The secret to your AI/ML success
Reaching your ROI is no longer a challenge when you use JFrog ML for all your AIÂ Infra and MLOps needs. Pay only for what you use with our affordable plans.
Free
Best suited for dev environments
Contact UsUp to 100 QPU/month for a year
Unlimited model versions on JFrog ML model registry
Train and deploy on scalable resources
Transform, store and manage features in the Feature store
Automatic inference and training pipelines
Monitor models and features
Model A/B testing and traffic splitting
Up to 1GB in-memory and 10GB storage
Chat support
Pay As You Go
Best suited for productions environments
Contact UsEverything in Free, plus:
Unlimited QPU
Multiple environments
<2 hours SLA for critical cases
99.8% uptime guarantee
Multi A-Z installation (DR)
Enterprise
Best suited for enterprise environments
Contact UsEverything in Pay As You Go, plus:
Volume discounts for pre-commitments
Self hosted hybrid deployments
SSO login
RBAC support
Dedicated support channel
<1 hour SLA for critical cases
<2 hours for important cases
Dedicated JFrog ML architect hours/month
2 annual on-site workshops
99.9% uptime guarantee
Private VPC installation
Storage
The JFrog ML platform contains two types of storage. Super fast in memory storage which is primarily used for online feature store, and warehouse storage, used for offline feature stores, model analytics, and monitoring.
Chosen by the world's best MLÂ team
FAQs
The platform is made for data scientists and AI practitioners who are looking to eliminate dependency on engineering resources and ML engineers who need someone else to take care of the heavy lifting so they can focus on their business’ needs.
We're a usage-based platform measured in QPUs, an internal processing unit we use at JFrog ML. We charge for build and deployment time, along with the resources you choose to use.
QPU, or JFrog ML Processing Unit, is our internal processing unit. To see how QPU’s are calculated, please see our documentation.
All Python based models are supported.
We support all common data sources, such as Snowflake, Kafka, BigQuery, RedShift, SQL, etc. We do not support data sources that are on-premise. See our documentation for the full list of supported data sources.
JFrog ML takes the confidentiality and integrity of its customer data very seriously and strives to ensure that data is protected from unauthorized access and is available when needed. Additionally, our assumption is that all data added into the platform by our customers is potentially PII / sensitive data, and is therefore treated as such.
Say goodbye to complex AI/ML
Chat with us to see the platform live and discover how we can help simplify your AI/ML journey.
