Do you need a feature store?
Not every team needs a feature store. The case for one grows with the number of models and features you run, the need to reuse features across them, and the cost of training-serving skew. For a single simple model, it can be overkill.
When you probably do not
If you run one model, with a handful of features you can recompute the same way in training and production, a feature store is more machinery than the problem needs. A well-tested shared library can keep the definitions in step.
When the case grows
The argument strengthens as you add models and features. Once several models reuse the same features, someone has to own those definitions, serve them live, and keep training and serving from drifting. That ownership is what a feature store is, whether you buy one or build it.
Build or buy
Building one is achievable. The cost is the second year: keeping the streaming pipeline current, handling late and out-of-order events, and staying on call for the serving path. Buying one trades that ownership for a bill. The deciding question is whether running feature infrastructure is work your team should own.
See it on your own traffic
Add the SDK, define one attribute, and see an attribute updating against your own traffic in the first session. The free tier runs on the real engine: no card, no sales call.