What is the difference between Google Cloud and Wafer?

Google Cloud and Wafer are both cloud GPU providers offering different pricing models, features, and GPU availability. Use our comparison tool to see real-time pricing and feature differences.

Which is cheaper: Google Cloud or Wafer?

Pricing varies by GPU model and usage requirements. Check our real-time comparison table to find the best deals for your specific needs.

Can I switch between Google Cloud and Wafer?

Yes, both providers offer flexible cloud GPU services. However, consider factors like data transfer costs, setup time, and specific features when switching between providers.

Google Cloud vs Wafer GPU Cloud Pricing 2026

GPU Pricing Comparison

No GPU pricing data available for comparison between Google Cloud and Wafer.

Features Comparison

Google Cloud

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.
Preemptible VMs
Short-lived compute instances at a significant discount, suitable for fault-tolerant workloads.

Wafer

Serverless Inference
Pay-as-you-go API access to hosted open-source models including GLM, Kimi, Qwen, and DeepSeek with no infrastructure management
Dedicated Endpoints
Custom-tuned inference deployments with performance guarantees, provisioned in under 24 hours
Automated Performance Optimization
Agents profile inference bottlenecks and tune across serving engines (vLLM, SGLang, TensorRT-LLM), custom kernels (CUDA, HIP, Triton, NKI), quantization (FP8/FP4), and decode strategies
Multi-Accelerator Hardware
Workloads run on NVIDIA B200/B300, AMD MI350X/MI355X, and AWS Trainium depending on the model and traffic shape
OpenAI-Compatible API
Standard chat completions endpoint at pass.wafer.ai/v1 with Bearer token authentication
Prompt Caching
Cached input tokens are billed at reduced rates on supported models

Pros & Cons

Google Cloud

Advantages

Flexible pricing options, including sustained use discounts
Strong AI and machine learning tools (Vertex AI)
Good integration with other Google services
Cutting-edge Kubernetes implementation (GKE)

Considerations

Limited availability in some regions compared to AWS
Complexity in managing resources
Support can be costly

Wafer

Advantages

High-throughput serving with results independently benchmarked on Artificial Analysis
Prompt caching with discounted cached-input rates
Dedicated endpoints set up in under 24 hours
Runs across NVIDIA, AMD, and AWS Trainium accelerators rather than a single vendor

Considerations

Small serverless model catalog centered on the GLM family
Inference only - no GPU rental, training, or fine-tuning hosting
Early-stage company with a smaller ecosystem than established inference providers

Compute Services

Google Cloud

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

Cloud Functions

Serverless compute platform for running code in response to events.

Automatic scaling and high availability
Pay only for the compute time consumed

Wafer

Pricing Options

Google Cloud

On-Demand

Pay for compute capacity per hour or per second, with no long-term commitments.

Sustained Use Discounts

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

Committed Use Discounts

Save up to 57% with a 1-year or 3-year commitment to a minimum level of resource usage.

Preemptible VMs

Save up to 80% for fault-tolerant workloads that can be interrupted.

Wafer

Pay-per-token

Prepaid credits with separate input and output token rates per model and no subscription

Cached input

Reduced rates for cached input tokens on supported models

Dedicated endpoints

Custom pricing for dedicated deployments with tuned performance targets, arranged with the sales team

Getting Started

Google Cloud

Get Started

1
Create a Google Cloud project
Set up a project in the Google Cloud Console.
2
Enable billing
Set up a billing account to pay for resource usage.
3
Choose a compute service
Select Compute Engine, GKE, Cloud Functions, or Cloud Run based on your needs.
4
Create and configure an instance
Launch a VM instance, configure a Kubernetes cluster, or deploy a function/application.
5
Manage resources
Use the Cloud Console, command-line tools, or APIs to manage your resources.

Wafer

Get Started

1
Create an account
Sign up at app.wafer.ai and load credits for pay-as-you-go usage
2
Generate an API key
Create a key in the console and pass it as a Bearer token
3
Make your first request
Call the OpenAI-compatible endpoint at pass.wafer.ai/v1 with a model from the serverless catalog

Support & Global Availability

Google Cloud

Global Regions

40+ regions and 120+ zones worldwide.

Support

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

Wafer

Support

Documentation at docs.wafer.ai, email support (hi@wafer.ai), and scheduled onboarding calls for enterprise