Paperspace
FreemiumHigh-performance GPU infrastructure for ML teams.
A powerful cloud computing platform offering scalable GPU infrastructure for deep learning and AI.
What is it
Paperspace (by DigitalOcean) is a powerful cloud computing platform and ML development environment designed specifically for the AI era. It focuses on providing a complete, developer-friendly infrastructure for building, training, and deploying machine learning models at scale.
What it can do
Its 'Gradient' platform offers hosted Jupyter notebooks, automated model training pipelines, and easy-to-deploy model endpoints. Paperspace provides access to a wide range of NVIDIA GPUs and high-performance networking, allowing for seamless scaling from a single notebook to massive, multi-node training clusters.
Who is it for
It is highly favored by data scientists, machine learning engineers, and AI research teams who want a robust, fully-managed environment that simplifies the complexities of ML infrastructure management and accelerates their model development.
Key Features
Gradient ML Infrastructure
Paperspace provides a powerful platform for training and deploying machine learning models on high-performance GPUs. Its Gradient platform simplifies the complex tasks of infrastructure management, providing a professional and highly efficient solution for data scientists and engineers at any scale.
Core GPU Desktop Cloud
Access a fully-featured Windows or Linux desktop in the cloud powered by professional-grade GPUs. Paperspace allows for high-end graphic design, video editing, and technical development from any device, providing a truly flexible and productive professional mobile and remote workflow.
Enterprise Data Security
Paperspace prioritizes the security of your proprietary code and data with industry-leading encryption and SOC 2 compliance. Your workloads remain private and secure, providing a safe environment for corporate teams and technical startups to build and scale their high-value AI and digital assets.
Managed Kubernetes Sync
Deploy and scale your AI applications effortlessly using managed Kubernetes clusters optimized for GPU workloads. Paperspace handles the complex technical orchestration, ensuring that your projects remain stable and highly available for your global audience with total professional precision.
Use Cases
Machine learning model training
Launch GPU-powered Jupyter notebooks or training jobs to build and fine-tune models of any size. Paperspace Gradient provides pre-configured templates with major ML frameworks and CUDA drivers installed, letting you go from signup to training in seconds.
Deep learning at scale
Scale from a single GPU to multi-node clusters with up to 8x NVIDIA H100 GPUs connected via NVLink. Paperspace handles the infrastructure orchestration so you can focus on experiments rather than DevOps.
ML model deployment and serving
Convert trained models into scalable API endpoints with Gradient Deployments. Monitor inference performance, manage versions, and scale compute up or down based on demand without managing underlying servers.
AI research and experimentation
Use on-demand GPU instances to run ablation studies, benchmark new architectures, or reproduce published results. The per-second billing means you only pay for active compute time, making it affordable to iterate rapidly.
Pricing plans
Frequently Asked Questions
Discussion
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