Effective cloud management requires visibility, automation, and governance tools to monitor resources, optimize costs, and enforce policies.

AWS Management

AWS CloudWatch provides monitoring for resources and applications, enabling alerts based on performance metrics.

AWS CloudFormation allowed infrastructure as code, automating resource provisioning and configuration.

AWS Config tracks resource inventory and configuration changes for compliance auditing.

Azure Management

Azure Monitor offered comprehensive telemetry and diagnostics across Azure resources.

Azure Resource Manager enabled declarative resource deployment with templates.

Azure Automation and Azure Policy allowed operational automation and governance enforcement.

Google Cloud Management

Google Stackdriver, later integrated into Google Cloud Operations, provided monitoring, logging, and diagnostics.

Deployment Manager enabled infrastructure as code for consistent resource management.

Cloud Billing API allowed tracking and managing costs programmatically.

In 2018, AWS, Microsoft Azure, and Google Cloud Platform made significant advances in managed databases, artificial intelligence, security, and management services, reflecting their unique strengths and strategic focuses.

AWS’s broad database portfolio and machine learning tools appealed to enterprises needing flexible, mature solutions. Azure leveraged its integration with Microsoft products and strong identity management to attract corporate customers. Google Cloud pushed innovation in global-scale databases, AI services, and developer-friendly management tools.

Choosing the right platform depended on workload requirements, existing technology stacks, and future cloud strategy.

The final part of this series will explore pricing models, customer support, and overall ecosystem maturity to help organizations make informed cloud decisions.

Pricing, Customer Support, and Ecosystem Maturity

Selecting a cloud provider in 2018 required more than just comparing technical features. The cost-effectiveness of services, quality of customer support, and maturity of the overall ecosystem played critical roles in decision-making. Organizations needed transparent and flexible pricing models, dependable support channels, and robust partner networks to ensure successful cloud adoption and long-term value.

Cloud Pricing Models and Cost Management

Cloud pricing is complex due to the wide variety of services, resource configurations, and usage patterns. Each major provider developed its pricing schemes, discounts, and billing options to attract different customer segments.

AWS Pricing Approach

Amazon Web Services offered a pay-as-you-go model, charging per second or hour depending on the service. For compute resources such as EC2 instances, customers could choose On-Demand pricing for maximum flexibility, Reserved Instances for cost savings on long-term commitments, and Spot Instances for surplus capacity at reduced prices.

AWS also provided volume discounts and Savings Plans that offered discounts based on committed usage over a one- or three-year period.

For storage and database services, pricing depends on the amount of data stored, data transfer, and I/O requests.

AWS Cost Explorer and Trusted Advisor helped users analyze and optimize spending, identifying underutilized resources and recommending cost-saving actions.

Azure Pricing Approach

Microsoft Azure’s pricing was competitive and flexible, supporting pay-as-you-go, reserved capacity, and spot pricing similar to AWS.

Azure offered a pricing calculator and cost management tools to forecast expenses and monitor budgets. The Hybrid Use Benefit allowed customers with existing Windows Server licenses to reduce compute costs by applying those licenses in the cloud.

Azure also introduced Enterprise Agreements and volume licensing to provide discounts to large organizations.

Pricing transparency was generally clear, but differences in resource measurement units sometimes complicated direct comparisons.

Google Cloud Pricing Approach

Google Cloud Platform emphasized simplicity and automatic cost savings. GCP used per-second billing for compute resources, which was a competitive advantage at the time.

Sustained Use Discounts automatically reduce prices for instances running a significant portion of the month without requiring upfront commitments, lowering the barrier to cost efficiency.

Committed Use Contracts offered additional discounts for customers willing to commit for one or three years.

Google Cloud’s pricing calculator and billing reports helped users manage their budgets.

Data transfer and storage costs were competitive, and Google’s network infrastructure promised savings for data-intensive applications.

Customer Support and Service Level Agreements (SLAs)

Reliable customer support and clear service guarantees were essential to enterprise confidence in cloud adoption.

AWS Support

AWS offered multiple support tiers: Basic (free), Developer, Business, and Enterprise, with increasing levels of access to technical support engineers, faster response times, and architecture guidance.

The Enterprise tier included 24/7 phone support, access to a Technical Account Manager (TAM), and proactive support for critical workloads.

AWS provided detailed SLAs for core services, typically guaranteeing 99.9% to 99.99% availability depending on the resource type.

Extensive documentation, training programs, and community forums supplemented official support.

Azure Support

Microsoft Azure’s support options ranged from Basic (free) to Developer, Standard, Professional Direct, and Premier, with tailored response times and advisory services.

Premier support offered the highest level of engagement, including dedicated account management and rapid incident resolution.

Azure SLAs promised 99.9% or higher uptime for key services, and Microsoft regularly updated customers on service health.

Azure also invested heavily in training through Microsoft Learn, certifications, and community events.

Google Cloud Support

Google Cloud provided Basic, Development, Production, and Enterprise support plans, each with defined response targets and access to technical specialists.

The Enterprise plan included 24/7 support with rapid escalation and technical account management.

Google’s SLAs offered 99.95% availability for Compute Engine and other major services.

Comprehensive documentation, tutorials, and active community engagement helped customers troubleshoot independently.

Ecosystem Maturity and Partner Networks

The maturity of the cloud ecosystem included not only the breadth of services but also third-party integrations, developer communities, and partner networks.

AWS Ecosystem

By 2018, AWS had the largest market share and a vast ecosystem with thousands of technology partners, system integrators, and independent software vendors.

The AWS Marketplace offered a wide range of software and services ready for deployment, easing integration challenges.

A large developer community and numerous certifications created a rich talent pool and knowledge base.

AWS continuously expanded services and regions to meet global demand.

Azure Ecosystem

Azure benefited from strong ties to existing Microsoft customers and technology partners.

Integration with Windows Server, SQL Server, Office 365, and Dynamics 365 made Azure attractive for enterprises invested in Microsoft technologies.

The Azure Marketplace featured numerous third-party applications and solutions, supported by a growing network of system integrators.

Microsoft’s strong presence in hybrid cloud scenarios further differentiated Azure’s ecosystem.

Google Cloud Ecosystem

Google Cloud’s ecosystem was rapidly evolving, with a focus on open-source technologies and data analytics partners.

Strong collaborations with startups and enterprises in AI and big data created a specialized ecosystem.

The Google Cloud Marketplace offered curated software and services, but was smaller compared to AWS and Azure.

Google’s reputation for innovation attracted developers, especially those interested in Kubernetes, containers, and machine learning.

In 2018, the choice between AWS, Microsoft Azure, and Google Cloud Platform extended beyond raw technical features to include pricing strategies, support quality, and ecosystem maturity. AWS’s extensive global presence, vast partner network, and flexible pricing made it the default for many enterprises. Azure’s seamless integration with Microsoft products and strong hybrid capabilities appealed to organizations with existing Microsoft investments. Google Cloud’s competitive pricing, cutting-edge AI tools, and open-source focus attracted innovators and data-centric companies.

Organizations needed to carefully evaluate these factors aligned with their business goals, workloads, and long-term cloud strategies to select the platform best suited to their needs.

Final Thoughts:

The cloud computing market in 2018 was a fiercely competitive space dominated by three giants: Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Each brought unique strengths, differentiated service portfolios, and strategic advantages tailored to varied customer needs.

AWS continued to lead with its unmatched scale, mature ecosystem, and broad service offering. Its comprehensive global infrastructure and flexible pricing models made it the go-to choice for enterprises requiring reliability, diversity of tools, and extensive third-party support.

Azure leveraged its deep enterprise relationships and seamless integration with Microsoft software, providing a compelling proposition for organizations invested in Windows environments. Its hybrid cloud capabilities addressed real-world challenges, making it attractive for businesses in transition or with complex compliance requirements.

Google Cloud, while smaller in market share, positioned itself as a forward-thinking platform with cutting-edge data analytics, machine learning tools, and developer-friendly innovations. Its automatic discounts and simple pricing structures appealed to startups and data-driven companies looking to leverage AI and open-source technologies.

When choosing a cloud provider in 2018, businesses had to carefully balance technical requirements, cost considerations, support expectations, and long-term ecosystem benefits. The ideal choice depended heavily on the specific workloads, existing IT investments, and strategic goals.

Looking beyond 2018, the cloud market’s evolution emphasized hybrid architectures, multi-cloud strategies, and specialized services. The competition between AWS, Azure, and Google Cloud drove innovation and pushed providers to continually enhance security, scalability, and user experience.

Ultimately, understanding each provider’s unique offerings and aligning them with organizational needs remains key to unlocking the full potential of cloud computing.

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Tags: 2018 Cloud Battle, aws, cloud, google, microsoft azure, Platform