Category: blog

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Cloud Cost Management with ML-based Resource Predictions (Part II)

As outlined in the Guidance Framework introduced in part one of this post, once users gain visibility of spending metrics, they must see opportunities to reduce monthly bills.

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Cloud Cost Management with ML-based Resource Predictions (Part I)

As enterprises go through digital transformation to improve efficiency, increase values and innovation, the adoption of cloud and Kubernetes has accelerated.

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Intelligent Autoscaling of Kafka Consumers with Workload Prediction

ProphetStor Federator.ai is an AI-based solution that helps enterprises manage and optimize resources for applications on Kubernetes.

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A Smarter, Cost-efficient Way to Provision Cloud Workloads with ProphetStor Federator.ai

ProphetStor Federator.ai is an AI-based solution that helps enterprises manage and optimize resources for applications on Kubernetes.

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Federator.ai – AI Solution for Auto-Scaling on Kubernetes with Datadog

With Datadog, our enterprise customers are now able to monitor their application workloads and get visibility into Kubernetes clusters of any scale. The new integrationbetween Federator.ai and Datadog has made it easier to deploy our service, collect and monitor any metrics (e.g. CPU and memory utilization, application latency etc) for applications running on Kubernetes. Similar to

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Application-Aware Federator.ai for Kafka on Kubernetes Enhances Performance and Reduces Cost

Introduction A Kafka Stream Application reads from a topic, Software-Defined Storage performs some calculations, transformations to finally write the result back to another topic. To read from a topic, it creates a consumer. Kafka has become a standard tool to manage a highly loaded streaming system. However, it does not provide mechanisms for dynamic cluster

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