Financial Technology
Use Case:

How Stash Cut Pipeline Compute Costs by 67% with Flyte™

Frustrated with its “homegrown” pipeline management, New York fintech company Stash compared popular workflow orchestration platforms. The Flyte™ engine behind Union cut provision and model execution time — and slashed costs. 

Stash’s personal finance app helps people invest small and grow wealth, bank, budget and save, and get financial advice. Its ML team helps modelers and researchers protect the system from fraud, optimize model execution, and lower compute costs and time to market.

Slash ML platform engineer Katrina Palen said the company had been using “a homegrown process” to manage its pipeline and realized it needed a more efficient, less costly solution. Her team chose Flyte™ over Kubeflow, Prefect and Airflow. Stash had many researchers with different coding styles and levels of familiarity with structuring, and Flyte™ delivered a good experience for all. 

“We want to make writing a workflow for people's jobs pleasant and straightforward. … [I]f it’s not simple and easy, people aren’t going to adopt the technology, so this one was a really big one for us,” Palen said.

The result? With Flyte™, Stash cut its provision time by 50%, reduced its model execution time by 65% and lowered its compute costs by 67%. Those results don’t yet include any savings from the caching or data set management capabilities that allow resources to be provisioned by task. “So, over time we’re expecting to save even more,” Palen said.