- ML Engineers & Data Scientists: Those training or fine-tuning models who need powerful GPUs.
- Research Labs & Academics: For experiments that require GPU compute without huge infrastructure costs.
- Startups & Small Teams: Wanting to scale AI workloads but constrained by budget.
- Developers: Working on GPU-intensive tasks such as image generation, large inference, or ML pipeline building.
- AI Product Teams: Needing persistent and flexible compute environments.
- Data-Intensive Businesses: Companies needing GPU power for analytics, simulation, or visualization.
How to Use It?
- Create Account: Sign up and select a plan that fits your compute needs.
- Choose Instance Type: Pick the GPU type (e.g. T4, A100) and configure CPU, RAM, disk, networking.
- Select Mode: Use “prototyping mode” for cost-efficient work with some optimizations; toggle to production mode when full compatibility is needed.
- Access via Tools: Connect using VS Code extension or CLI, launch instances quickly.
- Run Workloads: Deploy ML training, inference, notebooks, model serving or other GPU tasks.
- Monitor & Scale: Keep an eye on usage, performance, storage, and change specs or snapshots as needed.
- Cost Savings: Promises up to ~80% lower cost compared to larger cloud providers for GPU instances.
- Mode Flexibility: Prototyping mode lets users optimize cost with some trade-offs; production mode ensures compatibility and stability.
- Fast Startup & Convenience: One-click or few-click setup, along with extensions (VS Code), simplifies the process.
- Custom Hardware Configuration: Users can tweak CPU, RAM, disk size, networking to match workload needs.
- Persistent Storage & Snapshots: Enables data persistence and snapshotting for reproducibility and state-saving.
- High Networking Speeds: Provides high throughput networking (7-10 Gbps) to support heavy data movement.
- Very competitive pricing for GPU cloud compute
- Flexibility between cost savings and compatibility
- Easy to use for developers with familiar tools (VS Code, etc.)
- Good hardware options including high-memory GPUs
- Useful features like snapshots, persistent storage, and mode switching
- Prototyping mode may have compatibility issues for some workloads
- Possibly less ideal for very large scale production or ultra-low latency workloads where overhead matters
- Reliance on cloud infrastructure; for some use cases, on-prem or specialized hardware may outperform
- Pricing, though much lower, still adds up for heavy continuous usage
Custom
Custom Pricing.
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Rating Distribution
Average score
Popular Mention
FAQs
Yes — compared to typical rates from AWS/GCP/etc., the GPU instances are significantly more affordable.
Options include Tesla T4, A100 (40GB), A100 (80GB).
A cost-efficient mode with optimizations to increase utilization and reduce costs; good for development and experimentation.
Yes — users can switch to production mode when full compatibility and robustness are needed.
Yes — frameworks like PyTorch, TensorFlow, some model serving tools are supported, especially in prototyping mode.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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