Shyft Score
Directory quality rating
Our take
Shadeform offers flexible GPU cloud services with competitive pricing, ideal for AI/ML development.
Best for: Engineering teams developing AI and machine learning applications
Try Shadeform's free tier to see if it fits your workflow.
See how Shadeform fits your stackBenefits
Access powerful GPU computing resources instantly without upfront hardware investments
Scale your machine learning workloads up or down based on actual demand to control costs
Monitor your GPU performance in real-time to optimize resource allocation and prevent bottlenecks
Pay only for the GPU resources you actually use instead of maintaining expensive idle hardware
About
Shadeform provides on-demand GPU resources for machine learning with flexible pay-as-you-go pricing and framework integration. Data scientists and ML engineers use it to scale model training without long-term commitments.
On-demand GPU resources for scalable computing
Marketplace for GPU-optimized applications
Real-time performance monitoring and analytics
Flexible pricing options based on usage
Seamless integration with popular machine learning frameworks
Category
cloud-infrastructure
Department
Engineering
Pricing
Usage_based (from $0.10 per GPU hour)
Website
shadeform.ai
Use cases
Machine learning model training
Deep learning with GPU acceleration
AI research and prototyping
Scalable inference workloads
Framework integration (PyTorch, TensorFlow)
Best for
Pricing
Shadeform starts at $0.10 per GPU hour
Starting at $0.10 per GPU hour
Ecosystem
MCP servers, AI skills, and integrations that work with Shadeform
FAQs
Common questions about Shadeform and its capabilities
Shadeform is a marketplace for GPU cloud services, offering on-demand GPU resources for scalable computing. It helps data scientists, ML engineers, and software developers access GPU-optimized applications and monitor performance in real-time, with flexible, usage-based pricing.
Our team can help you integrate Shadeform with your existing tools and build custom automation workflows.
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