We all know the above lists of VM configurations. The question is how you translate these VMs into the right Azure VM. To match large numbers of legacy VMs in an easy way with the appropriate Azure VMs, we added the column GBRAM / CPU. (See the blog What the GBRAM/Core ratio of a VM indicates for more info about this topic).
The GBRAM / Core ratio makes it possible to classify VMs and match them with the appropriate Azure VM series based on this. I hereby adhere to the following distribution:
If we apply this classification of VM categories to the above list of VMs, then they match with the following Azure VM series:
To now make an accurate estimate of what it would cost to run the customer’s workloads on Azure with the same sizing as the current infrastructure, add the matching aggregated price per VM series and multiply by the number of GBRAMs associated with it. (For more information on Azure VM series aggravated pricing: Making easy price estimates with an aggregated Azure VM-series price
This will look like this:
With this method you are able to quickly give a reliable price estimate of the Azure costs for an infrastructure of comparable size the customer currently have in use; the so called ‘lift & shift’ scenario. This price level is a point of recognition for the customer, assuming the customer knows what the comparable monthly costs are of his current infrastructure.
After this “lift & shift” step, the Azure infrastructure is optimized through right-sizing, snoozing and the use of reserved instances. This, if done properly, will lead to a substantial cost reduction in Azure usage.
CloudLabs Smart Azure Calculator works on the method described above. This enables us to quickly make accurate Azure cost calculations in a lift & shift scenario, but also provides insights into how the monthly Azure costs can be reduced through optimizations of the original infrastructure.
If your are interested in more, see the video https://youtu.be/X3SGvIzha-4