Google Cloud vs TensorWave
Compare GPU pricing, features, and specifications between Google Cloud and TensorWave cloud providers. Find the best deals for AI training, inference, and ML workloads.
Google Cloud
Provider 1
TensorWave
Provider 2
Comparison Overview
GPU Pricing Comparison
| GPU Model ↑ | Google Cloud Price | TensorWave Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
L4 24GB VRAM • Google Cloud |  $0.56/hr★ Best8x GPU | Not Available | — | |
L4 24GB VRAM • | ||||
MI300X 192GB VRAM • TensorWave | Not Available |  $1.71/hr★ Best | — | |
MI300X 192GB VRAM • | ||||
MI325X 256GB VRAM • TensorWave | Not Available | $2.25/hr★ Best | — | |
MI325X 256GB VRAM • | ||||
MI355X 288GB VRAM • TensorWave | Not Available | $2.95/hr★ Best | — | |
MI355X 288GB VRAM • | ||||
Tesla T4 16GB VRAM • Google Cloud | $0.55/hr★ Best 4x GPU | Not Available | — | |
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Google Cloud | $2.48/hr★ Best 8x GPU | Not Available | — | |
Tesla V100 32GB VRAM • | ||||
L4 24GB VRAM • Google Cloud | $0.56/hr★ Best 8x GPU | Not Available | — | |
L4 24GB VRAM • | ||||
MI300X 192GB VRAM • TensorWave | Not Available | $1.71/hr★ Best | — | |
MI300X 192GB VRAM • | ||||
MI325X 256GB VRAM • TensorWave | Not Available | $2.25/hr★ Best | — | |
MI325X 256GB VRAM • | ||||
MI355X 288GB VRAM • TensorWave | Not Available | $2.95/hr★ Best | — | |
MI355X 288GB VRAM • | ||||
Tesla T4 16GB VRAM • Google Cloud | $0.55/hr★ Best 4x GPU | Not Available | — | |
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Google Cloud | $2.48/hr★ Best 8x GPU | Not Available | — | |
Tesla V100 32GB VRAM • | ||||
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.
TensorWave
- AMD Instinct Accelerators
Powered by AMD Instinct™ Series GPUs for high-performance AI workloads.
- High VRAM GPUs
Offers instances with 192GB of VRAM per GPU, ideal for large models.
- Bare Metal & Kubernetes
Provides both bare metal servers for maximum control and managed Kubernetes for orchestration.
- Direct Liquid Cooling
Utilizes direct liquid cooling to reduce data center energy costs and improve efficiency.
- High-Speed Network Storage
Features high-speed network storage to support demanding AI pipelines.
- ROCm Software Ecosystem
Leverages the AMD ROCm open software ecosystem to avoid vendor lock-in.
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
TensorWave
Advantages
- Specialized in high-performance AMD GPUs
- Offers GPUs with large VRAM (192GB)
- Claims better price-to-performance than competitors
- Provides 'white-glove' onboarding and support
Considerations
- A newer and less established company (founded in 2023)
- Exclusively focused on AMD, which may be a limitation for some users
- Limited publicly available information on pricing
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
TensorWave
AMD GPU Instances
Bare metal servers and managed Kubernetes clusters with AMD Instinct GPUs.
Managed Kubernetes
Kubernetes clusters for orchestrated AI workloads.
- Scalable from 8 to 1024 GPUs
- Interconnected with 3.2TB/s RoCE v2 networking
Inference Platform (Manifest)
An enterprise inference platform designed for larger context windows and reduced latency.
- Accelerated reasoning
- Secure and private data storage
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.
TensorWave
Getting Started
Google Cloud
- 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.
TensorWave
- 1
Request Access
Sign up on the TensorWave website to get access to their platform.
- 2
Choose a Service
Select between Bare Metal servers or a managed Kubernetes cluster.
- 3
Follow Quickstarts
Utilize the documentation and quick-start guides for PyTorch, Docker, Kubernetes, and other tools.
- 4
Deploy Your Model
Deploy your AI model for training, fine-tuning, or inference.
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.
TensorWave
Global Regions
Primary data center and headquarters are located in Las Vegas, Nevada. The company is building the largest AMD-specific AI training cluster in North America.
Support
Offers 'white-glove' onboarding and support, extensive documentation, and a company blog.
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