[Google Cloud](https://cloud.google.com/)

Google Cloud is Google’s cloud computing platform. It rents out compute power, storage, databases, data analytics tools, and AI services over the internet, on a pay-as-you-go basis, running on the same infrastructure that powers Google Search, Gmail, and Google Docs. Google lists over 100 products under the Google Cloud brand, spanning everything from virtual machines to fully managed AI agent platforms. For AI developers, Google Cloud matters because it hosts Vertex AI and BigQuery, the managed services that most production machine learning and analytics pipelines run on.

How Google Cloud works

Google Cloud delivers infrastructure as a service (IaaS), meaning you rent raw computing resources instead of buying hardware, plus platform and software services built on top of that infrastructure.

• Compute: Compute Engine gives you virtual machines you can resize down to the exact CPU and memory you need, avoiding the over-provisioning common on other clouds.
• Containers: Google Kubernetes Engine (GKE) and Cloud Run automatically deploy, scale, and manage containerized applications, so you do not have to run your own cluster infrastructure by hand.
• Storage and databases: Cloud Storage holds any amount of data as objects, while Cloud SQL, AlloyDB, and Spanner cover managed relational and globally distributed databases.
• Data and analytics: BigQuery is a serverless data warehouse that runs SQL queries over massive datasets without you managing any servers, billed by the amount of data scanned.
• AI and machine learning: Vertex AI and Agent Platform give developers access to over 200 foundation models, plus tools for training, fine-tuning, and deploying custom models and AI agents.
• Billing: every service is metered, so you pay per hour, per gigabyte, or per query, with discounts available for committed or sustained use.

What you can build with Google Cloud

• Production web applications: deploy a full-stack app on Cloud Run or GKE, backed by Cloud SQL or Spanner, scaling automatically as traffic grows without managing servers.
• Data analytics pipelines: stream events into BigQuery and run SQL analytics at a scale that would be slow or expensive on a single database server.
• Custom AI agents: use Vertex AI and Agent Platform to build, orchestrate, and govern AI agents that connect to your company’s data and tools.
• Retrieval-augmented generation (RAG) systems: combine Vertex AI’s foundation models with BigQuery or Cloud Storage to ground AI responses in your own documents and data.
• Computer vision and speech applications: call pretrained APIs for image recognition, video analysis, or Speech-to-Text instead of training models from scratch.
• Enterprise migration projects: move existing workloads to virtual machines or containers using Google’s migration tools, without rewriting the underlying application code.