v3.2 launched — new GPU instances available

DataScience in the cloud —
fast, powerful, scalable

Train ML models, run Jupyter notebooks, and deploy production solutions on powerful GPU clusters. No DevOps headaches, no wasted resources.

                        # Jupyter Notebook in the cloud

                        import dscloud as dsc

                        # Launch GPU training

                        dsc.launch_gpu(

                          image="pytorch:2.1-cuda12",

                          gpus=4,

                          ram="64GB"

                        )

                        # Scale deployment

                        model = dsc.deploy(

                          "my-bert-model",

                          replicas=8

                        )

Capabilities

Everything for Data Science in one cloud

From research notebooks to production pipelines — the platform covers the full ML development cycle.

⚡

Instant GPU Instances

Up to 8× NVIDIA H100 per instance. Start in seconds, per‑minute billing — zero idle cost.

📓

Jupyter Lab in Browser

Pre‑configured images with PyTorch, TensorFlow, JAX. Connect from any browser instantly.

🔗

Data Versioning

DVC‑compatible storage with checkpoints. Your data is always accessible and never lost.

📊

MLflow & W&B Integration

Experiment tracking out of the box. Log metrics, compare runs, and share results seamlessly.

🚀

One‑Click Deploy

REST API or gRPC endpoint for your model in 30 seconds. Autoscaling, monitoring, HTTPS included.

🔒

Enterprise Security

VPC, encryption, SSO, audit logs. GDPR‑compliant and built for regulated industries.

Pricing

Choose the right plan

Flexible plans for individual researchers, teams, and enterprise customers.

Starter

$0/mo

To explore the platform

10 GPU hours (T4) per month
1 Jupyter Lab instance
20 GB storage
Basic monitoring

DataScience in the cloud —fast, powerful, scalable