Centralized Kubernetes Cluster Lifecycle Management for Private Data Centers

Automate, Secure, and Optimize Kubernetes Operations at Scale

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Challenges of Kubernetes Cluster Lifecycle Management in Private Data Centers

Complex Cluster Lifecycle Operations

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.

Security, Compliance, and Governance Risks

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.

Scalability & Infrastructure Bottlenecks

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.

See how Rafay simplifies private cloud Kubernetes

Why Choose Rafay for Kubernetes Cluster Lifecycle Management in Private Data Centers?

Automated Cluster Provisioning & Scaling

Deploy production-ready clusters with automated workflows for consistency and efficiency.

In-Place Upgrades & Patching

Automate Kubernetes upgrades, patches, and add-ons with zero downtime.

Centralized Security & Compliance Controls

Enforce RBAC, network policies, and compliance controls across all clusters.

Multi-Cluster & Hybrid Management

Manage clusters across private data centers and hybrid clouds from a single control plane.

Self-Service Access & GitOps Automation

Empower teams with self-service Kubernetes management via UI, API, CLI, and GitOps.

Optimized Resource Utilization & Cost Control

Prevent overprovisioning, improve workload efficiency, and track resource consumption.

Built-In High Availability & Disaster Recovery

Ensure business continuity with automated failover, backups, and recovery mechanisms.

Optimize Kubernetes for your private cloud today

Why companies use Rafay’s managed Kubernetes service in private clouds

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

Before

After

Before Build a highly available and scalable Kubernetes solution

After Rafay managed upstream K8s for bare metal and virtual (vSphere)

Before Maintain critical K8s components (CNI, CRI, Etcd, etc..)

After Rafay manages critical K8s components

Before Integration with core Infrastructure providers (Storage, Network and LB)

After -500 integrations available out of the box

Before K8s cluster upgrades, node OS patches & Cluster add-on updates

After Automated fleet-wide workflows for in-place upgrades

Before K8s lifecycle operations automation (provision, scale, update, etc..)

After Out-of-box TF, CLI, API, GitOps

Customer Results

52%

Less licensing and personnel costs

0

Management overhead

10x

Faster cluster and add-on upgrades and patches

Try Rafay Now

Free Yourself from Old Technology

Leverage turnkey Kubernetes lifecycle management for data centers and the edge,
using our easy-to-use, fully CNCF-compliant Kubernetes distro.

Private Cloud Suite

Latest Blogs from the Kubernetes Current

Powering GPU Cloud Billing: Rafay + Monetize360 Integration

June 16, 2025 / by Mohan Atreya

In the fast-evolving world of GPU cloud services and AI infrastructure, accurate, flexible, and real-time billing is no longer optional — it’s mission critical. That’s why Rafay has partnered with Monetize360 to deliver an end-to-end pricing, billing, and revenue management… Read More

Choosing the Right Fractional GPU Strategy for Cloud Providers

July 14, 2025 / by Mohan Atreya

As demand for GPU-accelerated workloads soars across industries, cloud providers are under increasing pressure to offer flexible, cost-efficient, and isolated access to GPUs. While full GPU allocation remains the norm, it often leads to resource waste—especially for lightweight or intermittent… Read More

Demystifying Fractional GPUs in Kubernetes: MIG, Time Slicing, and Custom Schedulers

July 11, 2025 / by Mohan Atreya

As GPU acceleration becomes central to modern AI/ML workloads, Kubernetes has emerged as the orchestration platform of choice. However, allocating full GPUs for many real-world workloads is an overkill resulting in under utilization and soaring costs. Enter the need for fractional… Read More

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