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Reliable infrastructure for deep learning development

Why Valohai?

Setting up the deep learning infrastructure and comparing models takes time. Valohai automates the DevOps work and lets the team concentrate on building the models. Virtually unlimited amount of processing power at the fingertips of your ML team lets them train a model in minutes that would otherwise take a week.

Save up to 10x in costs

Cut away all setup and running costs of cloud computing. Valohai launches the servers you need in seconds, with zero configuration or setup time.

Train models faster

Run tens of experiments in parallel and quit spending time on running processes. Check the results once the run is ready and concentrate on making your models better.

Automatic Version Control

By maintaining a constant log from deployment to training, the team is able to reproduce experiments at the click of a button and address copliance issues.

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David Wang, TwoHat Security
"Having an engineering background and familiarity with Docker images, it was very easy to jump into Valohai. We configured our testing environment in Docker images and the tests in the Valohai YAML file, imported the project and boom! 💥We had 30 hyperparameter sweeps on our first try."

David Wang — Data Scientist, Twohat Security


With Valohai’s automatic version control, every execution done through the platform has a 100% audit trail automatically.

Gone are the days of getting a large bill at the end of the month from your cloud provider without knowing where the money has been spent!


Valohai is part of your DevOps team. Every Valohai account includes an onboarding session. We'll set up your project, help to define your Docker containers, cloud instances etc. You'll be invited to a joint Slack channel where we can keep in touch in real time. And every two weeks we have a scheduled Google Hangouts session.


Transparent view for the team

Team leaders can choose which team member to invite to which project and have a transparent view to the state of each project. Just like in GitHub you have a running commit log of every experiment both running and completed so that team members can check in on others work and avoid repeating the same experiments on and on again.

Grow your deep learning team

With a standardized way to version controlling data, model, hyperparameters and training algorithm you can be sure how a model was trained. Onboarding of new team members is easy, and if anybody ever leaves your team, you can be assured that everything is securely stored within your project and within reach of the rest of your team.

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Photograph of Toni Perämäki
Toni Perämäki
+1 (650) 614 1687
COO / Partnerships