Code-first AI infrastructure
AI infrastructure for
enterprise developers
Multi-cloud, production-ready GPUs for teams of any size
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Or custom cloud or on-prem
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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 ML engineers
Pre-configured CUDA, drivers, and optimized base images for PyTorch, HuggingFace, vLLM, and Unsloth. Jupyter and VS Code on every resource. Go from notebook to production endpoint without leaving the platform.
Access the full GPU stack
H100s, H200s, B200s, and B300s - across AWS, GCP, Azure, Nebius, and Crusoe. No quota battles. 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.
Built for teams
Shared GPU environments, user management, and per-team resource quotas – all in one place. SSO, RBAC, and collaborative notebooks so your entire ML team works from the same platform without stepping on each other.
H100, H200, B200, and B300s
Access the full NVIDIA GPU stack across AWS, GCP, Azure, Nebius, and Crusoe. Choose the right GPU for your workload and scale from 1 to 8 GPUs per workload.
Fine-tuning Llama 3 8B–70B with QLoRA. Distributed training on multi-GPU clusters.
Full-precision 70B fine-tuning. High-throughput inference on Llama 3 and Mistral variants.
405B inference on fewer GPUs. Pre-training runs where memory and bandwidth are the constraint.
Frontier-scale workloads. Maximum memory headroom for the largest models and context windows.
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 |
What does Saturn Cloud support?
Yes. Saturn Cloud supports multi-node clusters for distributed training workloads. FSDP, DDP, and DeepSpeed are all supported. You provision multi-node clusters from the dashboard with no manual node configuration. H100 and H200 SXM instances include NVLink 4.0 at 900 GB/s for inter-GPU communication.
Saturn Cloud provides access to H100, H200, B200, and B300 GPU instances. H100 and H200 are available across multiple regions via AWS, GCP, Azure, Nebius, and Crusoe. B200 and B300 Blackwell instances are available via Nebius. All GPU types support 1–8 GPUs per workload.
Yes. Saturn Cloud supports custom Docker images. You can bring any image that includes your dependencies, frameworks, and CUDA version. Saturn Cloud also provides pre-built images for PyTorch, HuggingFace, and other major ML frameworks if you want to get started without a custom build.
Yes. Saturn Cloud has first-party support for NVIDIA NIM inference microservices. You can pull and run NIM containers directly on H100 or H200 instances. Docker is pre-configured on every resource, and Saturn Cloud's secrets manager stores your NGC API key securely.
Saturn Cloud deploys inside your own cloud account — your VPC, your subnets, your IAM roles. Your data never moves through Saturn Cloud's servers. The platform is SOC 2 compliant with encrypted data at rest and in transit, full audit logging, and private networking with no public endpoints required.
Saturn Cloud runs standard Python with no proprietary APIs or SDKs. PyTorch, HuggingFace Transformers, TRL, vLLM, Unsloth, FastAPI, Dask, and any other framework your code already uses will run as-is. CUDA, drivers, and cuDNN are pre-configured in base images.
Yes. Every Saturn Cloud resource supports Jupyter notebooks and VS Code as development environments. You can also connect via SSH with any IDE. GPU-backed workspaces launch in seconds with your frameworks and dependencies pre-installed.
SageMaker requires its own SDK and extensive boilerplate for training and deployment. Saturn Cloud runs standard Python with no proprietary APIs. SageMaker is AWS-only; Saturn Cloud runs across AWS, GCP, Azure, Nebius, Crusoe, and on-prem. H100s on Saturn Cloud start at $2.95/hr via Nebius vs. SageMaker's EC2 premium pricing. Full comparison →
Yes. Saturn Cloud includes SSO with SAML and OIDC, role-based access controls (RBAC), and IAM role integration for cloud resources. Enterprise plans include user management, team-level cost controls, GPU utilization monitoring, and configurable idle shutdown to prevent runaway spend.
Saturn Cloud installs into your own cloud account on AWS, GCP, Azure, Nebius, Crusoe, Oracle, or on-prem Kubernetes. The same workloads — training jobs, inference endpoints, notebooks — run identically across all backends with zero code changes.
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