Managed AI Infrastructure
AI Operations
Running your AI infrastructure for speed and cost.
Scalability
Automatically scale AI workloads based on demand, optimizing resource use and reducing costs.
CI/CD Integration
Support automated pipelines, rolling updates, and rollbacks, ensuring continuous deployment of AI models without downtime.
Resource Management
Efficient allocation of CPU, GPU, and memory resources, essential for AI workloads.
Isolation for Experimentation
Provide isolated environments for data scientists to develop and test models.
High Availability
Ensure continuous operation through self-healing and multi-region/multi-cloud deployments.
Storage and Data Management
Support various storage solutions and complex data pipelines for managing large datasets.
Cost Optimization
Optimize resource usage and leverages cost-saving options like spot instances in public clouds.
Enterprise Ready
Security, Privacy + Compliance
Implement role-based access control and network policies to protect sensitive AI models and data. Utilize LLMs within private cloud environments ensuring data privacy for HIPAA, SOC 2 Type II and other privacy frameworks.
AI on Kubernetes
Cloud agnostic AI Infrastructure
Run your AI models using PyTorch, Tensorflow, or other open source frameworks on top of Kubernetes using the open source Kubeflow platform.
Hybrid-AI Cloud
Reduce Costs of AI Workloads
Building your AI infrastructure so that it can run within you AWS, Google, Azure cloud as well as on bare metal using our datacenter partners helping reduce the cost of your AI infrastructure.
Jumpstart Your Kubernetes Infrastructure