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The shift to the cloud and the subsequent boom in the sector promised companies the ability to digitally transform themselves while keeping their data secure. However, the cost of this transformation is on the rise, particularly with the addition of generative AI tools.

Big Tech companies, burdened with hefty cloud bills, find themselves in a catch-22 situation. They cannot opt out for fear of being left behind, so they are seeking ways to cut corners. One solution being explored is the development of in-house AI chips to reduce costs.

IBM, at a semiconductor conference in San Francisco, announced its consideration of using its in-house AI chips, specifically the Artificial Intelligence Unit, to lower cloud computing costs. Other tech giants like Google, Microsoft, and Amazon are already designing their own AI chips in an effort to save money on their AI endeavors. Previously, the focus had been on specialized chips like graphic chips, but the demand is expanding.

Microsoft has accelerated its project to design its own AI chips, aiming to make them available within the company and OpenAI by next year. Googles AI chip engineering team has also moved to its Google Cloud unit to expedite progress.

Not only are cloud providers facing high costs, but clients themselves are also grappling with soaring prices. Shifts to on-premises solutions are being considered due to the expense of building on-premises AI/ML resources. However, enterprises are wary of falling behind competitors in terms of AI/ML capabilities. Cloud solutions offer an attractive option for businesses that need to strengthen their infrastructure for AI/ML integration.

To maximize return on investment, clients must carefully consider their needs in terms of tools and compare the cost of creating and using models. Its also important to avoid trying to do everything independently and instead utilize open-source and paid models as a base, training them for specific enterprise data.

Cloud providers are also attempting to lower prices to attract more customers. Amazon Web Services (AWS), for example, aims to lower the cost of training and operating AI models.

As the demand for cloud services continues to increase, fueled by AI workloads, a Gartner report predicts that AI will be one of the top factors driving IT infrastructure decisions through 2023.

In this landscape, businesses may opt to outsource cloud management and maintenance to third-party firms or tools. Adopting a hybrid approach where on-premises AI hardware is used for sensitive data processing and latency-sensitive applications, while cloud services are utilized for data storage, distributed training, and deploying AI models, allows for cost optimization.

Given the bright trajectory of AI, the cloud industry is expected to continue experiencing significant growth and benefit from it.

Excerpt from:
The Rising Costs of Cloud Computing: Big Tech Responds with In ... - Fagen wasanni