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The acceleration of artificial intelligence (AI) adoption has had significant implications for enterprise cloud economics.
In this article, we look at the steps business can take to navigate the economic terrain ofcloud computing.
To address this, businesses must take proactive steps in establishing a robust governance framework for their AI services.
To improve the efficiency of AI workload deployment, organizations can adopt landing zone templates.
Additionally, integration of automated onboarding and offboarding processes can be implemented to minimize manual intervention and errors.
Head of Cloud Consulting and Engineering Services, EY UK.
When developing custom AI models, it’s crucial to right-size resources for optimization.
Training AI models can be resource intensive.
Embracing containerization (e.g., Kubernetes) and serverless computing offers flexibility in managing AI workloads efficiently.
FinOps tools can provide real-time visibility into spending.
Continuous monitoring of spending levels is essential to identify potential cost overruns or unexpected expenses.
A cloud cost reporting dashboard provides a centralized view of cost-related metrics and trends.
Another option is modelling different scenarios to assess the potential impact on costs.
This analysis can aid in demand forecasting, enabling better preparedness for changing business requirements.
Organizations should align subscription levels with the specific needs of each environment to achieve cost-efficient resource utilization.
Training the IT workforce
Effective cost management requirescollaborationbetween development and operations teams.
Conducting cost management training for these teams builds awareness of cost implications and instils best practices for cost optimization.
We’ve listed the best cloud cost management services.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
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