What is Google Cloud used for? The main ways businesses use it
Google Cloud (GCP) is used for the full range of cloud computing — hosting applications, storage, databases, networking — and is especially known for data analytics, machine learning and AI, and Kubernetes, reflecting Google’s own engineering heritage in those areas. Like the other major clouds, it is not one product but a large catalogue of services that organisations combine to build what they need. What sets Google Cloud apart is less about covering different ground and more about depth in a few particular areas, built from tools Google originally created to run its own services at scale. This article covers the main uses of Google Cloud, its particular strengths, who relies on it, and how it connects to certification.
Hosting, storage and databases — the general-purpose core
Like any major cloud, Google Cloud provides the basic building blocks every application needs: virtual servers to run compute workloads (see our explainer on what Google Compute Engine is), object storage to hold files and backups, and managed databases so teams do not have to install and patch database software themselves. This general-purpose core covers the same ground AWS and Azure do, and for many organisations it is the entry point — the same hosting, storage and database needs any application has, regardless of which cloud runs it.
Data analytics — where Google Cloud is particularly known
Analytics is one of Google Cloud’s clearest strengths, anchored by BigQuery, its serverless data warehouse for running SQL analytics over very large datasets (our BigQuery explainer covers it in depth). Organisations use it for business intelligence, reporting and ad hoc analysis at a scale that would be awkward for a conventional application database to handle. This strength traces directly back to Google’s own history of building systems to analyse huge volumes of data internally, long before offering the tooling externally.
Machine learning and AI
Google Cloud is equally known for machine learning and AI, centred on Vertex AI, its unified platform for building, training and deploying models — including access to foundation models for generative AI (see our Vertex AI explainer). Organisations use it both to build custom models on their own data and to call ready-made foundation models directly for generative AI tasks, without training anything themselves. As with analytics, this is an area where Google’s own research and internal use of machine learning long precedes the cloud product built around it.
Kubernetes and container orchestration
The third area Google Cloud is particularly associated with is Kubernetes, the open-source system for running and managing containerised applications at scale — which Google originally created and open-sourced, and which now runs on every major cloud. Google Kubernetes Engine (GKE), Google Cloud’s managed Kubernetes offering, is used by organisations running containerised applications that need to scale reliably (our GKE explainer covers what it does). Because Google built and open-sourced Kubernetes itself, GKE is often considered a particularly mature, close-to-the-source implementation of it.
Who actually uses Google Cloud
A similar range of organisations to any major cloud relies on Google Cloud — startups, established enterprises and public-sector bodies alike — but there is a noticeable concentration among teams whose workloads lean towards data analytics, machine learning or containerised, Kubernetes-based applications, precisely because those are the areas Google Cloud is strongest in. Organisations already using Kubernetes elsewhere, or with heavy analytics needs, often find Google Cloud a natural fit for exactly those workloads even when their general-purpose infrastructure runs elsewhere.
As with every major cloud, the honest picture is that no one organisation uses the entire catalogue — teams pick the services relevant to what they are building, drawing more heavily on Google Cloud’s data, AI and Kubernetes strengths where those genuinely matter to them.
How this connects to certification
Google Cloud’s certification track reflects this same shape. The foundational Associate Cloud Engineer exam (see our overview of what Google Cloud Platform is) covers the general-purpose core — compute, storage, databases, networking — that applies to almost any workload, while specialised tracks such as the Generative AI Leader certification go deeper into the AI side specifically. Learning Google Cloud well means starting with that general core before specialising into the areas — data, AI, Kubernetes — where the platform is particularly known.
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