VMware Engine
Running VMware workloads on Google Cloud: vSphere migration, hybrid scenarios, and data center expansion.
Brief description
Google Cloud offers a wide range of powerful software solutions for businesses and developers. With scalable infrastructure, data analytics tools, and machine learning capabilities, Google Cloud enables organizations to unlock the full potential of cloud technologies. Whether it's application management, data storage and analysis, or creating innovative solutions based on artificial intelligence, Google Cloud provides the tools and services to accelerate digital transformation and business growth.
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Running VMware workloads on Google Cloud: vSphere migration, hybrid scenarios, and data center expansion.
GCP Unified Security Management Center: visibility of risks, posture, alerts, and recommendations.
Cybersecurity expertise and services: incident response, threat intelligence, exposure management, and consulting.
A set of cloud services for teamwork and communication: email, documents, storage, meetings, and administration in one account.
Threat platform: collection, correlation, and analysis of cyber threats for SOC and response.
Managed Kubernetes: container launch, autoscaling, updates, and security for microservices.
Object storage for files and data: storage classes, access policies, versions, and integration with analytics.
Network and connectivity: VPC, load balancing, VPN/Interconnect, CDN, and security for cloud infrastructure.
Managed databases: SQL/NoSQL, migrations, replication, and high availability for applications.
Virtual machines in GCP: a wide range of types, scaling, images, and integrations for infrastructure.
Google Cloud AI models and assistants for generating, analyzing, and automating tasks in applications and data.
Hybrid/peripheral cloud: management of clusters and services outside GCP for edge and on-prem scenarios.
ML tools in GCP: training, deployment, and MLOps for models, including AutoML and pipelines.
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Google Cloud Platform is a suite of Google’s cloud services for computing, data storage, databases, analytics, Kubernetes and AI/ML. The platform comprises over 150 products and operates across 43 regions and 130 zones. A company can deploy virtual machines via Compute Engine, store files in Cloud Storage, analyse data in BigQuery and create ML models in Vertex AI. GCP is suitable for both individual applications and enterprise infrastructure.
The main difference lies in the range of services and technological focus. AWS holds a significant share of the global cloud infrastructure market, whilst Google Cloud is closely associated with Kubernetes, BigQuery and Vertex AI. If a team is building a containerised architecture, processing large datasets or planning ML projects, GCP is worth including in the comparison. For other companies, the deciding factors may be the AWS services they are already using, region requirements or the cost of a specific architecture.
Google Workspace is an enterprise suite comprising Gmail, Drive, Docs, Sheets, Meet, Calendar and administration tools. Unlike personal Gmail, a company uses its own domain and centrally manages users, access and policies via the Admin Console. Depending on the version, different storage capacities and features are available, including Vault. For services included in Workspace, Google guarantees an SLA of at least 99.9% monthly uptime.
Yes, Google Cloud is suitable for computational workloads, storing large files, production data analytics, containerised applications and machine learning. For example, Compute Engine GPU instances can handle specific computing or visualisation tasks, whilst BigQuery can collect historical equipment performance data. Vertex AI is used for computer vision and predictive maintenance. The specific approach depends on CAD/PLM systems, software licensing, data requirements and the scale of computations.
Start by describing your current infrastructure, applications, data volume and the tasks you need to migrate or run in the cloud. GEO-MENTOR can design the architecture, prepare the migration, and set up the container environment, CI/CD and enterprise services. If your organisation is already using AWS or on-premises servers, you may wish to consider a hybrid approach without a complete infrastructure replacement. This approach helps you understand where Google Cloud offers practical benefits and which services your project requires.
Google Cloud Platform (GCP) is Google’s cloud platform for computing, data storage, analytics, containers and machine learning. The platform’s infrastructure currently spans 43 regions and 130 availability zones, and Google’s share of the global cloud infrastructure market in the second quarter of 2026 stands at around 15 per cent. By way of comparison, AWS’s share stood at around 28 per cent, whilst Microsoft’s was 20 per cent.
One of GCP’s key differentiators is its leadership in analytics and ML/AI technologies: BigQuery is used to process large datasets, TensorFlow and Vertex AI for developing and deploying models, whilst Kubernetes automates the deployment, scaling and management of containerised applications. Kubernetes was originally developed at Google and later transferred to the Cloud Native Computing Foundation as an open-source project. The platform’s solutions are used by major companies such as Spotify, PayPal, Deutsche Bank and Twitter (X).
Google Cloud brings together services for computing, data storage, analytics, machine learning and network infrastructure. Engineering teams can use the platform to run applications and compute tasks, work with large datasets, build ML models and organise secure access to cloud resources. This suite enables the creation of infrastructure tailored to specific business processes without being tied to a single type of workload.
Compute Engine provides virtual machines and is comparable in purpose to AWS EC2, whilst GKE (Google Kubernetes Engine) is a managed Kubernetes environment, almost identical to AWS EKS, but created by the team that invented K8s. Cloud Run runs containerised applications without the need for server management, whilst Cloud Functions is suitable for event-driven functions. On Google Cloud, this stack can be used to host internal enterprise applications, containerised services and run engineering simulations on GPU instances.
Cloud Storage is an object storage service with several classes for active, infrequently accessed and archived data, comparable in purpose to AWS S3. BigQuery functions as a serverless data warehouse for analysing large datasets without the need to manage infrastructure yourself. GCP also offers Cloud SQL for MySQL and PostgreSQL, Firestore for NoSQL scenarios, and Spanner for distributed transactional databases. For example, BigQuery can be used to analyse equipment telemetry, production metrics and operational history.
Google is a leader in the field of ML/AI. Vertex AI brings together tools for building, training, deploying and monitoring ML models within a single environment. AutoML enables models to be trained with less manual development. TensorFlow is a popular ML framework used to build custom machine learning solutions, whilst the Gemini API is used to integrate generative AI. For engineering organisations, such tools are suitable, for example, for predictive equipment maintenance, technical data analysis and computer vision in product quality control.
Google Workspace brings together email, documents, files, meetings and a calendar within a single corporate environment. Unlike Google’s free services, the company gains centralised management of users, the domain, security and access via the Admin Console. Key tools:
The feature set and storage capacity depend on the Workspace edition selected. To choose a configuration that suits your number of employees, storage requirements and infrastructure, please contact our engineering experts GEO-MENTOR.
For engineering firms, Google Cloud Platform is particularly attractive for projects where machine learning, big data processing and containerised architecture are key. When choosing between GCP and AWS, it is worth comparing not the platforms as a whole, but the services suited to specific workloads.
| Parameter | GCP | AWS |
| ML and AI | Vertex AI, Gemini, TensorFlow | SageMaker, Bedrock |
| Data analytics | BigQuery | Redshift |
| Kubernetes | GKE, closely integrated with the Kubernetes ecosystem | EKS |
| Global network | Google’s own global network | AWS global infrastructure |
| Sustainability | Google offsets 100 per cent of its electricity consumption with renewable energy purchases every year | AWS is developing its own infrastructure decarbonisation programmes |
GCP is best suited primarily for machine learning, big data analytics and Kubernetes-first architectures. AWS may be more practical if a business has already built its existing infrastructure and integrations around its services.
GEO-MENTOR is an authorised Google Cloud Platform partner that helps companies build cloud infrastructure tailored to real-world workloads. Depending on the project, the team carries out:
GEO-MENTOR works with both AWS and Google Cloud, so it can select a single platform or a hybrid architecture to suit the project’s requirements. Our company also offers CAD/PLM solutions for your industry, which can be integrated with Google platforms to form a single digital enterprise environment.