One of the biggest reasons why companies hesitate to adopt the cloud is because they do not have a precise understanding of what their environments will look like once they’re in the cloud. How can they be sure their performance requirements will be met? How can they be certain the cost of operating their infrastructures in the cloud will be lower than hosting them on-premises?
To increase and accelerate cloud adoption, cloud service providers and systems integrators need to assure companies that moving to the cloud will result in their performance requirements being met at a lower cost than on-premises. The only way to instill this confidence is to precisely predict what seems to be unpredictable - TCO and compute and storage performance in the cloud. Fortunately, automated analytics that include benchmarking and right-sizing can do this accurately and quickly, as well as provide the level of detail required to settle a company’s hesitation.
Predicting Cloud TCO
In a previous post we wrote on calculating TCO, The Two Cloud TCO Mistakes You Didn’t Know You Were Making, we show that if you calculate cloud TCO by seeing what it would cost to forklift a company’s existing infrastructure into the cloud in what’s known as a “like-to-like” comparison, you’ll get an inaccurate estimate that does not account for the lower capacity that companies need once they migrate to the cloud. That’s why these TCO calculations result in a higher TCO, a common scenario that scares companies away from the cloud.
Instead of performing a like-to-like comparison, leverage a TCO calculator that automates performance analysis and cloud benchmarking to identify a company’s right-sized cloud (its optimal compute and storage options for each workload) and then calculates TCO based on this. When basing TCO on actual usage, you can avoid making an inaccurate estimate that may vastly overstate the costs of migrating on-premises workloads to the cloud.
Predicting Compute and Storage Performance in the Cloud
Predicting performance in the cloud requires translating on-premises compute and storage performance metrics to the cloud, and identifying the cloud compute and storage options that will meet these performance requirements. This, however, relies on benchmarks. Compute benchmarks provide an understanding of what every compute option in the cloud can handle in terms of CPU and memory. Storage benchmarks provide what each storage option can handle in terms of IOPS, disc capacity and occupancy, and throughput. Benchmarks are not available for the public cloud, and creating them manually is tough, if not nearly impossible. Fortunately, automated analytics can do this quickly and accurately.
At Cloudamize, we combine on-premises performance analysis, customer preferences, and cloud benchmarking to identify the compute and storage options in the cloud for each on-premises workload that will meet that workload’s performance requirements. Of those, the platform finds the option that will be the least expensive. Thus, companies can see exactly what each workload’s performance and cost will be in the cloud.
Use Automated Analytics to Accelerate Cloud Sales Cycles
At the end of the day, no whitepaper, no analyst’s report, and no news article is enough to help clients gain the confidence they need to move to the cloud. What they need is a fact-based analysis that the decision is right for their organization and a precise understanding of what cost and performance will look like once they migrate. The only way to get this level of precision is with automated analytics - and it’s why automated analytics help accelerate cloud sales cycles.