In this blog, Product Manager, Niall comments on the recent Flexera™ 2021 State of the Cloud Report. In particular, he discusses the current challenges around licensing models and the cloud.
Flexera’s recent 2021 State of the Cloud Report has stirred a lot of discussion. As usual it is chock-full of cloud adoption statistics and insights.
One table especially captured my attention – the top challenges of software in the cloud.
The challenges listed range from understanding the cost of licensing, to knowing when licenses are being used in the cloud.
On a more reflective glance, to me, these challenges really speak to a licensing model that should be more applicable to on-premise or ‘long running’ applications. In these scenarios, licenses are purchased on an allocation basis and assigned to the relevant users and/or departments.
This got me thinking about the cloud and how organisations can scale workloads.
When one thinks about burst or compute-intensive workloads, creating on-demand, cost efficient clusters in the cloud makes sense. The ideal scenario is to avoid having to ‘pre-invest’ in the ‘high water’ mark for the cluster, for it to then sit idle until the next burst or workload.
The same should theoretically apply to the software licenses you may need to use. Like compute, the license purchase model should preferably be on-demand, flexible and paid for based on consumption. Otherwise, those licenses are left idle, waiting for the next burst. In short, the license model should be based on what you consume, rather than what you allocate.
Although this challenges the current status quo, it does not necessarily mean software vendors lose out. In fact, the increased flexibility and consumption of licenses in short, large bursts may in fact increase licensing revenue.
While ultimately that is a conversation for software vendors and customers, the key takeaway for cloud users is, you will only be as scalable as your least scalable resource.
To learn more about scaling workloads in the cloud, get in contact with our team.
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