Our machine learning models analyze and predict compute capacity trends, pricing, and interruption rates to intelligently utilize spot, reserved and on-demand compute pricing models. Reduce costs by up to 90% using data-driven selection of the optimal cloud compute pricing models.
Simplify the process of defining scaling policies, identifying peak times, and automatically scaling the right capacity in advance by using machine learning to predict future resource needs and proactively scale to meet them while using the optimal mix of pricing models.
Ensure availability for production workloads
Integrating with your load balancer, our software automatically distributes workloads across multiple zones and excess capacity markets to ensure availability at minimum costs. Benefit from analytics that predict interruptions and take proactive actions to replace instances in advance.
Optimize without changing anything
Easily deploy your workloads to Spot with Terraform, CloudFormation, Ansible, Chef, and Puppet, and JSON. Our advanced scaling will make sure your workloads, whether working with Jenkins, Beanstalk, CodeDeploy, OpsWorks or other tools, are always up and running on the optimal blend of spot and on-demand instances as well as any available reserved capacity.
With Spot saving us at least $60,000 per month, our machine learning scientists can keep data for longer periods and invest in newer products and technologies.