Cloud Native AI
Infrastructure Solutions —
Built for Flexibility and Scale

There are several key features of an AI and Machine Learning infrastructure solution.

• Scalability: Supports large-scale deployments of AI applications, LLM, and ML models across hundreds or thousands of clusters with centralized control.

• Data Pipeline Management: Tools for ETL, versioning, and real-time data feeds.

• Networking: Fast and low-latency networking across multi-cluster environments.

• ML Framework Integration: Seamless support for popular ML frameworks like TensorFlow, PyTorch, Keras, etc..

• Orchestration and Resource Management: Managing workloads, scheduling jobs, and allocating resources efficiently.

• Monitoring and Observability: Real-time monitoring of model performance, hardware utilization, and errors.

• Security and Compliance: Encryption at rest and in transit, access controls, and adherence to industry standards.

Mirantis k0rdent AI supports multicloud and hybrid deployments with an open, composable architecture that enables consistent AI infrastructure operations across cloud (including AWS, Azure, and GCP), on-prem, and edge environments.

Mirantis supports training, fine-tuning, and inference workloads across LLMs, computer vision, and generative AI by using cloud-native tools and accelerated pipelines for scale and performance.

Unlike cloud-first AI infrastructure vendors that primarily deploy to public clouds like AWS, Azure, and GCP, Mirantis offers open-source solutions that can be deployed wherever needed, including public clouds, on-premises, hybrid clouds, and edge locations. These capabilities increase control, flexibility, and portability across environments and remove vendor lock-in.

Yes, Mirantis is an enterprise-grade AI infrastructure platform that enables GPU cluster deployment across clouds, data centers, or the edge. Our platform offers tools for scheduling, monitoring, and optimizing AI performance at scale.