DEVOPSdigest

JFrog announced general availability of its integration with NVIDIA NIM microservices, part of the NVIDIA AI Enterprise software platform.

The JFrog Platform is a unified, end-to-end, and secure DevSecOps and MLOps solution with native NVIDIA NIM integration. This enables rapid deployment of GPU-optimized, pre-approved machine learning (ML) models, and large language models (LLMs) to production with enterprise-grade security, increased visibility, and governance controls. This unified infrastructure enables developers to create and deliver AI-powered applications with greater efficiency and peace of mind.

“The demand for secure and efficient AI implementations continues to rise, with many businesses aiming to expand their AI strategies in 2025. However, AI deployments often struggle to reach production due to significant security challenges,” said Gal Marder, Chief Strategy Officer at JFrog. “AI-powered applications are inherently complex to secure, deploy, and manage, and concerns around the security of open-source AI models and platforms continue to grow. We’re excited to collaborate with NVIDIA to deliver an easy-to-deploy, end-to-end solution that enables companies to accelerate the delivery of their AI/ML models with enterprise-grade security, compliance, and provenance.”

The JFrog integration with NVIDIA NIM enables enterprises to seamlessly deploy and manage the latest foundational LLMs – including Meta’s Llama 3 and Mistral AI – while maintaining enterprise-grade security and governance controls throughout their software supply chain. JFrog Artifactory – the heart of the JFrog Platform – provides a single solution for hosting and seamlessly managing all software artifacts, binaries, packages, ML Models, LLMs, container images, and components throughout the software development lifecycle. By integrating NVIDIA NIM into the JFrog Platform, developers can easily access NVIDIA NGC – a hub for GPU-optimized deep learning, ML, and HPC models. This provides customers with a single source of truth for software models and tools, while leveraging enterprise DevSecOps best practices to gain visibility, governance, and control across their software supply chain.

The JFrog Platform update provides AI developers and DevSecOps teams with multiple benefits, including:

- Unified ML & DevOps Workflows: Data Scientists and ML Engineers can now version, secure, and deploy models using the same JFrog DevSecOps software development workflows they already know and trust. This eliminates the need for teams to use separate ML tools while ensuring automated compliance checks, audit trails, and governance of ML Models using JFrog Curation.

- End-to-End Security & Integrity: Implement continuous security scanning across containers, AI models and dependencies – delivering contextual insights across NIM microservices – to identify vulnerabilities, supplemented by smart threat detection that focuses on real risks and proactive protection against compromised AI models and packages.

- Exceptional Model Performance and Scalability: Optimized AI application performance using NVIDIA accelerated computing infrastructure, offering low latency and high throughput for scalable deployment of LLMs to large-scale production environments. Easily bundle ML models with dependencies to reduce external requirements and utilize existing workflows for seamless AI deployment. Additionally, the JFrog Platform offers flexible deployment options for increased scalability, including self-hosted, multi-cloud, and air-gap deployments.

“Performance and security are crucial for successful enterprise AI deployments,” said Pat Lee, vice president, Enterprise Strategic Partnerships, NVIDIA. “With NVIDIA NIM integrated directly into the JFrog Platform, developers can accelerate AI adoption with a unified, end-to-end solution for building, deploying, and managing production AI agents at scale.”