JFrog
The single source of truth for the agentic software supply chain
About JFrog
JFrog is a software supply chain platform built around Artifactory, a universal private registry for binaries, containers, packages, and AI models. The JFrog Platform adds Xray security scanning, Curation, Advanced Security, Distribution, AppTrust governance, and MLOps tooling, available as SaaS or self-managed.
Pros & Cons
Pros
- Supports 30+ package types in a single registry, so teams can consolidate Maven, npm, Docker, PyPI, and more
- Xray and Curation catch vulnerable or malicious dependencies before they reach builds
- Mature CI/CD integrations, REST API, and JFrog CLI fit into most existing pipelines
- Available as SaaS, self-managed, hybrid, and multi-cloud
- Federation features handle multi-site and multi-region enterprises well
- Adds governance for AI models and AI coding agents alongside traditional artifacts
Cons
- Expensive for small teams once security add-ons are included; most security features start at Enterprise X ($950/month)
- Steep learning curve and complex setup for advanced features
- UI can feel complex for newcomers
- Self-managed deployments with advanced features are resource-heavy
- Consumption-based pricing makes storage and transfer costs harder to predict
Key Features
Artifactory universal registry
Stores and proxies binaries for 30+ package types, including Maven, npm, PyPI, Docker, Helm, Go, and NuGet, in one system of record.
Container registry
Scalable private Docker/OCI registry with unlimited Docker Hub pulls through remote caching.
JFrog Xray (SCA)
Continuously scans code and binaries for vulnerabilities and license issues, with policies that can block risky artifacts from builds.
JFrog Curation
Vets open-source packages before they enter the organization and blocks malicious or non-compliant dependencies at the registry.
Advanced Security
Contextual analysis, secrets detection, IaC scanning, and snippet detection on top of Xray, plus zero-touch remediation that swaps in patched package versions.
AI model registry and AI Catalog
Manages ML models and AI assets alongside software artifacts, scans models for security issues, and detects shadow AI use.
Agent governance
JFrog Agent Plugin and Agent Guard extend security policies into AI coding agents, restricting which skills, MCP servers, and tools they can consume.
Repository and platform federation
Synchronizes artifacts, projects, access, and policies across multiple sites, regions, and clouds.
AppTrust (DevGovOps)
Release gates with prebuilt CRA and NIST compliance frameworks and prompt-to-release evidence traceability.
CI/CD integrations and JFrog CLI
REST API, Artifactory Query Language, JFrog CLI, and 100+ integrations with CI servers, IDEs, and cloud platforms.
Pricing
- Limited-time offer (list price $150/month)
- Artifactory universal binary management
- 25 GB base consumption
- Scalable container registry
- Unlimited Docker Hub pulls
- AI model registry and AI asset repositories
- 3 projects
- Community support
- Everything in Pro
- 125 GB base consumption
- 99.9% uptime and 24/7 SLA support
- Repository federation and automated cleanup
- Enterprise access control (SSO)
- Code and binary SCA (Xray)
- 30 projects
- Hybrid and multi-cloud
- Everything in Enterprise X
- Custom consumption
- Multisite federation and distribution
- 99.99% uptime guarantee option
- Platform federation and private CDN
- AppTrust governance (bundle)
- High-touch 24/7 support
Best For
Central artifact repository
Engineering organizations replace scattered package registries with one Artifactory instance that proxies public repos and hosts internal builds.
Software supply chain security
Security teams use Xray, Curation, and Advanced Security to scan dependencies and binaries and block risky packages at the registry.
Container image management
DevOps teams host private Docker/OCI images and cache Docker Hub pulls to avoid rate limits.
Compliance and release governance
Regulated companies use AppTrust release gates and evidence chains to stay audit-ready for CRA and NIST requirements.
MLOps and AI asset governance
ML teams store, scan, and deploy models while platform teams control which AI assets and agent tools developers can use.
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