Solving the Infrastructure Bottleneck

The AIchor platform allows the user to run Machine Learning and Reinforcement Learning workloads at scale, leveraging the underlying infrastructure in the cloud from code commits

AIchor platform dashboard that shows many experiments with different steps, status, and costs.

Powered by

Why AIchor

AIchor's platform objective is to take advantage of the full power of high density CPU cores and advanced GPUs to distribute machine learning workloads at scale while abstracting the hardware and technical intricacies to AI Engineers and researchers.

  • Optimize the cost of infrastructure by using a containerized environment

  • Schedule machine learning experiments at scale

  • Rely on a reliable solution with higher dataset durability

  • Have a seamless user experience and empower AI engineers

Seamless Distribution

A secure platform where Compute requirements specifications can be easily bootstrapped in a single file. Having one centralized access to the clusters.

Seamless Distribution A secure platform where compute requirements specifications can be easily bootstrapped in a single file, providing centralized access to clusters. Streamlining the distribution of computing resources for efficient management.

How It Works

Get your workloads running on distributed resources in 4 steps

1 Create a Cluster

Deploy a GKE cluster from Aichor web interface where your workload will be running.

2 Plug your VCS to Aichor

Connect your repository (on Github, Gitlab or Bitbucket) to provide Aichor with access to your code.

3 Configure your job

Add a manifest file to your repository to specify resources (RAM, CPU, GPU) required for your run.

4 Trigger your job

Run your job with a simple commit and monitor its progress, logs and real-time resources used.

Workload Management

Logs management

Realtime logs being streamed and accessible via the UI

Logs management AIchor


Users can monitor and manage their workloads from a web page, no need for ssh access. They can track compute costs easily by teams or projects from the user interface.

Resources management AIchor


Ability to interact with input and output datasets.

Storage AIchor

Working with big pre-trained models is challenging in terms of hardware requirements as we are all familiar with the CUDA OUT OF MEMORY error. We found AIchor to be the perfect solution that not only fixed all of that but also optimized the cost of infrastructure by using a containerized environment and engaging the A100s only when called. Thanks to AIchor, we now only need a data scientist with docker skills in order for us to iterate and improve our models.

Request a Demo

Test AIchor now with a Free demo.