Anaconda has acquired Outerbounds and its metaflow-based AI/ML orchestration framework. With this move, Anaconda says it provides a managed path for AI-native development at enterprise scale.
Anaconda has acquired Outerbounds, developer of the Metaflow open source AI/machine learning orchestration and deployment framework. This will provide companies and organizations with a “managed path” for AI development from experimentation to full-scale operations, Anaconda said.
Anaconda, which provides one of the leading platforms for data science, machine learning, and AI enterprise development, touted the acquisition as a “significant step” in its evolution to provide an integrated platform across the entire AI-native software development lifecycle.
Anaconda said that with the addition of Outerbounds, customers can benefit from an end-to-end enterprise stack that includes managed AI model deployment, production-grade agent workflows, and trusted software distribution and environments.
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“For years, Anaconda has served as a trusted foundation for AI and data science in development, and this acquisition is a natural next chapter,” Anaconda CEO David DeSanto said in a statement. “The future belongs to AI-native development, where AI models are core to how applications are built, rather than something bolted on at the end.”
“The challenge that businesses face today is that in order to realize that vision, they need to piece together tools, platforms, and governance components that were never designed to work together or work together with AI,” DeSanto said. “Until now, no other platform has covered the entire AI-native development lifecycle. With Anaconda and Outbounds, for the first time, enterprises can securely scale complex, composite AI systems from idea to production on the infrastructure they already trust.”
In a news release announcing the acquisition of Outerbound, Anaconda said AI-native applications are “fundamentally different” from traditional software because AI models are at the core and applications are “non-deterministic, agent-driven and exponentially more complex.”
Citing a survey of more than 1,100 developers by code verification firm Sonar, Anaconda said AI-generated code now accounts for 42% of all new code. However, AI-written code produces 1.7 times more defects than human-written code, and 80% of dependencies recommended by AI coding have known risks.
As a result, Anaconda says, the bottleneck in the software development process is no longer writing the code, but managing everything the code depends on across a distributed infrastructure to deliver reproducible, secure, and consistent results.
Open source Metaflow was born from within Netflix to handle demanding AI/ML workloads. Built on Metaflow, commercial Outbounds provides end-to-end orchestration, experiment tracking, artifact management, and scalable compute across cloud systems, data platforms, and hybrid environments with access to modern GPUs.
Metaflow is used by engineering teams within companies such as GE HealthCare, Warner Brothers, and Realtor.com.
With over 50 million users, Anaconda provides developers with secure packages, verified dependencies, a trusted environment, reproducible builds, and curated open source AI models. With the addition of Outerbounds to Anaconda’s offerings, customers will now have an end-to-end enterprise AI stack that incorporates workflow orchestration, compute management, experiment tracking, and enterprise governance in a single platform, Anaconda said.
Anaconda said it is committed to the continued development and support of Metaflow as an open source project, and that Anaconda engineers will continue to contribute to Metaflow along with the Anaconda platform. Metaflow is available under Apache License version 2.0.
“Joining Anaconda is a moment that Outerbounds is working toward,” Outerbounds co-founder and CEO Ville Tuulos said in a statement. “Anaconda has spent more than a decade earning the trust of some of the world’s largest enterprises, and that trust is the foundation our customers need to confidently deploy AI systems into production.”
“What makes this combination so powerful is our shared commitment to Python, reproducibility, and software engineering best practices,” Tuulos said. “Together, we can give data scientists and AI engineers everything they need to move from secure environments to production-level orchestration and turn AI innovations into real, measurable outcomes.”
