Pricing mechanics
Frequently Asked Questions
We’ve designed our Pay-As-You-Go plan with your interest in mind, so let us be transparent about it.
Pricing
$0.10
per hour of a spark task
Unlimited Users
Unlimited Applications
Logs & Metrics Monitoring
Single Sign On
VPN and VPC Peering Support
3rd Party Integrations
Slack & Community Support
14-Day Free Trial
A single pay-as-you-go plan
A fair pricing mechanism
Competing data platforms base their fee on server uptime, and charge you whether these servers are actually used by Spark or whether they’re sitting idle. Up to 80% of a typical bill originates from wasted compute infrastructure.
At Data Mechanics, we charge when you do real work, not when your servers are idle. This means we’re incentivized to make your infrastructure as efficient as possible and cut down the waste, so that you can reduce your cloud bill and its environmental impact.
We export the Spark event logs of each application running on the platform, and sum the duration of all the Spark tasks. It’s the same information that you can see on the Spark UI, reported by Spark with a millisecond accuracy.
If you don’t run any Spark command, there is no Data Mechanics fee. This can happen if you run a pure Python/Scala application from a notebook or submitted through our API.
You will still incur costs from the cloud provider though, so it’s a good idea to shut down a notebook once you’re done with your work, so that all the pods are destroyed and the Kubernetes cluster can autoscale down.
As soon as an application finishes, our dashboard provides you this information. For recurring applications (we call them "jobs"), you can also track the evolution of your Data Mechanics costs, along with other key metrics. Finally we produce a billing report with detailed information and useful cost attribution breakdown at the end of each month.
If you're currently on another Spark platform, you're probably using a small number of static cluster and Spark configurations for most of your workloads. You're likely to suffer from ressources overprovisioning, long periods of idleness and parallelization issues as in the graph below.
When these problems happens, other Spark platforms will charge you for the total server uptime, including the wasted compute time. At Data Mechanics, not only do we charge you only for the used compute time, we also tune your configurations automatically and continuously for each of your Spark applications to eliminate the waste altogether.
Some of our customers have reduced their costs by over 50% since they migrated to our platform.