Chainguard secures AI platform stacks

This points to a powerful distribution wedge, sell once to an AI platform vendor, then secure every model training and inference workload that vendor runs for its own customers. In practice, Chainguard is not just replacing a base image for one app, it is getting embedded into the standard containers underneath platforms like Cloudera, where data science teams pull PyTorch, Conda, and other ML building blocks that are usually slower to patch and harder to audit than mainstream web software.

• Cloudera made the segment real, not theoretical. In October 2025 it announced that Chainguard secured its Data and AI platform and cut CVE footprint by more than 90%. Cloudera had already been moving multiple cloud data services onto Chainguard based images earlier in 2025, showing this was platform level adoption, not a one off pilot.
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• AI platforms are a particularly good fit because their software stacks are messy. A single deployment can bundle Linux packages, Python libraries, CUDA or NVIDIA drivers, orchestration tools, and older open source data components. Chainguard’s product is built for exactly this job, pre hardened images for PyTorch, Conda, and GPU environments, rebuilt continuously so the platform operator does not have to chase fixes package by package.
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• This customer type also scales economically better than selling image by image to individual app teams. Chainguard already serves more than 150 paying customers and grew from about $5M ARR in 2023 to $40M by January 2025. Winning AI infrastructure vendors can compound that growth because each platform standardization can flow through many downstream enterprise deployments.
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The next step is a broader move from securing developer containers to securing the default runtime for enterprise AI. As more companies centralize models, pipelines, and governance inside a few AI and data platforms, the vendor that becomes the secure base layer for those platforms can turn infrastructure security into a high leverage channel for long term expansion.