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Simplify ML Compute: Scale Across Clouds and On-Prem
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Unified Compute Management for Every ML Workflow
Outerbounds turns compute complexity into simplicity. Manage multi-cloud, on-prem, and hybrid resources from one platform while optimizing costs and scaling effortlessly. By integrating compute with the entire ML lifecycle, Outerbounds ensures your resources adapt to the needs of experimentation, training, and deployment without unnecessary overhead.
Multi-Cloud and On-Prem Support
Use the Resources You Trust
Outerbounds works with AWS, GCP, Azure, and Slurm clusters, letting you leverage existing infrastructure while maintaining control. Bring your own cloud or mix and match seamlessly.
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Cost Optimization Built-In
Maximize Efficiency, Minimize Spend
Outerbounds provides detailed cost reports and recommendations to reduce cloud spending. Autoscaling ensures you’re only using the resources you need.
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Scalable and Flexible Workflows
From Small Experiments to Foundation Models
Easily allocate CPU or GPU resources with Python decorators, scale automatically based on workloads, and train distributed models across thousands of GPUs without reconfiguring workflows.
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Simplified Compute Management
Abstract Complexity, Focus on Results
Built on Kubernetes and integrated with Slurm, Outerbounds abstracts the complexity of compute orchestration, giving data scientists powerful tools without operational burdens.
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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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Trusted by the best
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