Google Cloud

What's good about Google Cloud

• Flexible pricing options, including sustained use discounts
• Strong AI and machine learning tools (Vertex AI)
• Good integration with other Google services
• Mature managed Kubernetes offering (GKE)
• Strong global network infrastructure
• Broad AI/ML and data analytics service portfolio

Google Cloud GPU prices

9 GPU models across 78 configurations. Rank compares Google Cloud's cheapest on-demand listing for each GPU with every provider's cheapest.

What a run costs on Google Cloud

24 hours at today's cheapest listing for each GPU, at the size it is listed.

• 8× H100 SXM for 24 hours

  Iowa (us-central1) at $11.06/GPU-hr

  $2,123.77

  $1,510.33 over the market median rate

• 8× H200 for 24 hours

  Iowa (us-central1) at $10.60/GPU-hr

  $2,035.37

  $1,124.89 over the market median rate

• 1× A100 SXM for 24 hours

  Iowa (us-central1) at $5.07/GPU-hr

  $121.65

  $81.69 over the market median rate

The market median is every provider's cheapest on-demand price for that GPU.

Google Cloud LLM API prices

Pay-per-token pricing, per 1M tokens.

Prices last updated November 10, 2026

Billing & terms

On-Demand

Pay for vCPUs, GPUs, and memory with a one-minute minimum and per-second billing thereafter, with no long-term commitments.

Sustained Use Discounts

Automatic discounts for running instances for a significant portion of the month.

Resource-based Committed Use Discounts

Discounted rates in exchange for a 1-year or 3-year commitment to a minimum level of resource usage in a region, and combinable with reservations.

Compute Flexible Committed Use Discounts

Spend-based commitments that apply across eligible machine families and regions rather than to specific resources.

Spot VMs

Spare capacity at discounted Spot prices for fault-tolerant workloads that can be preempted.

Regions

40+ regions and 120+ zones worldwide.

Support

Role-based (free), Standard, Enhanced and Premium support plans. Comprehensive documentation, community forums, and training resources.

Watch out for

• Limited availability in some regions compared to AWS
• Complexity in managing resources
• Support can be costly
• Steeper learning curve for some services

Features

Compute Engine

Scalable virtual machines with a wide range of machine types, including GPUs.

Google Kubernetes Engine (GKE)

Managed Kubernetes service for deploying and managing containerized applications.

Cloud Functions

Event-driven serverless compute platform.

Cloud Run

Fully managed serverless platform for containerized applications.

Vertex AI

Unified ML platform for building, deploying, and managing ML models.

Spot VMs

Spare compute capacity at discounted Spot prices, suitable for fault-tolerant workloads that can be interrupted.

Flexible machine sizing

Balance vCPUs, memory, and disk with up to 8 GPUs per instance, billed per second after a one-minute minimum.

Cloud Storage

Scalable and durable object storage.

Persistent Disk

Block storage for Compute Engine instances.

Cloud Load Balancing

High-performance, scalable load balancing.

Virtual Private Cloud (VPC)

Software-defined networking for your cloud resources.

Services

• Compute Engine

  Offers customizable virtual machines running in Google's data centers.

• Google Kubernetes Engine (GKE)

  Managed Kubernetes service for running containerized applications.

  • Automated Kubernetes operations
  • Integration with Google Cloud services
  • Advanced cluster management features

• Cloud Functions

  Serverless compute platform for running code in response to events.

  • Automatic scaling and high availability
  • Pay only for the compute time consumed
  • Supports multiple programming languages

• Cloud Run

  Fully managed serverless platform for deploying and scaling containerized applications.

  • Runs stateless containers on a fully managed environment
  • Automatic scaling and high availability
  • Pay only for the resources used

• Vertex AI

  Access to Google's Gemini models and other foundation models through a fully managed platform with enterprise security, MLOps tools, and Google Cloud integration.

  • Gemini Models: Access Google's latest Gemini Pro and Flash models with multimodal capabilities
  • Model Garden: Curated collection of open-source and Google-developed models
  • Grounding: Connect models to Google Search or your own data for accurate responses
  • Context Caching: Cache large context windows for cost savings on repeated queries
  • Pay-per-token: Standard per-token pricing for input and output
  • Context Caching: Reduced rates for cached context in long conversations
  • Provisioned Throughput: Reserved capacity for predictable performance

Getting started

1. Step 1

   Create a Google Cloud project

   Set up a project in the Google Cloud Console.

2. Step 2

   Enable billing

   Set up a billing account to pay for resource usage.

3. Step 3

   Choose a compute service

   Select Compute Engine, GKE, Cloud Functions, or Cloud Run based on your needs.

4. Step 4

   Create and configure an instance

   Launch a VM instance, configure a Kubernetes cluster, or deploy a function/application.

5. Step 5

   Manage resources

   Use the Cloud Console, command-line tools, or APIs to manage your resources.