mlvault snapshots your ML artifacts (checkpoints, logs, metrics) encrypts them before they leave your machine, and stores them permanently alongside your MLflow runs.
One pip install · Works with existing MLflow setup · Free during beta
You've been there. Three days of training. The instance goes down. The checkpoint is gone.
Works alongside MLflow. No migration. No new workflow to learn.
One pip install pulls everything in. Paste your storage credentials and you're ready.
pip install obsideo-cloud-mlflow mlvault init # Paste your mlvault storage credential when prompted. # Get one by applying for beta access below.
No changes to your training loop. Log metrics and artifacts the same way you always have.
import mlflow mlflow.set_experiment("my-experiment") with mlflow.start_run() as run: mlflow.log_metric("val_loss", 0.42) mlflow.log_artifact("checkpoints/epoch_10.pt") run_id = run.info.run_id
Encrypts on your machine, then stores permanently. Takes 30–60 seconds. Run it in the background while your next experiment starts.
mlvault commit <run_id> # Encrypting... ✓ # Uploading... ✓ # Done. Artifacts committed: my-experiment_20260307_143022_a3f9c1
Download, decrypt, and reconstruct your full artifact directory from any machine.
mlvault restore my-experiment_20260307_143022_a3f9c1 # Restored to ./my-experiment_20260307_143022_a3f9c1/
Not generic file storage bolted onto a tracker. Purpose-built for training runs.
Your artifacts are encrypted in transit and at rest. Enterprise-grade encryption applied automatically, no configuration, no S3 bucket policies, no IAM roles. It just works.
Artifacts aren't tied to your cloud account, your instance, or a single provider. Stored across distributed infrastructure: resilient, redundant, accessible when you need them.
Keep using MLflow for experiment tracking, metrics, and the UI you already know. mlvault adds permanent encrypted artifact storage as a plugin. Nothing to migrate.
Flat-rate pricing. No egress fees. No surprise bills at the end of the month. The more artifacts you store, the bigger the saving over AWS.
| Provider | Cost / TB / year | Client-side encryption | MLflow native |
|---|---|---|---|
| mlvault | ~$150 | ✓ Default | ✓ Plugin |
| AWS S3 | ~$276 | ✗ Server-side only | ✗ Manual setup |
| Google Cloud Storage | ~$240 | ✗ Server-side only | ✗ Manual setup |
| W&B Artifacts | $600+ (per user) | ✗ | ✗ Separate platform |
Running experiments on GPU workstations or cloud VMs who need checkpoints to survive instance termination.
Who need to reproduce experiments months later and can't afford to lose the artifact trail.
Training proprietary models on sensitive data who need encrypted storage they actually control.
We manually provision every account, so you get real support, not a ticket queue. That's the beta deal.