AI-Driven Capacity Management

IT middle management is constantly trying to find the right balance between making the required resources available and trying to stay within budget restrictions. In an increasingly uncertain world, AI can help them in this task by enabling them to create accurate and effective forecasts that predict weeks and months in advance.

AI based IT capacity management

Capacity planning requires managers to estimate the compute, storage, network, and software required for business operations. As practically all cloud providers charge on a consumption level, proper capacity planning is required to prevent escalation of costs. With AI-based time series analysis of key resource usage, your business always knows what's needed, where's it needed and for how long.

Benefits of AI-driven Capacity Management

Optimum Utilization

AI can ensure that enterprises cut down on the cost of underutilized resources. Modern cloud architectures have created situations where drawing additional resources is relatively easy. This can lead to wastage due to high resource usage from incorrectly-sized deployments. AI ensures that they do not create mismanaged deployments that lead to wastage of enterprise resources. Instead, AI models can optimize usage of resources by automating resource allocation based on need.

Optimum Utilization of resource

Better Demand Planning

AI analyses the usage of resources over the past several months and goes over all aberrations to create a highly specialized forecast for every KPI you're tracking. This enables managers to understand resource demand weeks or even months ahead of time. Due to this, they can adequately plan any scale up activities to meet higher demand in times of peak load on resources. Ensure your business always meets its operational needs in times of necessity.

AI-enabled demand planning

Greater Visibility

Multi-cloud operations are increasingly common in large enterprises. As organizations switch freely between different cloud providers based on requirements, provider-specific tools become less relevant and less useful for the overall picture. It becomes harder to definitively track and compare utilization across multiple providers in a meaningful way, especially as you go up the responsibility chain. AIOps circumvents this by providing utilization metrics and data of all systems and predicting them. This enables risk mitigation as services are split between providers but also enables monitoring where your resources are never over-provisioned or under-provisioned.

End-to-end visibility
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