Your models. Your data. Your infrastructure.
A complete ML environment — training, versioning, notebooks, inference — running entirely inside your own perimeter. Your IP stays yours. Your results stay private.
Sending your training data to a cloud provider is a risk most teams haven't measured.
When you train on hyperscaler ML services, your proprietary data — the training set representing years of competitive advantage — traverses their infrastructure. Their terms govern it.
AravaliStack gives ML and research teams a complete, self-hosted alternative with none of the capability compromise.
Model Training
GPU-scheduled training with full experiment tracking. Every run logged and reproducible.
Notebook Environment
Browser-based data science notebooks with pre-configured environments.
Model Registry
Version, stage, and deploy models with full lineage and rollback capability.
Edge Inference
Deploy models to edge locations with offline-first capability and GitOps sync.