FinOps and AI: Balancing innovation and cost efficiency

Accelerated adoption of artificial intelligence (AI) is fuelling rapid expansion in both the amount of stored data and the number of processes needed to train and run machine learning models. This is driving an unprecedented hunger for extra compute power. To manage this power effectively, companies depend on cloud infrastructures that can handle giant processes at scale. Operating expenses have skyrocketed as a result. For IT leaders, balancing must-have AI-powered innovation in the cloud with cost efficiency poses a massive challenge.

AI’s impact on cloud costs – managing the challenge

AI and machine learning drive up cloud computing costs in various ways. It takes huge volumes of data and a lot of computing resources to train a high-quality AI model. Once that’s done, the data used for model training needs to be stored. The fast pace of AI development also means that models require continuous improvement and retraining to keep up with the latest technological innovations.

Additionally, AI and ML workloads often require specialised hardware (e.g. GPUs, NPUs and TPUs), which is much more expensive than standard computing resources and needs special associated infrastructure for deployment and maintenance. This pushes costs up even more.

Clearly, the computational needs and resource usage of AI workloads are often at odds with the budgetary constraints imposed on companies. That’s not all. Fluctuating workloads – computing multiple images in parallel, for example – which may call for significant computational power at one point and less at another, can make it hard to know exactly how much will be needed and when.

Companies must manage all of this while maintaining the performance and efficacy of AI models and planning resources effectively to minimise expenses.

AI and ML offer solutions to their own cost challenges

The good news? Despite putting real pressure on costs, AI and ML also provide a powerful way to deal with those pressures – in both the short and long term.

Whether it’s driving automation or optimising cloud resource use, AI is integral to providing efficient and cost-effective solutions. One example? AI-powered optimisation algorithms can dynamically adjust resource levels by leveraging usage patterns and performance metrics to provide computing power when it’s needed and scale it back when demand is low.

They can even dynamically scale capacity to match changing needs, dramatically reducing the costs associated with over-provisioning and under-provisioning.

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AI-powered predictive models are essential to forecasting peak usage and scaling resources. By analysing historical data to identify trends, a model can predict future demand, which can help companies prepare for spikes in resource utilisation and avoid costs for resources that go unused during low-demand periods.

What’s more, AI-driven FinOps can provide real-time cloud cost monitoring and management, allowing organizations to better understand and manage their consumption of cloud resources. This helps them gain visibility into their usage and identify opportunities for cost savings.

Positioning the business for sustainable success

The advent of FinOps and AI is injecting a whole new dimension into cost optimisation and innovation. Even though AI consumes vast amounts of computational power and resources, its capabilities, coupled with those of FinOps, offer measurable saving and resource management opportunities – proactively and strategically providing companies with bandwidth for innovation and growth, while optimising their operational expenses.

The bottom line: by harnessing the power of AI-based automation, predictive modelling, and real-time cost-monitoring, companies can realise new opportunities for revenue maximisation, cost-optimisation, and improved resource allocation.

Particularly now, with customers and investors placing greater emphasis on innovative enterprise solutions that can sustain innovation and growth through FinOps, this provides a benchmark for improved technological and financial performance – positioning AI-based enterprises for long-term success in an increasingly competitive landscape. To learn more, visit us here.

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