What is RazorOps?

RazorOps is an AI-powered platform that continuously optimizes Kubernetes clusters for peak performance at the lowest possible cost. By analyzing real-time workload behavior and predicting demand, RazorOps automatically rightsizes resources, eliminates waste, and ensures your infrastructure runs at maximum efficiency. Purpose-built for Kubernetes, RazorOps helps teams to:

Intelligent Rightsizing

Automatically tune CPU and memory requests/limits to match actual workload needs — eliminate overprovisioning instantly.

Predictive Scaling

Forecast traffic surges before they happen using AI behavioral modeling — scale proactively, not reactively.

Cost Optimization

Reduce cloud bills by up to 60% through autonomous resource management, spot instance balancing, and waste elimination.

Performance Guardrails

Maintain SLOs and uptime guarantees while optimizing — never sacrifice application performance for cost savings.

Whether you're a growing startup running a handful of clusters or an enterprise managing hundreds, RazorOps adapts to your infrastructure and delivers measurable savings from day one. Stop paying for idle Kubernetes capacity — let AI handle the optimization so your team can focus on building great products.

Our Mission

At RazorOps, our mission is to make Kubernetes infrastructure intelligent, efficient, and cost-effective for every team. We are dedicated to building an AI platform that autonomously optimizes performance and eliminates wasted cloud spend — so engineering teams can focus on innovation instead of infrastructure tuning. We strive to be the trusted partner in every organization's journey toward efficient, high-performance Kubernetes operations.

Our Story

RazorOps was born out of a shared frustration: teams running Kubernetes were consistently overpaying for cloud infrastructure while still experiencing performance issues. Despite the promise of container orchestration, most clusters run at less than 40% utilization — wasting resources, burning budgets, and leaving performance gains on the table.

The founding team — comprising DevOps veterans, infrastructure engineers, and AI researchers — came together with a bold goal: to build an intelligent system that could understand Kubernetes workloads deeply and optimize them autonomously. No more manual tuning of resource requests and limits. No more guessing at capacity planning.

The early days were spent deep in Kubernetes internals, building behavioral models that could predict workload demand and rightsize resources in real time. With their combined expertise in distributed systems and machine learning, the team developed RazorOps — an AI platform that continuously learns, predicts, and optimizes Kubernetes infrastructure safely and automatically.

What sets the RazorOps team apart is their relentless commitment to listening to platform and SRE teams. From startups to large enterprises, every piece of feedback has shaped the product into a solution that balances cost efficiency with performance guarantees — never sacrificing one for the other.

Today, RazorOps is more than an optimization tool — it's an autonomous intelligence layer for Kubernetes that helps teams reduce cloud costs by up to 60%, improve resource utilization, and maintain peak performance without lifting a finger. The RazorOps story is one of solving real infrastructure pain with AI-driven automation.