Case Study

Dataloop leverages DoiT Flexsave to optimize compute engine rates regardless of usage fluctuations

Software, Technology
Google Compute Engine, Machine Learning

Dataloop benefits from Google Cloud Commited Use Discounts for its data management, data pipelines and annotation platform even with varying compute demands

The ever-changing world of visual recognition technology (i.e. visual AI) is powered by amounts of unstructured data so huge that preparing them for AI is a long, arduous and expensive process. Dataloop strives to solve this problem by helping companies build and deploy powerful visual data pipelines to prepare data for machine learning by labeling data, automating data ops, customizing production pipelines and weaving in human-in-the-loop for data validation. Processing large, unpredictable amounts of unstructured data continuously meant Dataloop’s cloud usage needed extensive optimization.

The brief

One of the most significant parts of the Dataloop platform is the data operations engine, which powers their pipeline creation, automation and Function-as-a-Service features. This is what allows their customers to blend code, data, etc. into one cohesive, harmonic pipeline. Using this pipeline, customers can then insert triggers or filters that split information into tasks — annotate, validate, train a model with, pass into a different dataset or create an automation.

This level of data processing requires a great deal of cloud computing power, but forecasting that usage proved to be an impossible task given Dataloop customers’ unpredictable needs. One day, a customer might be completely silent as they build their pipeline, but the next they might upload over a million items or have 1,000 annotators on the platform. These fluctuations make it challenging for Dataloop to determine how much compute power they’ll need in the following week, let alone in future months.

Given that uncertainty, purchasing a 1-year or multiyear CUD from Google Cloud becomes very risky, so Dataloop purchased one with a conservative commitment threshold. However, internal cloud optimization efforts lowered their compute needs to about 70% of their CUD commitment, which in turn prompted underutilization fees. Essentially, they were being penalized for becoming more efficient.

Dataloop needed a way to manage their on-demand instances that would accommodate their customers’ usage bursts but would keep cost effectiveness.. An ideal solution would be easy and quick to implement, thus freeing time to work on actually expanding and growing the company’s service offerings.

What we did

To help solve these challenges, Dataloop turned to Flexsave, DoiT International’s cloud savings solution that automates savings without any backend configuration. Using automation and machine learning, DoiT is able to cover much of Dataloop’s on-demand workloads that aren’t already optimized via their existing commitment with Google Cloud.

Perhaps most importantly, they were able to turn on Flexsave with a single click and then leave it on autopilot, freeing up huge chunks of time that would otherwise have been spent on manual management of their cloud rate optimization efforts.

Now, rather than shuffling compute around all day, Dataloop’s engineering team can focus more on their own development efforts while still enjoying 30% savings on their on-demand instances. Since enabling Flexsave, over 80% of Dataloop’s on-demand workloads are covered by DoiT’s solution, thus allowing them to scale up and down as needed, depending on their customers’ fluctuating usage.

The result

  • Eliminated all underutilization penalties from their CUD
  • Realizes benefit from CUDs on over 80% of on-demand workloads without signing additional commitments
  • Automatically gets the best rate for compute usage, regardless of fluctuations
Koby Molcho, VP of Research & Development, Dataloop
“Unpredictable bursts in user activity add an extra layer of complexity when managing our Compute Engine spend. Thanks to Flexsave, our compute costs are stabilized even in the face of dynamic usage. This allows us to focus on our product, knowing our unit costs around compute are optimized.”

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