Azure is emerging as a strong contender in the cloud market, and is the logical choice for many organizations that leverage Microsoft technologies on-premises. But, the way these services are consumed in the cloud can be quite different than in legacy environments, and purchasing the right mix of services and resources can be very complex. Without the right analytics, it is nearly impossible to ensure that what is being purchased actually matches what is needed by the application workloads.
Densify’s Cloud-Learning Optimization Engine—Cloe—uses advanced machine learning to model workload demand patterns and precisely match these demands with the optimal Azure resources. By continuously aligning resource supply and application demand, Densify simultaneously reduces risk and increases efficiency in Azure environments. And, because Densify’s recommendations are accessible via API, Dev teams can easily incorporate them into their application manifests, enabling their cloud applications to leverage machine learning to constantly optimize themselves.
The result is next-generation, self-optimizing applications. Densify breaks down silos between Dev, Infrastructure & Ops, and Finance, enabling seamless, data-driven collaboration. By embedding code snippets into their apps that enable them to become self-aware and self-optimizing, Developers are freed from the burden of determining granular resource requirements. App teams can receive API-based optimization services from Ops teams, achieving a safe and efficient environment without having to go through cumbersome processes to perform optimization. And the Finance organization benefits from the much higher cost efficiency that comes with decisions that are powered by machine learning.