Code-first AI infrastructure
AI infrastructure built for
developers
On-demand GPUs, multi-cloud scaling, production-ready
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Or custom cloud or on-prem
Trusted by 100,000+ AI teams and developers
Why Saturn Cloud
Designed to help AI teams deploy faster
Code-first by default
Write standard Python using any framework. No proprietary APIs or vendor SDKs to learn. Your PyTorch, HuggingFace, and vLLM code runs as-is on Saturn Cloud.
Built for performance
Launch GPU workspaces with pre-configured CUDA, drivers, and your favorite ML frameworks. Go from idea to production endpoint in the same day.
Elastic GPU access
On-demand access to H100s, H200s, B200s, and B300s across AWS, GCP, Azure, Nebius, and Crusoe. No quota battles, no long-term reservations. Choose 1–8 GPUs per workload.
Enterprise security, zero setup
Deploy in your own cloud account with your VPC, IAM roles, and compliance requirements. SSO, RBAC, and cost controls included. Your data never leaves your infrastructure.
Products
Powering any AI workload
Training
Fine-tune and train models on single or multi-GPU clusters with PyTorch, HuggingFace, and Unsloth. H100s from $2.95/hr. Run scheduled training jobs or iterate in notebooks.
Inference
Serve LLMs and ML models in production with vLLM, NVIDIA NIM, or any serving framework on dedicated GPUs. Deploy endpoints that scale with your traffic.
Deployments
Deploy APIs with FastAPI, host dashboards with Streamlit, and run scheduled jobs for production pipelines. Go from notebook to production endpoint in minutes.
Development
GPU-accelerated workspaces with Jupyter notebooks, VS Code, or any IDE via SSH. Custom Docker images, Git integration, and collaborative environments for your entire team.
Platform
Build on a powerful foundation
From workspaces to production, every layer of Saturn Cloud’s platform is
engineered to give AI teams the tools to build robust, scalable applications.
AI-native runtime
Pre-configured CUDA, GPU drivers, and optimized base images for every major ML framework. Custom Docker images supported. Workspaces launch with everything your code needs.
Secure data access
Connect to your cloud storage, data warehouses, and model registries using IAM roles and encrypted secrets. Integrates with S3, GCS, Snowflake, and any data source your code can reach.
First-party integrations
Built-in support for Git, MLflow, Weights & Biases, Dask, and the full NVIDIA AI stack, including NIM. Connect your existing MLOps tools without additional configuration.
Multi-cloud GPU pool
Access GPU capacity across AWS, GCP, Azure, Nebius, Crusoe, Oracle, and on-prem Kubernetes. Run the same workloads on any backend with zero code changes.
Security
Security and governance
Enterprise-grade security that deploys in your cloud account. Your data,
your VPC, your compliance requirements – with full admin controls for your team.
VPC deployment
Saturn Cloud runs inside your own cloud account. Your data never touches our servers. Full network isolation with private subnets and no public endpoints.
Identity & access
SSO with SAML and OIDC, role-based access controls, and IAM role integration for cloud resources. Manage who can access what across your entire team.
SOC 2 compliant
Audited security controls, encrypted data at rest and in transit, and detailed audit logging. Built for teams with strict compliance requirements.
Cost controls & quotas
Set spending limits per user or team, monitor GPU utilization in real time, and auto-shut down idle resources. Full visibility into who is using what.
The difference
See how Saturn Cloud compares
Saturn Cloud gives AI teams the GPU access, developer experience, and production tooling they need — without proprietary lock-in or infrastructure overhead.
| DIY on AWS / GCP / Azure | Saturn Cloud |
|---|---|
| Provision and manage your own Kubernetes cluster | Managed infrastructure — click to launch |
| Assemble notebooks, tracking, deployments from separate tools | Unified MLOps stack out of the box |
| Write custom YAML for every training job | Promote notebooks to jobs and endpoints in the UI |
| No built-in idle detection — GPUs bill 24/7 | Automatic shutdown after configurable idle period |
| Locked into one cloud provider's ecosystem | Same experience across 7 infrastructure backends |
| Weeks of setup before your first training run | First model training in under 15 minutes |
| Amazon SageMaker | Saturn Cloud |
|---|---|
| Setup Requires VPC configuration, subnets, and AWS IAM setup before first notebook | Setup Sign up and launch a GPU workspace in minutes — no DevOps required |
| Code Proprietary SageMaker SDK with extensive boilerplate for training and deployment | Code Standard Python — your PyTorch, HuggingFace, or vLLM code runs as-is |
| GPU pricing Premium over base EC2 prices (e.g. $25/hr for 8xA100 vs $22/hr EC2) | GPU pricing H100s from $2.95/hr via Nebius, plus access to AWS, GCP, Azure GPU fleets |
| GPU flexibility Some GPU types require large fixed configurations (e.g. 8xA100 minimum) | GPU flexibility Choose 1–8 GPUs of any type. Scale up or down per workload |
| Cloud lock-in AWS only — models, data, and workflows tied to AWS services | Cloud lock-in Run on AWS, GCP, Azure, Nebius, Crusoe, Oracle, or on-prem |
| Deployment Separate SageMaker Endpoints service with its own API and configuration | Deployment Deploy with vLLM, FastAPI, or any framework — promote directly from notebooks |
| Databricks | Saturn Cloud |
|---|---|
| Focus Data engineering platform with ML bolted on — built around Spark | Focus Purpose-built for ML engineering — workspaces, training jobs, deployments |
| Pricing DBU-based pricing on top of cloud compute — costs escalate at scale | Pricing Transparent per-hour GPU pricing, no abstraction layers or hidden fees |
| Startup time 4–5 minute cluster spin-up before you can run a single cell | Startup time GPU workspaces launch in seconds with pre-configured CUDA and drivers |
| Code Databricks-specific APIs and MLflow integration required for full functionality | Code Standard Python — bring any framework, any library, any workflow |
| GPU access GPU configuration tied to underlying hyperscaler instance types | GPU access Direct GPU selection (T4 through H200) across 7 infrastructure backends |
| Deployment Model serving through MLflow or Spark Structured Streaming | Deployment Deploy with vLLM, FastAPI, NIM, or any serving framework you choose |
| Google Colab | Saturn Cloud |
|---|---|
| GPU access Shared GPUs with no availability guarantee — sessions disconnect randomly | GPU access Dedicated GPUs (T4 through H200) with guaranteed availability |
| Environment Notebook-only — no terminal, no file management, no custom images | Environment Full environment with Jupyter, VS Code, terminal, custom Docker images, and Git |
| Scale Single notebook, single GPU — no multi-GPU or distributed training | Scale Multi-GPU training (up to 8x H100/H200), Dask clusters for distributed compute |
| Production No deployment or serving capability — prototyping only | Production Deploy models as APIs, run scheduled jobs, host dashboards |
| Team use Built for individual users — limited collaboration and no RBAC | Team use Multi-user with SSO, RBAC, shared images, and team resource management |
| Data security Data stored on Google's infrastructure — limited compliance controls | Data security Deploy in your own cloud account — your VPC, your IAM, your compliance |
Taking runtime down from 60 days to 11 hours is such an incredible improvement. We are able to fit in many more iterations on our models. This has a significant positive impact on the effectiveness of our product.
— Seth Weisberg, Principal ML Scientist, Senseye