Independent Survey Sponsored by Anodot Points to Need for AI-Based Cloud Monitoring and Machine Learning to Reduce Cloud Costs Within 30-60 Days
Anodot, the autonomous business monitoring company, announced the results of an independent survey that reveals how organizations struggle to control skyrocketing cloud computing costs of the remote workforce, even as business moves to a hybrid model.
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“Cloud costs are extremely hard to track”
In Q2 of 2021, Anodot surveyed more than 100 senior IT, finance, and operations leaders on their experiences managing cloud costs during the pandemic and shortly thereafter as vaccinations became commonplace and more people returned to work. The survey revealed the following:
- Cloud costs are skyrocketing, and most organizations are having a hard time controlling these costs
- Fewer than 20% of survey respondents stated that they were able to immediately detect spikes in cloud costs
- Greater than 25% stated that it can take months or weeks or several days to notice a spike in cloud costs
- For 59% of those who spend in excess of $2M monthly on cloud usage, it can take days to detect an anomalous surge; and, on heavy cloud usage days, nearly 50% of respondents reported that cloud costs can surge by as much as 10-19%
- Business spending more than $2 million on cloud costs had less insight into cloud costs than companies that spent less than $2 million
- Roughly 77% of respondents with more than $2 million in cloud costs said they were surprised by how much they spent
- About 60% of respondents admitted that it took them at least a few days to detect anomalous surges, which can easily equate to $100,000s in unnecessary revenue loss
- This delay increased cloud costs by a staggering 10%
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- 2020 was a particularly challenging year for managing cloud costs, primarily due to the move to remote work
- Nearly 30% of respondents saw a 25-50% jump in cloud costs, month-to-month, during a six-month period
- Almost 20% realized a 50-100% increase in cloud costs, month-to-month, during a six-month period
- This year, many organizations experienced a challenging or somewhat challenging experience when transitioning business-critical operations to the cloud
- Nearly 30% experienced a very rough or challenging transition
- Only 10% experienced a very smooth transition
For most organizations, cloud services and Software-as-a-Service represent a large and fast-growing share of their budgets. Cloud computing is projected to make up 14% of enterprise IT spending worldwide in 2024 – up from 9% in 2020, according to a recent report by research firm Gartner. This will continue a trend. Gartner says that worldwide spending on public cloud services will grow 18% this year alone to a total of $304.9 billion, up from $257.5 billion in 2020.
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“Cloud costs are extremely hard to track” according to Anodot Co-Founder and CEO David Drai, who said this makes it challenging for IT, finance, and operations teams to manage cash flow and set reasonable expectations for cloud usage. “Undetected mistakes often account for rising cloud costs and those glitches are not found by traditional monitoring tools used by most organizations. Given the rise in cloud costs due to digital transformation and a shift to hybrid workforce models, it is incumbent on IT leaders to use the correct tools to monitor their cloud costs.”
Using traditional approaches to business monitoring for cloud costs can take days as well as waste valuable time for the engineers who need to review dashboards. Anodot’s AI and ML tools speed time to detection by 70 percent, and many companies can identify cost-related issues within an hour, saving businesses hundreds of thousands of dollars. As cloud costs take up an increasingly large percentage of companies’ IT spending in the move to digitalization, the speed of detection and remediation will be especially critical to financial planning.
AI-Based Cloud Monitoring and Machine Learning Are More Effective
“Within one month of deploying an AI solution, a company can cut cloud costs by 10% and provide long-lasting results that improve IT operations,” said Drai. “AI-based cloud monitoring and machine learning are the most effective ways to control cloud costs, offering the ability to detect and resolve spikes in cloud usage before significant expenses are incurred. This is the most accurate technology for problematic usage before they take a toll on revenues.”
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