Google Cloud

Google Cloud is Google’s public cloud, and this Google Cloud platform overview focuses on what sets it apart: data analytics, machine learning and Kubernetes. It suits data-heavy products, teams already using Google Workspace or Firebase, and anyone who wants managed containers without running their own control plane.

Google Cloud core services #

Google Cloud has a large catalog, but a handful of services cover most real projects.

• Compute Engine: virtual machines with predefined or custom machine types, so you can choose vCPU and memory independently.
• Google Kubernetes Engine (GKE): managed Kubernetes, available in Standard mode (you manage nodes) or Autopilot mode (Google manages nodes and you pay for pod resources).
• Cloud Run: runs containers serverlessly and scales to zero when there is no traffic.
• Cloud Storage: object storage with several storage classes for frequent, infrequent and archival access.
• BigQuery: a serverless data warehouse for SQL analytics over large datasets.
• Vertex AI: a unified platform for training, tuning and serving models, including access to Gemini and a Model Garden of Google, partner and open models.
• Cloud SQL and Firestore: managed relational databases (PostgreSQL, MySQL, SQL Server) and a serverless document database.

How resources are organized

Everything in Google Cloud lives inside a project. Projects hold resources, enabled APIs and permissions, and each project is linked to a billing account. Larger organizations add folders and an organization node above projects to apply policies centrally. Our guide to Google Cloud projects, billing accounts and core services walks through this structure step by step.

How pricing works #

Google Cloud is pay-as-you-go, with most compute billed per second after a short minimum. Several mechanisms lower the effective price:

New customers can usually get a free trial with credit to spend over a limited period. At the time of writing Google advertises $300 in credit for 90 days, but confirm the current offer on the official pricing page. Separately, a free tier gives ongoing monthly allowances on certain products, such as a small VM in selected US regions. The two are easy to confuse, so read our comparison of the Google Cloud free trial and free tier before you plan around either.

Where it fits well #

• Analytics: BigQuery lets you query large datasets without managing clusters, and it connects to Looker and other BI tools.
• Containers: Kubernetes started at Google, and GKE Autopilot plus Cloud Run give you two levels of managed container hosting.
• AI development: Vertex AI brings Gemini, open models, evaluation and deployment into one console and API.
• Google ecosystem teams: Firebase projects are Google Cloud projects, so mobile and web teams can grow into the wider platform.

Things to consider #

Google Cloud has fewer services than AWS in some niches, and a smaller partner ecosystem in certain enterprise markets. Check that the managed services you need are available in your target region before committing.

Products and names change. Google has renamed and consolidated several services over the years, so older tutorials may reference consoles or APIs that look different today. Rely on the current documentation.

Projects start with default quotas on resources such as GPUs and vCPUs per region. If you need more, request a quota increase from the Quotas page in the console. Some requests are reviewed, and new billing accounts may need some payment history before larger increases are approved.

Getting started the official way #

1. Sign in with a Google account and start at the official Google Cloud website. Setting up billing requires a valid payment method and identity details, which Google uses for verification and fraud prevention.
2. Turn on 2-Step Verification for the Google account that owns the billing account and projects.
3. Create a project per application or environment, and grant teammates IAM roles on specific projects instead of broad owner access.
4. Add a budget alert, then enable only the APIs you need.
5. Follow the quickstarts in the Google Cloud documentation for Compute Engine, Cloud Run or BigQuery.

Google Cloud Skills Boost and the documentation include hands-on labs if you prefer learning in a sandboxed environment before touching your own project.

NewCloudStore is independent and not affiliated with Google; trademarks belong to their owners.