Alternatives
Products that do what nKS - nOps Karpenter Solution does
Reduce your EKS cost by up to 60% on auto-pilot
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2019 · github.com
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Hey HN: Kaveh here, the founder of https://www.usage.ai/ We help companies drive down AWS EC2 spend. Why? Because the way it's done now is a pain. DevOps and Software Engineers end up spending time managing costs rather than focusing on business problems. Previous to founding Usage, I worked on high-performance computing research at JP Morgan Chase and as a software engineer at a number of smaller startups. Here's how it works: We are typically brought in by a DevOps manager to cut AWS EC2 costs. The app is entirely self-service and the savings are generated automatically,…
2022 · usage.ai
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Hey HN: I'm Kaveh, the founder of Usage (https://www.usage.ai/) We help companies drive down AWS costs. Why? Because the way it's done now is a pain. Stakeholders, especially engineers, are required to spend unnecessary time manually finding underutilized or overly expensive EC2s. We believe the optimization process should be done automatically through a series of sophisticated algorithms. At the moment, there are over 70,000 AWS EC2 prices - doing that manually just won't scale at most organizations. My background is in software engineering. Previous to founding Usage, I…
2020
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2020 · technicallywizardry.com
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2018 · github.com
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2020 · github.com
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CAST AI (https://cast.ai) has built a cloud optimization platform that reduces AWS cloud costs 50% to 90%, optimizes DevOps, and automates disaster recovery via multi-cloud with a single cluster. Intelligent optimization engine delivers a cost-efficient, high-performing, and resilient infrastructure for every Kubernetes workload. If it sounds too good to be true - try free. To make it easier CAST AI provides AWS and GCP cloud credentials for free. Visit https://cast.ai Here's how it works: 1.Use CAST AI to deploy your K8s clusters. Next, take a look at the CAST AI…
2021
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2021 · cloudshim.com
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2021 · vantage.sh
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2019 · github.com
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2019 · github.com
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Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
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2014 · optkit.com
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