Council Post: Taming Generative AI; Strategies to Control Enterprise Inference Costs

One of the simplest yet most impactful ways to reduce inference costs is by selecting the right model size.
As generative AI continues to revolutionize industries, enterprises are increasingly deploying large language models (LLMs) to streamline processes, enhance customer experiences, and drive innovation. However, while the benefits of generative AI are immense, the cost of running these models in production—known as inference costs—can spiral out of control if not managed effectively. This financial burden not only affects the bottom line but can also hinder long-term scalability and sustainability. In this article, we explore strategies that can help enterprises control the rising costs of AI inference while maintaining high-quality performance. From selecting the right model to optimizing prompts and using advanced techniques like knowledge distillation and quantization, these approa
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Picture of Kalyana Bedhu
Kalyana Bedhu
Kalyana Bedhu, is an AI/ML Leader at a finance company. As an AI/ML thought leader specializing in digital transformation, data science, and AI integration, he is entrepreneurial and a quick learner, adept at converting business challenges into technological strategies. At Ericsson, he led an AI-enabled transformation, enhancing services, operations, and products, and managed $5M+ AI projects across six portfolios, ensuring unit success and maintaining senior stakeholder relationships. His career highlights include developing intrapreneurship ventures for connected cars and worker safety, targeting a $20B+ market, creating over $20M in new data products and services, and establishing Ericsson's AI/ML practice by hiring and retaining top data science talent. He aims to transform your P&L through strategic use of analytics and AI.
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