GPU/Neocloud Billing using Rafay’s Usage Metering APIs
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
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Automate, Secure, and Optimize Kubernetes Operations at Scale
Managing Kubernetes clusters on-premises requires manual provisioning, scaling, and upgrades, leading to operational inefficiencies. Without automated workflows, organizations struggle with configuration drift, downtime risks, and increased maintenance efforts.
Private data centers must meet stringent security and compliance requirements while ensuring operational consistency. Enforcing access controls, network policies, and audit logging across multiple clusters without centralized governance increases security vulnerabilities.
Unlike cloud environments, private data centers have finite resources, making it challenging to scale clusters efficiently. Without intelligent resource management and workload optimization, organizations risk overprovisioning, underutilization, and degraded application performance.
Deploy production-ready clusters with automated workflows for consistency and efficiency.
Automate Kubernetes upgrades, patches, and add-ons with zero downtime.
Enforce RBAC, network policies, and compliance controls across all clusters.
Manage clusters across private data centers and hybrid clouds from a single control plane.
Deploy production-ready clusters with automated workflows for consistency and efficiency.
Prevent overprovisioning, improve workload efficiency, and track resource consumption.
Ensure business continuity with automated failover, backups, and recovery mechanisms.
There are numerous hurdles to building a Kubernetes solution on-prem. Rafay customers use a CNCF-compliant fully-managed Kubernetes distro in their data centers
Leverage turnkey Kubernetes lifecycle management for data centers and the edge, using our easy-to-use, fully CNCF-compliant Kubernetes distro.
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
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Agentic AI is the next evolution of artificial intelligence—autonomous AI systems composed of multiple AI agents that plan, decide, and execute complex tasks with minimal human intervention.
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Whether you’re training deep learning models, running simulations, or just curious about your GPU’s performance, nvidia-smi is your go-to command-line tool.
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See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!