Should GPU Cloud Operators Build or Buy a Platform Layer?
In this article, we’ll discuss what it actually takes to build a platform layer in-house, what it costs, and where the decision tips …
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Why GPU Clouds Need a Platform Layer
GPU clouds that sell only compute hours are losing enterprise customers to hyperscalers. Enterprise AI teams don't evaluate GPU clouds solely on price per GPU hour. They evaluate the full platform experience, including developer environments, training orchestration, model deployment, access controls, and usage tracking. When those are unavailable, teams revert to SageMaker, Vertex AI, or Azure ML, even at 2–3x the cost.
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Using our Claude Code plugin to set up our team's website workspace
Our website is built with Hugo. Some of our contributors aren't developers, and installing the toolchain on a laptop is enough friction …
Designing a Claude Code Plugin for AI Infrastructure
Three design principles we learned building a Claude Code plugin for Saturn Cloud: treat the live API as ground truth, treat the skill …
Building an ML Pipeline on Saturn Cloud with the Claude Code Plugin
How we used the saturn-cloud Claude Code plugin to build a reproducible ML demo end-to-end: ingestion, dataset versioning, feature …
Saturn Cloud vs AWS SageMaker for LLM Training
A comparison of setup, GPU access, pricing, and workflow for teams training and deploying large language models, including when …
Run Claude Code on a Cloud GPU in 10 Minutes – No Root Workarounds Required
How to get Claude Code running in fully autonomous mode on an H100 on Saturn Cloud from sign-up to first agent output, with working …