Microsoft Azure vs. Google Cloud Platform: A Cloud Platform Comparison

Although cloud adoption has been underway for years, only 8% of companies considered themselves highly mature in cloud adoption as of 2024. This rate suggests that many organizations are still struggling with optimizing their cloud strategies.

Selecting the right cloud provider plays a critical role in this process, which makes it one of the most important decisions in shaping cloud development projects.

The three dominant players in the cloud domain are Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Their market positioning is illustrated in the image below.

We’ve already explored the AWS vs. Azure debate, so now it’s time to turn our focus to Microsoft Azure and Google Cloud Platform.

Microsoft Azure is a cloud computing platform that provides a wide range of tools and resources for building, deploying, and managing applications through Microsoft’s global network of data centers. Officially launched in 2010, Azure has become a household name in the digital domain. Microsoft states that more than 95% of Fortune 500 companies trust Azure, among which enterprise leaders like Adobe, BMW, Coca-Cola, FedEx, HP, Intel, and LinkedIn.

Azure operates in over 70 regions globally, which is more than any other cloud provider. Microsoft’s network comprises over 400 data centers, strategically located to ensure excellent availability across various zones.

Originally launched in 2008, Google Cloud Platform (GCP) is a suite of cloud computing services that provides public cloud infrastructure for hosting web-based applications. Depending on their needs, companies from various industries leverage GCP’s AI and ML development services, virtual machines, data warehouses, databases, object storages, and serverless computing systems.

The GCP ecosystem also provides a rich set of tools for developing custom cloud-native applications, as well as services for building advanced analytical and machine learning algorithms.

Some notable clients of GCP from across the world are Spotify, PayPal, Etsy, UPS, Target, Deutsche Bank.

Google Cloud Platform comprises 42 cloud regions and 200-plus network edge locations, providing excellent service availability globally.

Azure and GCP go toe-to-toe when it comes to most business-critical parameters. However, there are still particular areas where one cloud platform outperforms another, which makes them more relevant to specific businesses. Let’s run a Google Cloud vs. Azure comparison across the most critical parameters.

Azure offers a vast and tightly integrated selection of tools for various needs, from Azure AI services like OpenAI, Cognitive Services, and Azure ML, to Power BI and DevOps solutions. Most of these services are ready to use out of the box, with minimal setup and configuration required. In addition, Azure is deeply embedded into the broader Microsoft ecosystem, offering seamless integration with Office 365, GitHub, the Power Platform, and other service suites.

GCP also provides a broad selection of services, particularly those designed for creating complex analytical scripts and ML-powered features. In particular, BigQuery ensures convenient data organization and analytics while Vertex AI and AutoML allow development teams to experiment with ML algorithms. Some other popular tools within the Google Cloud Ecosystem include Cloud Build for CI/CD pipeline development, Terraform for infrastructure provisioning, and Google Kubernetes Service for app containerization. It is essential to note that GCP’s service catalog is modular and often requires integration with open-source and custom components.

As a result, Azure offers a broader range of services that are readily available for quick and seamless integration. Meanwhile, GCP is a better fit for specialized cloud solutions with unique AI-powered features and high-performance requirements.

Azure is a reliable choice for building apps with off-the-shelf AI capabilities that require minor to moderate customization. In particular, Azure-based apps easily integrate with large language models (LLMs) via Azure OpenAI Service. Azure also provides a vast ecosystem of AI building blocks and shows excellent compatibility with ChatGPT.

In contrast, GCP might be a better fit for developing custom ML models and research-grade experimentation. Google provides software engineers and data scientists with a Vertex AI platform, an efficient tool for building, deploying, and scaling custom machine learning models. In addition, developers can use Jupyter Notebook for convenient experimentation with ML algorithms and TensorFlow for scalable model training.

Overall, Azure proves to be an excellent choice for building AI features at scale. Meanwhile, GCP might be a better solution for developing flexible and experimental ML pipelines.

Azure typically has moderate rates for data storage and operations. In addition, it allows teams to save on Windows Server licenses if they are already using the Microsoft ecosystem. Customers can be billed on a pay-as-you-go basis or choose to reserve virtual machines (Azure Reserved Instances) for 1 or 3 years. The latter option allows them to save up to 72% compared to standard on-demand pricing. You can use Azure’s cost calculator for a more detailed pricing estimate specific to your case.

GCP often has higher rates for both data storage and virtual machines compared to Azure. However, Google Cloud offers more sustained-use discounts and temporary offers. GCP users are billed on a pay-as-you-go basis, but there is also an option to choose committed use discounts. In this case, you commit to using a certain amount of vCPU and RAM for 1 or 3 years. The discount is typically 70% for memory-optimized machine types and offers greater flexibility compared to Azure Reserved Instances. By committing to resource amounts instead of specific VM types, you ensure that your savings apply even if your workloads change. For more information on GCP’s pricing, check out its compute engine pricing calculator.

In sum, both GCP and Azure offer their services through the pay-as-you-go model. Cost efficiency depends on your workload type, usage patterns, and commitment level. GCP may be a better choice for short-term, bursty workloads. Meanwhile, Azure is often more cost-effective when it comes to reserved capacity and pure data storage, which is illustrated in the table below.

Azure provides an excellent experience for developers working with the Microsoft stack. Its tight integration with other Microsoft-powered tools, like Visual Studio Code, GitHub, and Azure DevOps can significantly boost development speed. In addition, Azure’s seamless integration with GPT-based services allows development teams to enhance cloud-based solutions with AI functionality in the short term.

GCP, on the other hand, offers a more flexible and developer-centric experience to data scientists and ML engineers. It provides tools and services for experimentation, which makes Google Cloud a convenient option for research and development (R&D) teams and software engineers who experiment with complex analytical scripts.

In sum, Azure is optimized for development productivity and efficient enterprise workflows. Meanwhile, GCP excels in developer experimentation and control.

With a broad range of services tailored for hybrid and enterprise-grade deployments, Azure proves to be a flexible cloud platform with a variety of options for optimization and on-demand scaling. Meanwhile, Azure’s flexibility is primarily structured around Microsoft’s tools and services.

GCP offers greater flexibility for building custom AI pipelines with native Kubernetes and open-source tooling. Similarly to Azure, GCP offers capabilities for fast and efficient on-demand platform scaling.

In summary, both GCP and Azure work well when it comes to scaling or optimizing cloud-based systems, but the flexibility they offer caters to different needs.

Microsoft offers a wide range of Azure features and tools for streamlined management of cloud-based systems. For example:

• Azure Portal, a unified interface for managing Azure resources, monitoring services, and configuring system settings.
• Azure Resource Manager, a service that allows development teams to easily define infrastructures in Azure, organize infrastructure resources, and manage them within different categories.
• Azure Monitor, a comprehensive monitoring solution that provides telemetry data, metrics, logs, and alerts for Azure services and applications.

Azure’s selection of system management services offers built-in governance, compliance, and monitoring features that are especially relevant to large-scale environments.

It is also important to note that your level of involvement in Azure system management depends on the model you choose. In particular, you can choose between an IaaS (Azure Virtual Machine) and PaaS (Azure App Service) service models for custom software development, and ready-to-use SaaS (Dynamics 365).

GCP also provides tools for granular system control. Google offers Cloud Console, a web-based interface that allows software engineers to manage, configure, and monitor Google Cloud Platform services. For managing ML algorithms within GCP-based systems, the team can use Vertex AI.

As has already been mentioned, GCP also provides you with an IaaS (Compute Engine) and PaaS (App engine) options, as well as a ready-to-use SaaS (Google Workspace). The model you choose defines your level of involvement in system management.

Consequently, both Azure and GCP provide excellent levels of system manageability. The two cloud giants deliver a significant range of tools that provide resource optimization, categorization, monitoring, and configuration for all their resources and services.

The table below summarizes the main takeaways from the Azure vs. GCP comparison across the most business-critical areas.

Both Azure and GCP ecosystems are extremely mature and capable of covering nearly all cloud development use cases. However, there are particular situations where one platform outshines another. Below, we discuss use cases where Azure has an advantage over GCP, and where GCP stands out in comparison.

Microsoft Azure can become an ideal solution for a wide range of use cases, especially when it comes to developing large cloud infrastructures. Here are some of the most common scenarios where Azure is the preferred choice for cloud migration or development:

• You need a cloud-based enterprise solution
  Azure is a preferred choice for building an enterprise infrastructure that relies on Microsoft tools like Entra ID, Windows Server, SQL Server, SharePoint, or Microsoft 365. With Azure, businesses can seamlessly extend their on-premises solutions into the cloud with minimal disruption thanks to Azure’s strong tooling for hybrid cloud models. Azure also provides access to Azure Virtual Desktop, Power BI, and Dynamics 365, tools and services that can have significant value to enterprise-level clients. It also works efficiently with .NET, a Microsoft-backed software development framework that is commonly used for enterprise app development.

• You want to build a hybrid cloud infrastructure
  Azure provides a range of services that can be applied for architectures that span on-premises, multi-cloud, and edge environments. For example, Azure Arc, Azure Stack, and ExpressRoute extend Azure services across environments, bring cloud capabilities on-premises, and provide secure private connectivity, respectively. In addition, a hybrid cloud infrastructure based on Azure still supports unified management, governance, and identity across environments. If a business needs to retain some workloads or data on local servers due to security or regulatory reasons while taking advantage of cloud scalability and innovation, Azure is a preferred solution.
• You prioritize compliance and efficient platform governance
  Azure is a great choice for organizations that operate in highly regulated industries like healthcare, finance, government, and manufacturing. Microsoft’s cloud platform holds a solid set of compliance certifications, such as ISO, GDPR, HIPAA, FedRAMP, etc. With advanced security tools, such as Azure Policy, Blueprints, Security сenter, and сost management, Azure offers a solid level of control and governance over the cloud-based infrastructure.

As has already been mentioned, GCP may be an excellent choice for businesses that prioritize innovation and experimentation. Let’s take a closer look at ideal scenarios for using GCP:

• You put a significant emphasis on data analytics
  Built with data-first architecture, GCP fits businesses that require advanced analytics. With services like BigQuery, Dataflow, Pub/Sub, and Looker, companies that run GCP-based platforms get excellent possibilities for processing, analyzing, and visualizing massive datasets efficiently. Add to that GCP’s advanced capabilities for implementing AI-powered analytics, and it becomes a preferred platform for building innovative, data-driven solutions.
• You want to drive innovation with a limited budget
  GCP is often appealing to startups and engineering teams that seek cost-effective cloud pricing models. Google Cloud offers a comprehensive set of options for experimentation, automatic sustained use discounts, and committed use contracts. In addition, GCP offers great possibilities for fast development of cloud-native infrastructures with tools like Cloud Run, Cloud Functions, and Firebase. This is especially appealing for experimental and innovative software built primarily for demonstration purposes, where wide-scale outreach isn’t a priority.

Many organizations adopt a multi-cloud strategy by combining GCP and Azure services. For example, a company can rely on an Azure infrastructure for enterprise integration while using GCP features for data analytics. In particular cases Azure- and GCP-based solutions can be efficiently connected, while their combination allows businesses to avoid vendor lock-in.

Both Azure and GCP are powerful contenders in the cloud development domain. Each of the platforms performs well in the most critical business parameters. Azure may be a better fit for enterprise-grade solutions, hybrid infrastructures, and systems that deal with strict compliance regulations. Meanwhile, GCP may have a slight edge when it comes to advanced analytics and innovation with a limited budget.

Regardless of the cloud platform you choose, Leobit is ready to help. With over 60 cloud projects delivered and recognition as a Clutch Top Azure Company 2025 and Top GCP Company 2024, we’re fully committed to helping you embrace the full potential of modern cloud technologies.

Our specialists are especially skilled in Azure services, as we are a Microsoft Solutions Partner for Digital App and Innovation and have experience from over 45 successful Azure projects. Whether you need to build a cloud infrastructure, modernize your legacy system, migrate to Azure, or implement Azure AI services in your software, we are ready to help.

Contact us to start your cloud transformation journey with us.