JFrog

Optimize distribution and consumption of artifacts across complex topologies with private edge nodes and CDN

Extend SDLC across multiple locations by automatically syncing artifacts across federated repositories with one-way and bi-directional replication

Run user-defined automated policies to ensure repository hygiene with logged and auditable cleanup runs

Deploy multiple redundant nodes to ensure continuous uptime, load balancing, and failover support

Ensure the integrity of releases through to consumption with immutable multi-technology Release Bundles and permission-based distribution hubs

Control access to JFrog sites with IP and geo filtering while optimizing performance via DNS routing

Simplify auditing, optimize performance and deliver data retention compliance with automated, policy-based, long-term archiving as a service

Leverage A/B testing and shadow deployments for enhanced evaluation of model performance

The single source of truth for your AI ecosystem enabling organizations to rapidly adopt AI with governance, security, and compliance

Core functionality for data scientists and engineers including experiment tracking, model training, and model/build registry

Take models to production with real-time/batch streaming, model monitoring, observability, drift monitoring, and analytics

Advanced model registry allowing teams to store all model data, files, and components together for simplified versioning, traceability, and distribution

Store, manage, and serve machine learning features for training and inference ensuring consistency, reusability, and discoverability

The world's most advanced AI model registry providing the only lifecycle solution for AI/ML discovery, creation, governance, and deployment

Monitor and analyze model performance and behavior in production including runtime metrics and analytics

Deploy model services across multiple cloud providers

Deploy model services across multiple environments for consistency across development, staging, and production

Ensure high availability and low latency for global users by deploying models across different geographic regions

Accelerate code delivery and reduce risk through real-time AI-automated source code and dependency vulnerability remediation

Continuously monitor for and remediate CVEs throughout application lifecycle even post-release with prioritized remediation

Software Composition Analysis to identify and resolve security vulnerabilities (CVEs) and license compliance issues in open-source dependencies for both source and binary code

Static Application Security Testing to identify code vulnerabilities

Security analysis with contextual information for better vulnerability remediation

Infrastructure as Code security scanning for configuration vulnerabilities

Safeguard developer environments by validating and governing third-party extensions, eliminating hidden risks in the toolchain

Identify and protect against insecure configurations

Modern code-to-runtime security with contextualized and agent-ready scanners natively integrated where code and artifacts live

Detect and identify leaked secrets in code and artifacts

Extend package scanning capabilities to ML models to identify vulnerabilities, malicious models, license issues, and operational risk

Block malicious packages from entering your organization with automated, policy-based OSS package curation

Extend Runtime Integrity to provide workload and process level insights including tracing issues back to the developer owner

Monitor and ensure the integrity of running applications

Gain applicability information along with a clear remediation path for transitive dependency CVEs