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Compare GPU and LLM inference API pricing between Google Cloud and IO.NET. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
Average Price Difference: $0.77/hour between comparable GPUs
| GPU Model ↑ | Google Cloud Price | IO.NET Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 PCIE 40GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 192GB VRAM • IO.NET | Not Available | — | ||
B200 192GB VRAM • | ||||
H100 PCIe 80GB VRAM • IO.NET | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | 8x GPU | — | |
H200 141GB VRAM • | ||||
HGX B300 288GB VRAM • IO.NET | Not Available | — | ||
HGX B300 288GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | — | ||
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 4090 24GB VRAM • IO.NET | Not Available | — | ||
RTX 4090 24GB VRAM • | ||||
RTX 5090 32GB VRAM • IO.NET | Not Available | — | ||
RTX 5090 32GB VRAM • | ||||
A100 PCIE 40GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 192GB VRAM • IO.NET | Not Available | — | ||
B200 192GB VRAM • | ||||
H100 PCIe 80GB VRAM • IO.NET | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | 8x GPU | — | |
H200 141GB VRAM • | ||||
HGX B300 288GB VRAM • IO.NET | Not Available | — | ||
HGX B300 288GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | — | ||
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 4090 24GB VRAM • IO.NET | Not Available | — | ||
RTX 4090 24GB VRAM • | ||||
RTX 5090 32GB VRAM • IO.NET | Not Available | — | ||
RTX 5090 32GB VRAM • | ||||
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.
Short-lived compute instances at a significant discount, suitable for fault-tolerant workloads.
Access to 300,000+ verified GPUs from 139 countries with 6,000+ cluster-ready GPUs
Deploy clusters in under 90 seconds with auto-scaling capabilities
Choose from containers, Ray clusters, or bare metal based on workload needs
Uses the same distributed computing framework that OpenAI used to train GPT-3
AI models, smart agents, and API integration for workflow automation
Kernel-level VPN with secure mesh protocols for data protection
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 clusters for AI/ML workloads with multiple deployment options
AI models, smart agents, and API integration platform
Decentralized pool of GPU providers with unified APIs and competitive pricing.
Pay for compute capacity per hour or per second, with no long-term commitments.
Automatic discounts for running instances for a significant portion of the month.
Save up to 57% with a 1-year or 3-year commitment to a minimum level of resource usage.
Save up to 80% for fault-tolerant workloads that can be interrupted.
Most cost-effective option for distributed ML workloads using Ray framework
Standard containerized deployments with Docker support
Premium pricing for direct hardware access and maximum performance
Dynamic pricing based on actual resource usage with automatic scaling
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.
Create an account on the IO.NET platform with no complex KYC requirements
Purchase $IO tokens for compute payments or add other supported payment methods
Select from containers, Ray clusters, or bare metal based on your workload
Specify GPU requirements, region preferences, and scaling options
Launch your cluster in under 90 seconds and start your AI/ML workloads
40+ regions and 120+ zones worldwide.
Role-based (free), Standard, Enhanced and Premium support plans. Comprehensive documentation, community forums, and training resources.
Global distributed network across 139 countries with intelligent geographic clustering and latency optimization
Documentation portal, Discord community (500,000+ members), Telegram support, and direct engineering support for GPU and driver questions