JFrog ML

What it does

JFrog ML takes trained or fine-tuned models, LLM applications, prompts, and feature data and turns them into deployed model services. Those services can run as live API endpoints, Kafka stream serving, or batch inference jobs, with monitoring and alerts included.

Teams use it to fine-tune models, serve them at scale, run A/B tests and shadow deployments, and manage features through a built-in Feature Store. Because it is part of the JFrog Platform, model artifacts can sit alongside software artifacts under the same security and governance controls. It deploys natively on AWS, Google Cloud, and Microsoft Azure, either on JFrog's cloud or on your own infrastructure.

It fits data scientists, ML engineers, AI developers, and DevOps and security teams that want ML work inside an existing software delivery process. Getting started involves booking a demo or starting a trial, and the most advanced security capabilities are in paid JFrog Platform tiers.

What it can do

• Train and fine-tune models, including building from scratch or adapting LLMs
• Deploy models as live API endpoints, Kafka streams, or batch inference jobs
• Run A/B tests and shadow deployments to evaluate model performance
• Manage features for training and inference with a built-in Feature Store
• Monitor deployed models with real-time monitoring and alerts
• Deploy across multiple environments, clouds, and regions

Works with

• AWS
• Google Cloud
• Microsoft Azure
• GitHub
• Jenkins
• Hugging Face

Before you choose

• Confirm JFrog ML usage allowances, infrastructure costs and any overage billing with the provider.
• Full ML model scanning and security features depend on the JFrog Advanced Security and Enterprise tiers

Listed under

• Coding
• DevOps and observability
• AI developers
• Data science
• Feature store
• LLM applications
• MLOps
• Model deployment

Get started

A demo booking option and a trial link are offered for JFrog ML.

1. Book a demo or start a trial through the JFrog ML page to access the platform
2. Connect your cloud provider (AWS, Google Cloud, or Azure) or choose JFrog-hosted infrastructure
3. Bring your model artifacts and feature data into the platform and configure deployment targets

Confirm the applicable plan, usage charges and commercial terms with the provider.

We couldn’t find published prices for JFrog ML. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.

• Streamline AI/ML Model Management | JFrog ↗jfrog.com
• Welcome to JFrog Docs ↗docs.jfrog.com
• Software Pricing and Packages | JFrog ↗jfrog.com

Company website

jfrog.com: product, pricing and policy pages

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knowledge_base

Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.