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A Better Alternative to Kubeflow: Simplified, Scalable, and Python-First
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A Better Way to Leverage Kubernetes for ML
Kubeflow makes Kubernetes accessible for ML, but Outerbounds makes it seamless. By abstracting complexity, Outerbounds enables data scientists to define workflows in Python, avoid YAML, and focus on ML development. With a fully managed platform that integrates compute, orchestration, and tracking, Outerbounds delivers faster time-to-value and a unified MLOps experience—all without the operational overhead.
Simplified Kubernetes Integration
Python Workflows, No YAML
Define workflows entirely in Python while Outerbounds abstracts Kubernetes complexity. No YAML configs or deep Kubernetes expertise needed—just efficient, accessible ML workflows.
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Fully Managed Platform
Infrastructure, Handled for You
Outerbounds deploys, updates, and manages your Kubernetes-based ML platform. Spend less time on infrastructure and more time on delivering impactful ML projects.
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Unified and Scalable MLOps
From Experimentation to Production
Outerbounds combines orchestration, tracking, and deployment into one integrated platform. Scale seamlessly across teams and clouds without piecing together tools.
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Cloud-Agnostic and Cost-Efficient
Optimize Resources Across Clouds
Avoid vendor lock-in with multi-cloud support and built-in tools for optimizing compute spend. Scale your ML workflows without unnecessary overhead or reconfiguration.
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Start building today
Drop in any Friday at 9am PT for an open Q&A with our team. Whether you're curious about Outerbounds or have specific questions — nothing is off limits.
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