Feast
6.1#3 in feature store softwareFree planFree planWebAPILinuxSelf-hosted
VisitFeast vs JFrog ML Feature Store
JFrog ML Feature Store
5.7#9 in feature store softwareWebAPILinuxSelf-hosted
VisitWhat both publish
| Fact | Feast | JFrog ML Feature Store |
|---|---|---|
| Deployment | Feast can be deployed on Kubernetes, where feature servers and scheduled or ad-hoc jobs can run as Kubernetes workloads. | JFrog ML supports AWS, Google Cloud, and Microsoft Azure, and offers deployment on JFrog's platform or on a customer's infrastructure. |
| Intended users | The quickstart identifies data scientists, MLOps engineers, data engineers, and AI engineers as users Feast is designed to serve. | JFrog ML is presented for data scientists, ML engineers, and AI developers taking AI and ML services into production. |
Feast plans
- FeastFree
JFrog ML Feature Store plans
- JFrog MLPriced on request
Pick Feast if…
Feast is designed for data scientists, MLOps engineers, data engineers, and AI engineers managing features for AI or machine-learning applications. It suits teams that need batch and real-time serving or point-in-time-correct training data.
Pick JFrog ML Feature Store if…
It suits data scientists, ML engineers, and AI developers bringing AI and ML services into production. Teams needing streaming or real-time feature sets should account for the hybrid deployment requirement.