Most Teams Don't Need a Feature Store
I've watched three startups spend months migrating to Feast, Tecton, or Hopsworks only to revert back to Postgres within a year. The pitch is always compelling: centralized feature management, consistent serving, time-travel queries, automatic backfills. But when your model predicts churn for 50K users daily and you have 12 features from two tables, you're solving a problem you don't have.
Feature stores solve real problems at scale. Google's Zipline handles billions of features. Uber's Michelangelo powers thousands of models. But if you're not operating at that magnitude, the operational overhead outweighs the benefits. You're trading SQL you already understand for a new abstraction layer that breaks in surprising ways.
Here's the uncomfortable truth: most ML teams maintain fewer than 10 models in production, use under 100 features total, and serve predictions in batch jobs that run hourly or daily. For that workload, a well-indexed Postgres table with a cron job beats a feature store on every metric that matters: development speed, operational simplicity, debugging clarity.
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