guides/clusters/overview.md

---
title: Overview
version: EN
---

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VESSL enables seamless scaling of containerized ML workloads from a personal laptop to cloud instances or Kubernetes-backed on-premise clusters. &#x20;

While VESSL comes with an out-of-the-box, fully managed AWS, you can also integrate **an unlimited number** of (1) personal Linux machines, (2) on-premise GPU servers, and (3) private clouds. You can then use VESSL as a single point of access to multiple clusters.&#x20;

VESSL Clusters simplifies the end-to-end management of large-scale, organization-wide ML infrastructure from integration to monitoring. These features are available under **🗂️ Clusters**.

* **Single-command Integration** — Set up a hybrid or multi-cloud infrastructure with a single command.
* **GPU-accelerated workloads** — Run training, optimization, and inference tasks on GPUs in seconds
* **Resource optimization** — Match and scale workloads automatically based on the required compute resources
* **Cluster Dashboard** — Monitor real-time usage and incident & health status of clusters down to each node.
* **Reproducibility** — Record runtime metadata such as hardware and instance specifications.

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