Google Cloud Platform (GCP) is a suite of cloud services for computing, data storage, networking, analytics, and application development. For a more detailed look at the services and implementation best practices, you can consult McCloud’s expert review of Google Cloud Platform.

Use cases for GCP

The cloud platform enables you to run applications without purchasing or maintaining your own servers. Resources can be scaled according to workload, and access is managed via accounts, roles, and security policies.

GCP is used both for new digital products and for migrating specific components of existing IT infrastructure. The choice of model depends on requirements regarding availability, data storage location, integrations, and budget.

Key service categories

Compute: virtual machines, managed applications, and container environments for running services.

Storage and databases: object storage, as well as relational and NoSQL databases for various data types and workloads.

Analytics: tools for data collection, processing, and visualization, including BigQuery.

Artificial intelligence and machine learning: services for preparing, deploying, and using models, including Vertex AI.

Networking and security: private networks, load balancing, access management, and event logs.

What to evaluate before moving to the cloud

Before selecting services, it is advisable to document your current systems, their dependencies, data volumes, and business continuity requirements. You should also identify which data requires backup, encryption, or restricted access. Cloud costs depend on more than just computing resources; they are also influenced by storage, data transfer, network configurations, and redundancy strategies. Therefore, it is advisable to establish budgets, cost alerts, and rules for shutting down unused resources in advance.

Where to start

A practical approach usually begins with a single, controlled scenario—such as backups, a test environment, an analytics dataset, or a standalone application. Once performance, access, and costs have been verified, this approach can be scaled to other systems.