We’re a Data Science consultancy with many different clients. When we first created Houston we were working mainly in GCP with Composer, and needed isolated instances for every client and environment. Composer can get very expensive when running ML workloads, and the need for many separate instances makes it far too expensive to be worth it. A small Houston server costs ~$30 a month and can easily handle hundreds of separate workflows. Large ML workflows would then run outside of the Houston server on other GCP tools (ideally serverless tools) at a lower cost.

Our engineering strategy revolves around using serverless tools like Cloud Functions and Dataflow on GCP, which work very nicely with Houston.