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Compare GPU and LLM inference API pricing between EcoHash and Paperspace. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
| GPU Model ↑ | EcoHash Price | Paperspace Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 SXM 80GB VRAM • Paperspace | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
H100 SXM 80GB VRAM • Paperspace | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
Quadro P4000 8GB VRAM • Paperspace | Not Available | — | ||
Quadro P4000 8GB VRAM • | ||||
Quadro P5000 16GB VRAM • Paperspace | Not Available | — | ||
Quadro P5000 16GB VRAM • | ||||
Quadro RTX 4000 8GB VRAM • Paperspace | Not Available | — | ||
Quadro RTX 4000 8GB VRAM • | ||||
Quadro RTX 5000 16GB VRAM • Paperspace | Not Available | — | ||
Quadro RTX 5000 16GB VRAM • | ||||
RTX A4000 16GB VRAM • Paperspace | Not Available | — | ||
RTX A4000 16GB VRAM • | ||||
RTX A5000 24GB VRAM • Paperspace | Not Available | — | ||
RTX A5000 24GB VRAM • | ||||
RTX A6000 48GB VRAM • Paperspace | Not Available | 8x GPU | — | |
RTX A6000 48GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHash | Not Available | — | ||
RTX PRO 6000 96GB VRAM • | ||||
Tesla V100 32GB VRAM • Paperspace | Not Available | 8x GPU | — | |
Tesla V100 32GB VRAM • | ||||
A100 SXM 80GB VRAM • Paperspace | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
H100 SXM 80GB VRAM • Paperspace | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
Quadro P4000 8GB VRAM • Paperspace | Not Available | — | ||
Quadro P4000 8GB VRAM • | ||||
Quadro P5000 16GB VRAM • Paperspace | Not Available | — | ||
Quadro P5000 16GB VRAM • | ||||
Quadro RTX 4000 8GB VRAM • Paperspace | Not Available | — | ||
Quadro RTX 4000 8GB VRAM • | ||||
Quadro RTX 5000 16GB VRAM • Paperspace | Not Available | — | ||
Quadro RTX 5000 16GB VRAM • | ||||
RTX A4000 16GB VRAM • Paperspace | Not Available | — | ||
RTX A4000 16GB VRAM • | ||||
RTX A5000 24GB VRAM • Paperspace | Not Available | — | ||
RTX A5000 24GB VRAM • | ||||
RTX A6000 48GB VRAM • Paperspace | Not Available | 8x GPU | — | |
RTX A6000 48GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHash | Not Available | — | ||
RTX PRO 6000 96GB VRAM • | ||||
Tesla V100 32GB VRAM • Paperspace | Not Available | 8x GPU | — | |
Tesla V100 32GB VRAM • | ||||
| Model ↑ | EcoHash | Paperspace | 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 | $0.090 in $0.370 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 | — |
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.
Interactive Jupyter notebooks with a free tier, pre-configured templates, and access to powerful GPUs.
Serve machine learning models as scalable API endpoints.
High-performance virtual machines with a wide variety of NVIDIA GPUs for demanding workloads.
Features for teams to collaborate on projects, including shared drives and SSO.
Offers persistent storage that can be shared across different machines and notebooks.
Programmatic access to the Paperspace platform to automate workflows.
Virtual machines with a wide range of NVIDIA GPUs for various workloads.
Managed Jupyter notebooks for interactive development and experimentation.
A serverless product for deploying trained models as REST APIs.
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 Paperspace account. You can start with a free plan.
Select the product that best fits your needs, such as Gradient Notebooks for interactive development or Core Machines for more control.
Choose a pre-configured template for your desired framework and select a GPU or CPU instance.
Launch your machine or notebook and begin building, training, or deploying your AI applications.
Available across multiple global regions, including the Americas.
Offers support via documentation and community channels.
Data centers are located in the US (Secaucus, NJ and Santa Clara, CA) and Europe (Amsterdam, NL).
Support is available through a community forum, extensive documentation, and a support ticket system.