ML Registry Addresses AI Supply Chain Risks and Reliability Differentiation Emerges from AWS Outage
The News
At KubeCon North America 2025, Cloudsmith discussed its evolution from cloud-native developer-focused artifact management to enterprise security positioning, with messaging shift from artifact management as primary value to security as the “what” with artifact management as the “how,” reflected in new slogan “manage every artifact, secure every build” that positions security as primary benefit through visibility and policy control over software components.
Analyst Take
The company is successfully acquiring both innovative cloud-first developers and traditional enterprise customers. Cloudsmith has overcome security team objections that previously blocked adoption despite development team enthusiasm, improving security posture through hiring a Head of Security, building a security team, implementing a Vanta-powered trust center providing transparent on-demand access to security information via an AI bot, renewing all existing certifications, and prioritizing continuous security feature development to meet enterprise security team demands and compliance pressures.
Cloudsmith differentiates from competitors like JFrog and Sonatype through superior reliability, inclusive pricing, and better customer support, with customers citing poor JFrog support (slow ticket responses), frustrating add-on pricing for every feature, and operational inefficiencies versus Cloudsmith’s shared Slack channels and all-inclusive pricing.
A major differentiator emerged during a recent AWS US-East-1 outage when JFrog Artifactory customers experienced downtime and were asked to pay more for higher availability, while Cloudsmith remained operational, providing a powerful economic and business continuity argument. The company emphasizes translating practitioner benefits, better support, and efficiency into economic arguments for business decision-makers focused on total cost of ownership, time-to-value, and business impact of downtime.
Cloudsmith developed an ML registry based on Hugging Face for LLM models and datasets that includes vulnerability scanning by ingesting security data specific to ML models, helping customers secure their AI software supply chain by vetting components from public repositories like Hugging Face.
The company is developing an AI Copilot serving three use cases: improving developer experience by making packages easier to find and use, enabling DevOps engineers to configure Cloudsmith using natural language, and providing analytics on usage and trends for managers. Cloudsmith emphasizes awareness of AI security risks, including prompt injection and the importance of securing data, with messaging addressing how the platform helps companies comply with regulations like the EU’s Cyber Resilience Act (CRA).
Cloudsmith’s messaging pivot to “security-first artifact management” reflects pragmatic recognition that enterprise buying decisions prioritize risk reduction over developer productivity, even when practitioners drive initial adoption. Artifact repositories have traditionally been positioned as developer infrastructure for dependency management and build reproducibility, with security features treated as compliance checkboxes rather than primary value propositions.
The shift to “manage every artifact, secure every build” reframes artifact management as a security infrastructure that provides visibility and policy control over software supply chain components, aligning with increased executive and board-level attention to software supply chain security following high-profile incidents like SolarWinds, Log4j, and the 3CX supply chain attack.
However, the positioning depends on whether security teams recognize artifact management as a critical security control versus viewing it as development tooling with security features, and whether Cloudsmith can demonstrate measurable risk reduction that justifies investment beyond basic artifact storage and distribution.
The competitive differentiation from JFrog based on reliability, pricing, and support addresses genuine customer pain points, but these advantages may not be sustainable as competitors respond to market feedback. The AWS US-East-1 outage example, where JFrog customers experienced downtime while Cloudsmith remained operational, provides compelling proof point for reliability claims, but it also raises questions about architectural differences that enabled Cloudsmith’s resilience and whether JFrog’s multi-region failover capabilities justify premium pricing for customers with stringent availability requirements.
The criticism of JFrog’s add-on pricing model for every feature versus Cloudsmith’s all-inclusive pricing reflects broader SaaS market tension between usage-based pricing that scales with value and predictable pricing that simplifies budgeting, with customers increasingly resistant to nickel-and-diming for capabilities they view as table stakes. However, all-inclusive pricing also creates challenges for Cloudsmith as the platform adds capabilities, the company must determine which features remain included versus becoming premium add-ons without alienating customers who chose Cloudsmith specifically for transparent pricing.
The ML registry for AI supply chain security addresses an emerging need as organizations adopt LLMs and ML models from public repositories like Hugging Face without adequate vetting of security vulnerabilities, licensing compliance, or data provenance. The vulnerability scanning capability that ingests security data specific to ML models provides value beyond general-purpose artifact scanning, but the effectiveness depends on the maturity of ML-specific vulnerability databases and whether the security community develops standardized approaches to identifying and disclosing ML model vulnerabilities comparable to CVE databases for traditional software.
The positioning as “securing the AI software supply chain” aligns with regulatory trends like the EU Cyber Resilience Act that extend software supply chain security requirements to AI systems, but organizations must determine whether artifact repository-based controls provide sufficient governance or whether AI supply chain security requires additional capabilities around model behavior testing, bias detection, and output validation that artifact management cannot address.
The AI Copilot development, serving developers, DevOps engineers, and managers with different use cases, reflects the broader trend of embedding AI assistance into development tools, but it also creates questions about differentiation when every platform adds similar capabilities. The developer experience improvements (making packages easier to find and use) and natural language configuration for DevOps engineers address usability barriers that slow adoption, but the value depends on whether AI assistance actually reduces time-to-productivity or whether it introduces new failure modes when AI-generated configurations are incorrect or incomplete.
The analytics and trend insights for managers address visibility gaps, but organizations must determine whether artifact repository analytics provide actionable insights or whether they need broader software delivery metrics that span the entire toolchain. The emphasis on AI security risks, including prompt injection and data security, demonstrates awareness of concerns that may slow AI feature adoption, but Cloudsmith must balance adding AI capabilities that improve user experience against introducing new attack surfaces that undermine the security-first positioning.
Looking Ahead
Cloudsmith’s success with security-first positioning depends on whether the next 12-18 months validate that enterprises recognize artifact management as critical security infrastructure worth dedicated investment, or whether it remains development tooling with security features that organizations address through existing solutions.
The company must demonstrate measurable security outcomes, reduced vulnerability exposure, faster incident response, and improved compliance posture that justify artifact repository investment beyond basic functionality. The competitive differentiation based on reliability, pricing, and support provides near-term advantages, but sustainability requires continuous innovation in security capabilities, operational excellence, and customer experience that prevent competitors from closing the gap.
The ML registry for AI supply chain security positions Cloudsmith for emerging regulatory requirements and enterprise governance needs, but success depends on whether organizations adopt centralized artifact management for ML models or whether AI workflows remain fragmented with models sourced directly from public repositories, internal training pipelines, and vendor APIs.
The broader AI supply chain security market is evolving rapidly, with specialized solutions emerging for model scanning, bias detection, and runtime monitoring that may compete with or complement artifact repository-based controls. Cloudsmith must determine whether to expand beyond artifact management into broader AI governance capabilities or maintain focus on secure artifact distribution while integrating with specialized AI security tools. The challenge is proving that artifact management provides a foundational layer for AI supply chain security that justifies platform adoption, while the market determines whether comprehensive AI governance requires dedicated solutions versus extending existing software supply chain security practices to ML artifacts.
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