Overview
CloudKnife provides analysis and automation tools for better cloud decisions across multi-cloud environments, with deepest support currently on Azure. The platform is designed for platform and DevOps leaders to make defensible, review-ready optimization decisions without sacrificing engineering ownership.
Key people
In the news
- This is what we like to see. Finding savings is easy. Making recommendations people actually trust enough to execute is the hard part. Almost 70% acted on already. We’ll take that. 🔪
- Finding a cheaper SKU is the easy part. But what if the workload runs SAP and the proposed SKU is not certified or supported? A useful recommendation needs to consider workload behaviour, software requirements, operational constraints, production risk and migration effort. CloudKnife is being built to turn theoretical savings into actions teams can confidently take. Fewer recommendations. Better decisions. #FinOps #CloudOptimisation #CloudEngineering
- Cloud optimisation is not only about identifying waste. Teams also need enough context to understand where action is required and what they should do next. This customer insight captures exactly why we are building CloudKnife: to turn cloud signals into clear, explainable actions while keeping teams in control. #Azure #FinOps #CloudOptimisation #CloudKnife
- Cloud cost visibility is useful. But knowing where the money went is not the same as knowing what to do next. A responsible optimization decision needs technical context, evidence and a clear understanding of risk. That is the problem we are solving at CloudKnife. #Azure #FinOps #CloudOptimisation #CloudKnife
- This is exactly the kind of detail that makes a recommendation trustworthy rather than just plausible. Most tools don't go near burstable SKU suggestions because the credit behaviour makes it hard to get right. Showing the baseline directly on the CPU chart is what lets your engineer make the call with confidence instead of opening four more tabs to verify it. Small feature. Big difference in how fast a decision gets made.
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