Optimizing cloud costs is a challenge and major pain point for businesses. Because of this, various services and solutions in the cloud market are looking to use artificial intelligence (AI) to offer cloud cost reduction.
This blog post will explore ways in which artificial intelligence can help reduce overall cloud spending and will introduce several use cases of AI and machine-learning-based[EK1] cloud cost optimization.
It is important to note that cloud cost optimization is not something that can be taken care of at the early stages of cloud adoption. For a cloud cost management strategy to be effective, a business needs to continuously review its cloud cost spending.
Addressing cloud efficiency challenges
This is where artificial intelligence technologies such as deep learning and machine learning can come in handy to reduce costs for a user’s next cloud bill. Possible uses of artificial intelligence in cloud cost cloud optimization include: addressing cloud efficiency challenges (such as resource management), minimizing operational waste, and utilizing predictive analyses for various purposes.
In other words, AI-based cloud management tools can be used as intelligence software automation tools to help make business-level decisions, predict potential issues, and reduce operational overhead for IT teams.
Artificial intelligence can even help detect cost spikes. Existing automation tools have already been used to automatically turn off resources when not in use, which helps save costs. When the same automation features are amplified with artificial intelligence, resource management is handled more intelligently so that resources that you do need are not mistakenly switched off.
Resource management for cloud cost savings
Introducing artificial intelligence technologies into the mix can help identify unused resources more quickly, automatically scale resources, and more. Machine learning can be used to collect data from built-in cloud monitoring services, train algorithms, and then fine-tune the newly created machine.
This means an artificial intelligence machine can figure out when a system needs more resources and then automatically scale to meet increased demands. The AI machine will learn when resources are no longer needed and reduce usage to minimize cloud costs.
A useful example of this would be managing the high cost of implementing high availability (HA) in the cloud. This is because high availability uses the same types of resources to achieve a desired level of performance.
While most cloud service providers allow their customers to provision resources dynamically, these capabilities are not designed for HA purposes or with cost savings in mind. This results in IT teams needing to employ manual processes – hence the need for artificial intelligence (AI) capabilities to further reduce cloud costs.
Minimizing cloud operational waste
AI can also help with predictive analysis for cloud spending when it comes to cloud operations. One real world example is with the healthcare industry. Managing computing equipment in a hospital, which typically requires the use of expensive IT and cybersecurity resources, is a notable way in which AI supports operations.
Another example of AI use is in the gaming industry, where AI can assist in reducing operational costs. Graphics processing units (GPU) play an important part in high-performance computing since GPUs are designed to handle visually intensive tasks when processing digital graphics.
When building gaming PCs, for example, and choosing cost-effective cloud servers in terms of the number of virtual CPUs and storage, artificial intelligence can reduce operational waste and, as a result, cloud costs. Companies like Vast AI use artificial intelligence to simplify the process of GPU rentals in order to reduce cloud compute costs.
Artificial intelligence (AI) for predictive analyses
AI can reduce cloud costs by proactively predicting performance issues and resolving them before they become issues for businesses. For example, an AI machine can learn from a set of historical data that logs uptime and downtime and learn to identify when a server or system will go down in the future. In the long run, this can translate to cloud cost reductions since downtime can get expensive.
AI technologies can also be used to predict cloud spending. Grumatic, a cloud cost optimization tool, monitors cloud spending every hour and then uses AI to analyze usage data to predict spending. With its real-time monitoring and alerts, Grumatic also provides alerts on anomalies. [ADD MORE HERE]
Conclusion
Ultimately, businesses must realize the potential of artificial intelligence to go beyond human capabilities in performance fine-tuning. There are just too many variables in cloud computing to manually handle services for cost optimization.
As mentioned previously, automated tools and application performance management (APM) tools do exist, but they are able to only scratch the surface of cloud cost reduction. Moving forward, artificial intelligence will continue to disrupt cloud operations and infrastructure management in positive ways as discussed with all of the possibilities with AI-based cloud cost optimization.
