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Compare GPU and LLM inference API pricing between EcoHash and Vultr. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
| GPU Model ↑ | EcoHash Price | Vultr Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 SXM 80GB VRAM • Vultr | Not Available | 4x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A40 48GB VRAM • Vultr | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 180GB VRAM • Vultr | Not Available | 8x GPU | — | |
B200 180GB VRAM • | ||||
H100 SXM 80GB VRAM • Vultr | Not Available | 8x GPU | — | |
H100 SXM 80GB VRAM • | ||||
MI300X 192GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI300X 192GB VRAM • | ||||
MI325X 256GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI325X 256GB VRAM • | ||||
MI355X 288GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI355X 288GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHash | Not Available | — | ||
RTX PRO 6000 96GB VRAM • | ||||
A100 SXM 80GB VRAM • Vultr | Not Available | 4x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A40 48GB VRAM • Vultr | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 180GB VRAM • Vultr | Not Available | 8x GPU | — | |
B200 180GB VRAM • | ||||
H100 SXM 80GB VRAM • Vultr | Not Available | 8x GPU | — | |
H100 SXM 80GB VRAM • | ||||
MI300X 192GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI300X 192GB VRAM • | ||||
MI325X 256GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI325X 256GB VRAM • | ||||
MI355X 288GB VRAM • Vultr | Not Available | 8x GPU | — | |
MI355X 288GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHash | Not Available | — | ||
RTX PRO 6000 96GB VRAM • | ||||
| Model ↑ | EcoHash | Vultr | Input Diff ↕ |
|---|---|---|---|
DeepSeek | $0.910 in $2.72 out | Not available | — |
Zhipu | $1.00 in $3.00 out | Not available | — |
Z AI | $1.25 in $4.30 out | Not available | — |
Moonshot | $2.00 in $12.00 out | Not available | — |
Meta | $0.100 in $0.100 out | Not available | — |
MiniMax | $0.200 in $0.900 out | Not available | — |
Alibaba | $0.150 in $0.500 out | Not available | — |
Alibaba | $1.90 in $5.70 out | Not available | — |
Alibaba | $0.400 in $0.800 out | Not available | — |
Alibaba | $0.400 in $0.400 out | Not available | — |
OpenAI | $0.100 in $0.0000 out | Not available | — |
Explore how these providers compare to other popular GPU cloud services
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Offers configurations with 1, 2, 4, or 8 RTX Pro 6000 GPUs.
Supports automatic selection of LoRA/QLoRA fine-tuning over 1-4 GPUs.
Provides an API compatible with OpenAI for various AI models.
Users get full root access with shared filesystems on RTX Pro environments.
Inference endpoints can be deployed across multiple regions with failover.
Access to diverse GPU options including AMD Instinct and NVIDIA Tensor Core GPUs
Deploy GPU resources across 32 cloud data center regions worldwide
Vultr Kubernetes Engine for GPU-accelerated containerized workloads
Deploy and scale GenAI models quickly with Vultr Serverless Inference
Choose between GPU-accelerated VMs or dedicated bare metal servers
Accelerate content delivery across six continents with Vultr CDN
Virtual machines and bare metal servers with NVIDIA GPUs
Computing infrastructure powered by AMD Instinct accelerators
Managed Kubernetes service for GPU-accelerated containerized applications
Create an account on the EcoHash platform.
Select the desired GPU instances for your project.
Consult the EcoHash documentation to understand available features.
Launch your GPU environment for training or inference.
Start using the API for model inference or training tasks.
Sign up for a free Vultr account
Choose from AMD or NVIDIA GPU options based on your workload
Select between virtual machine or bare metal deployment
Set up networking, storage, and security options before launching
Available across multiple global regions, including the Americas.
Offers support via documentation and community channels.
32 global cloud data center regions across North America, South America, Europe, Asia, Africa, and Australia
Documentation, community forums, support tickets, and dedicated customer support