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JFrog ML

Place
#11 of 36
Score
6.1

The facts below are what the maker publishes about JFrog ML, checked and dated. Our editors have not written about it yet.

The facts

Each one read from JFrog ML's own pages, with the date it was checked.

What it does

Alerts
JFrog ML alerting documentation describes integrations with Slack, Opsgenie, and PagerDuty for model and feature-set alerts.checked 3 Oct 2026
Audience
The product is intended for data scientists, ML engineers, and AI developers bringing AI and ML services into production.checked 3 Oct 2026
Certifications
JFrog's Trust Certificate Program lists SOC 2 Type II, SOC 3, IRAP, ISO 27001, ISO 27701, ISO 27017, TISAX, CSA STAR Level 1, Cyber Essentials, and KY3P by S&P Global.checked 3 Oct 2026
Cloud providers
The product page states that JFrog ML natively supports AWS, Google Cloud, and Microsoft Azure.checked 2 Oct 2026
Clouds
JFrog ML states native support for AWS, Google Cloud, and Microsoft Azure, with deployment on JFrog's platform or customer infrastructure.checked 3 Oct 2026
Deployment
Models can be served as API endpoints, used for batch inference, and A/B tested.checked 3 Oct 2026
Deployment options
JFrog ML Cloud offers cloud deployment, while its hybrid architecture allows data and models to remain in a customer's cloud environment.checked 2 Oct 2026
Feature pipelines
JFrog ML supports batch, streaming, and real-time feature transformations with end-to-end pipeline visibility.checked 2 Oct 2026
Feature Store
The Feature Store supports batch, streaming, and real-time feature sets, but JFrog ML SaaS supports batch feature sets only; real-time and streaming feature sets require a hybrid deployment.checked 3 Oct 2026
Integrations
JFrog's integration catalog includes Hugging Face, NVIDIA, Amazon SageMaker, and Azure Machine Learning integrations.checked 3 Oct 2026
Lifecycle
The platform supports data preparation, model building and training, deployment, monitoring, and pipeline automation.checked 2 Oct 2026
MLOps bundle eligibility
The pricing page says Unified MLOps is available for Enterprise X and Enterprise + subscriptions, while Ultimate MLOps is available for Enterprise + subscriptions.checked 2 Oct 2026
Model deployment
Models can be deployed as REST endpoints, batch transformation jobs, or streaming applications.checked 2 Oct 2026
Model registry
JFrog ML uses Artifactory as a model registry for versioning, traceability, and governance, and describes Xray scanning models, containers, and artifacts for vulnerabilities and license compliance issues.checked 3 Oct 2026
Model training
The product page says users can train or fine-tune models, including building models from scratch.checked 2 Oct 2026
Observability
Its production observability features include performance metrics, model feature management, and inference analysis and queries.checked 2 Oct 2026
Product
JFrog ML is an all-in-one solution to build, deploy, manage, and monitor AI workflows, from GenAI and LLMs to classic machine learning.checked 3 Oct 2026
Production testing
JFrog ML supports gradual model deployment and A/B testing in production.checked 2 Oct 2026
Purpose
JFrog ML is a unified platform for building, deploying, and monitoring machine learning models in production.checked 2 Oct 2026
Security and governance
JFrog describes JFrog ML as bridging AI/ML and DevSecOps and bringing AI development into an existing secure software development lifecycle.checked 2 Oct 2026
Security controls
JFrog's self-managed AI/ML service uses a frontend on the customer's JFrog Platform instance and a backend hosted by JFrog; enabling it requires a direct internet connection to bridge.ml.frog.io and an Enterprise+ license with AI Catalog entitlement.checked 3 Oct 2026

The company

Company
JFrog says its Liquid Software journey began in 2008; an official JFrog-hosted company profile lists its founding year as 2008 and headquarters in Netanya, Israel, and Sunnyvale, California.checked 3 Oct 2026
Founded
2008checked 28 Sep 2026
Headquarters
Sunnyvale, California, and Netanya, Israelchecked 28 Sep 2026

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