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JFrog ML Feature Store
Sunnyvale, California, and Netanya, Israel
What it is
JFrog ML Feature Store centralizes the development, sharing and serving of machine-learning features to help keep training and inference data consistent. It is part of JFrog ML, which provides tools for building, deploying, managing and monitoring AI workflows, from generative AI and large language models to classic machine learning. Teams can define features once and reuse them across projects, using historical data retrieval for training and online values for inference. Feature sets may be batch, streaming or real-time, but the SaaS offering supports batch sets only; streaming and real-time sets require hybrid deployment. Documented batch sources include Snowflake, BigQuery, MongoDB, Amazon S3 files, Redshift, MySQL, Postgres, ClickHouse, Vertica and AWS Athena. Kafka and Kinesis are listed as streaming sources. Users can configure sources and feature sets through the SDK or CLI, or create data sources in the web UI. JFrog ML supports AWS, Google Cloud and Microsoft Azure, and deployment on JFrog's platform or customer infrastructure.
For and against
For
- Features can be reused across projects.
- Historical retrieval supports training data.
- Works with AWS, Google Cloud and Azure.
- Offers a trial.
- Supports deployment on customer infrastructure.
Against
- SaaS supports batch feature sets only.
- Streaming and real-time sets require hybrid deployment.
The verdict
JFrog ML Feature Store is aimed at teams that want shared feature definitions across training and inference workflows. Its SaaS scope is limited to batch feature sets, while streaming and real-time use requires hybrid deployment.
Who it suits
It is presented for data scientists, ML engineers and AI developers moving AI and machine-learning services into production. Teams needing streaming or real-time feature sets should note the hybrid deployment requirement.
Plans and prices
JFrog ML
Contact sales for pricing; current pricing page does not state a JFrog ML price
Read 8 Oct 2026 · jfrog.com
On the record
- Data integrations
- Documented batch data sources include Snowflake, BigQuery, MongoDB, Amazon S3 files, Redshift, MySQL, Postgres, ClickHouse, Vertica, and AWS Athena.docs.jfrog.com · read 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.jfrog.com · read 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.docs.jfrog.com · read 8 Oct 2026
- Feature Store
- The Feature Store centralizes development, sharing, and serving of ML features to keep training and inference data consistent.docs.jfrog.com · read 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.docs.jfrog.com · read 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.docs.jfrog.com · read 8 Oct 2026
- Intended users
- JFrog ML is presented for data scientists, ML engineers, and AI developers taking AI and ML services into production.jfrog.com · read 8 Oct 2026
- JFrog integration
- The Qwak integrations page lists JFrog Artifactory and Xray for model artifact management and scanning.qwak.com · read 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.docs.jfrog.com · read 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.jfrog.com · read 8 Oct 2026
- Security compliance
- JFrog's Trust Center lists SOC 2 Type II and ISO 27001 among its certifications and reports.jfrog.com · read 8 Oct 2026
- Streaming integrations
- The data sources page identifies Kafka and Kinesis as streaming sources.docs.jfrog.com · read 8 Oct 2026
- Trial
- The JFrog ML product page offers a Start a Trial option.jfrog.com · read 8 Oct 2026
