Mastering the Cloud Economy in the Age of AI Adoption
The acceleration of artificial intelligence (AI) adoption has had significant impacts on the enterprise cloud economy. As companies invest heavily in AI, they must also focus on strategically managing escalating cloud costs to stay competitive in this transformative era of AI. In this article, we look at the steps companies can take to navigate the economic terrain of cloud computing.
Governance and process optimization
In the cloud economy, cost proliferation poses a significant challenge due to the lack of effective governance. To address this, companies must take proactive steps in establishing a robust governance framework for their AI services. This involves defining a predetermined set of services tailored to the specific needs of the organization, coupled with establishing clear service level agreements (SLAs). These SLAs outline performance metrics, availability, and support for each service, ensuring transparency and accountability in the use of AI resources.
To improve the efficiency of deploying AI workloads, organizations can use landing zone templates. Configured for different tasks such as custom AI models, NLP, speech and vision recognition, these templates provide a consistent basis for resource deployment.
Additionally, the integration of automated onboarding and offboarding processes can be implemented to minimize manual intervention and errors. Organizations should also establish a standardized chargeback and pricing mechanism that provides transparent tracking of AI service costs and enables informed decisions based on resource consumption patterns. Additionally, implementing a structured invoice reconciliation process ensures financial transparency by quickly monitoring and addressing billing discrepancies.
Head of Cloud Consulting and Engineering Services, EY UK.
Cloud resource tracking and optimization
Deploying AI assets can be simple, but managing them wisely reduces the total cost of ownership. By enforcing tagging best practices, companies can logically group resources for effective tracking, providing insight into the purpose and ownership of each resource, helping with efficient cost allocation and management.
When developing custom AI models, it is critical that you have the right scope of resources for optimization. Adjusting CPU or GPU cores, optimizing SKUs, and fine-tuning database, storage, and networking configurations aligns resources with real-world requirements, eliminating unnecessary costs.
Training AI models can be resource intensive. Embracing containerization (e.g. Kubernetes) and serverless computing provides flexibility in managing AI workloads efficiently.
For customers’ AI development, factors such as spot/reserve instances, license cost optimization via Bring Your Own License (BYOL) and cloud parking or energy planning in development and test environments can lead to significant cost savings. In addition, optimizing AI services such as vision and NLP based on specific requirements, such as face detection, OCR, landmark identification, object detection, speech-to-text and text-to-speech, should be tailored to the usage volume for efficient use of resources. .
Organizations should look to optimize a number of cost management and network monitoring tools that can help manage costs across multiple cloud platforms.
FinOps tools can provide real-time visibility into spend. This allows organizations to monitor and control costs more efficiently and help teams understand the financial impact of their cloud operations so they can make informed decisions to optimize resource usage.
Continuous monitoring of expenditure levels is essential to identify potential cost overruns or unexpected expenses. By proactively monitoring and setting alerts, organizations can set thresholds and receive notifications when spend approaches or exceeds predefined limits, helping them control costs before they escalate. A cloud cost reporting dashboard provides a centralized view of cost-related metrics and trends. It consolidates data from various cloud services and presents it in an easy-to-use interface, and enables stakeholders to analyze spending patterns, identify cost drivers and make informed decisions about resource allocation and optimization.
Another option is to model different scenarios to assess the potential impact on costs. This analysis can help predict demand, allowing better preparation for changing business requirements.
Subscription optimization
This includes managing the number of data analytics and AI landing zones across environments, including development, testing, route to live (RTL) and live production to ensure resources are provisioned based on actual demand. Organizations must tailor subscription levels to the specific needs of each environment to achieve cost-efficient use of resources.
Training the IT staff
Effective cost management requires collaboration between development and operations teams. Providing cost management training for these teams increases awareness of cost implications and ensures best practices for cost optimization. Creating a cost-conscious IT community promotes a culture of financial responsibility and ensures that cost considerations are an integral part of decision-making processes.
Contract optimization of cloud service providers
As organizations increase the use of AI services, renegotiating enterprise agreements with cloud service providers becomes essential. Changes in consumption patterns can lead to shifts in cost structures and by renegotiating contracts, organizations can tailor agreements to their changing needs, potentially securing more favorable terms.
In an age of exponential AI adoption, finding the perfect balance between technological innovation and cost efficiency is no small feat; However, with the right strategies, organizations can successfully navigate the evolving landscape of the cloud economy.
We’ve listed the best cloud cost management services.
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