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Compare GPU and LLM inference API pricing between Google Cloud and Theta EdgeCloud. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
| GPU Model ↑ | Google Cloud Price | Theta EdgeCloud Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
H200 141GB VRAM • Theta EdgeCloud | Not Available | — | ||
H200 141GB VRAM • | ||||
Tesla T4 16GB VRAM • Theta EdgeCloud | Not Available | — | ||
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Theta EdgeCloud | Not Available | — | ||
Tesla V100 32GB VRAM • | ||||
H200 141GB VRAM • Theta EdgeCloud | Not Available | — | ||
H200 141GB VRAM • | ||||
Tesla T4 16GB VRAM • Theta EdgeCloud | Not Available | — | ||
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Theta EdgeCloud | Not Available | — | ||
Tesla V100 32GB VRAM • | ||||
| Model ↑ | Google Cloud | Theta EdgeCloud | Input Diff ↕ |
|---|---|---|---|
Google | $1.50 in $7.50 out | Not available | — |
Google | $1.50 in $7.50 out | Not available | — |
Explore how these providers compare to other popular GPU cloud services
Compare Google Cloud with another leading provider
Compare Google Cloud with another leading provider
Compare Google Cloud with another leading provider
Compare Google Cloud with another leading provider
Compare Google Cloud with another leading provider
Compare Google Cloud with another leading provider
Scalable virtual machines with a wide range of machine types, including GPUs.
Managed Kubernetes service for deploying and managing containerized applications.
Event-driven serverless compute platform.
Fully managed serverless platform for containerized applications.
Unified ML platform for building, deploying, and managing ML models.
Spare compute capacity at discounted Spot prices, suitable for fault-tolerant workloads that can be interrupted.
Combines traditional cloud GPUs via Google Cloud and AWS with 30,000+ community-operated edge nodes for flexible compute.
Supply-demand driven pricing where node operators set rates and users select GPUs based on availability and cost.
Routes heavy training jobs to cloud/datacenter GPUs and distributes parallelizable inference across edge nodes.
Reroutes jobs to healthy nodes if a community node goes offline mid-task, ensuring workload continuity.
Docker-based job execution with Jupyter Notebook and SSH access for flexible development environments.
On-demand model inference APIs and dedicated AI model serving with support for popular generative AI models.
Offers customizable virtual machines running in Google's data centers.
Managed Kubernetes service for running containerized applications.
Serverless compute platform for running code in response to events.
On-demand GPU instances from distributed edge nodes and cloud partnerships.
AI-specific services and APIs for model deployment and inference.
Specialized video processing and streaming services.
Pay for vCPUs, GPUs, and memory with a one-minute minimum and per-second billing thereafter, with no long-term commitments.
Automatic discounts for running instances for a significant portion of the month.
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.
Spend-based commitments that apply across eligible machine families and regions rather than to specific resources.
Spare capacity at discounted Spot prices for fault-tolerant workloads that can be preempted.
Dynamic pricing set by node operators in a supply-demand GPU marketplace.
Hourly billing with no long-term commitments or contracts required.
Set up a project in the Google Cloud Console.
Set up a billing account to pay for resource usage.
Select Compute Engine, GKE, Cloud Functions, or Cloud Run based on your needs.
Launch a VM instance, configure a Kubernetes cluster, or deploy a function/application.
Use the Cloud Console, command-line tools, or APIs to manage your resources.
Sign up at thetaedgecloud.com to access the GPU marketplace dashboard.
Explore available H100, A100, 4090, and 3090 instances with marketplace pricing from node operators.
Choose a GPU instance and configure your containerized workload with Docker, Jupyter Notebook, or SSH access.
Launch your workload with automatic failover and monitor progress through the dashboard.
40+ regions and 120+ zones worldwide.
Role-based (free), Standard, Enhanced and Premium support plans. Comprehensive documentation, community forums, and training resources.
30,000+ globally distributed edge nodes with cloud capacity via Google Cloud and AWS regions.
Documentation at docs.thetatoken.org, dashboard monitoring, and community support channels.