JFrog ML Feature Store
JFrog ML Feature Store is part of JFrog ML, a toolkit for building, deploying, managing, and monitoring AI workflows, from generative AI and large language m…
What JFrog ML Feature Store is
JFrog ML Feature Store is part of JFrog ML, a toolkit for building, deploying, managing, and monitoring AI workflows, from generative AI and large language models to classic machine learning. The feature store gives teams a shared place to develop, serve, and reuse ML features, helping keep the data used for training and inference consistent. Teams can define features once for multiple projects, use historical values for training, and retrieve online values for inference. Feature sets may be batch, streaming, or real-time, although JFrog ML SaaS supports batch sets only; streaming and real-time sets require hybrid deployment. Data sources and feature sets can be configured through the SDK or CLI, while data sources can also be created in the web interface. Listed batch sources include Snowflake, BigQuery, MongoDB, Amazon S3, Redshift, MySQL, Postgres, ClickHouse, Vertica, and AWS Athena; Kafka and Kinesis are listed for streaming. Deployment supports AWS, Google Cloud, Microsoft Azure, JFrog’s platform, or customer infrastructure. Models can be served as API endpoints, batch jobs, or Kafka stream applications. Pricing is paid, with a trial offered; no price is listed.
Good
- Reuse feature definitions across projects.
- Historical retrieval supports training workflows.
- Online values support inference workflows.
- Deployment supports AWS, Google Cloud, and Azure.
- Models can be served in three listed modes.
Watch out
- SaaS supports batch feature sets only.
- Streaming and real-time sets require hybrid deployment.
- No price is listed.
Who it suits
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.
Verdict
JFrog ML Feature Store brings feature development, reuse, and serving into a broader AI workflow platform. Its deployment options and integrations are useful to production teams, but streaming and real-time feature sets require hybrid deployment.
Plans and prices
From JFrog ML Feature Store's own pricing page, checked 8 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| JFrog ML | Priced on request | Contact sales for pricing; current pricing page does not state a JFrog ML price |
The facts
Each one read from JFrog ML Feature Store's own pages, with the date it was checked.
What it does
- Data integrations
- Documented batch data sources include Snowflake, BigQuery, MongoDB, Amazon S3 files, Redshift, MySQL, Postgres, ClickHouse, Vertica, and AWS Athena.checked 8 Oct 2026
- Deployment
- JFrog ML supports AWS, Google Cloud, and Microsoft Azure, and offers deployment on JFrog's platform or on a customer's infrastructure.checked 8 Oct 2026
- Feature reuse
- Teams can define features once and reuse them across projects, with offline historical retrieval for training and online values for inference.checked 8 Oct 2026
- Feature Store
- The Feature Store centralizes development, sharing, and serving of ML features to keep training and inference data consistent.checked 8 Oct 2026
- Feature types
- Feature sets can be batch, streaming, or real-time, but JFrog ML SaaS supports batch feature sets only; streaming and real-time feature sets require hybrid deployment.checked 8 Oct 2026
- Feature workflow
- Users can define data sources and feature sets with the JFrog ML SDK or CLI, or create data sources in the JFrog ML UI.checked 8 Oct 2026
- Intended users
- JFrog ML is presented for data scientists, ML engineers, and AI developers taking AI and ML services into production.checked 8 Oct 2026
- JFrog integration
- The Qwak integrations page lists JFrog Artifactory and Xray for model artifact management and scanning.checked 8 Oct 2026
- Model serving
- Models can be deployed as live API endpoints, batch inference jobs, or Kafka stream applications, with gradual deployment and A/B testing described in the product documentation.checked 8 Oct 2026
- Product scope
- JFrog ML provides tools to build, deploy, manage, and monitor AI workflows from GenAI and LLMs to classic machine learning.checked 8 Oct 2026
- Security compliance
- JFrog's Trust Center lists SOC 2 Type II and ISO 27001 among its certifications and reports.checked 8 Oct 2026
- Streaming integrations
- The data sources page identifies Kafka and Kinesis as streaming sources.checked 8 Oct 2026
The company
- Founded
- 2008checked 28 Sep 2026
- Headquarters
- Sunnyvale, California, and Netanya, Israelchecked 28 Sep 2026
Money
- Trial
- The JFrog ML product page offers a Start a Trial option.checked 8 Oct 2026
