Feature Stores: Build, Buy, or Skip?
A practical guide to deciding when feature stores add value and when they're premature optimization.
The feature store promise
Feature stores promise to solve feature versioning, discovery, and serving consistency. But they’re not always the right answer.
When to build
Build a feature store when:
- You have 10+ models sharing features
- Feature computation is expensive and needs caching
- Multiple teams need feature discovery and governance
- You’re already building feature pipelines that could be centralized
When to buy
Buy a feature store when:
- You need enterprise-grade governance and compliance
- Your team lacks infrastructure expertise
- You need immediate feature serving at scale
- Budget allows for managed services
When to skip
Skip feature stores when:
- You have fewer than 5 models
- Features are simple and don’t need versioning
- Your team is small and communication is easy
- You’re still experimenting with ML use cases
The migration path
Start with a simple feature registry (a database + API). Add serving and versioning only when you hit real pain points.