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AI Costs Vs Performance: Strategies For Running LLMs In Finance

Mar 17, 2025 - forbes.com
The article discusses the challenges financial institutions face with the high costs of deploying Large Language Models (LLMs) in AI applications, such as real-time credit scoring and fraud detection. It outlines strategies to optimize AI infrastructure and reduce costs, including fine-tuning smaller models, using retrieval-augmented generation, leveraging quantization and distillation techniques, and choosing the right AI infrastructure between cloud, on-premises, or hybrid solutions. Additionally, it emphasizes the importance of efficient API usage, smart prompt engineering, and continuous cost monitoring to ensure cost-effective deployment without compromising performance.

The article also highlights the need for robust AI governance frameworks to maintain compliance and security while optimizing costs. It suggests implementing data access controls, maintaining LLM audit logs, and enforcing cost-based access policies. The future of AI in financial services is expected to be shaped by cost-effective models like DeepSeek, which can significantly reduce LLM costs while maintaining accuracy. Financial institutions that successfully balance innovation with efficiency will be able to maximize AI ROI and maintain competitiveness in the industry.

Key takeaways:

  • Financial institutions can optimize AI infrastructure by fine-tuning smaller models and using techniques like Retrieval-Augmented Generation (RAG) and model distillation to reduce costs.
  • Choosing the right AI infrastructure, whether on-premises, cloud, or hybrid, is crucial for balancing cost and performance based on workload demands.
  • Efficient API usage and smart prompt engineering can significantly cut costs by batching queries, optimizing prompt length, and utilizing caching mechanisms.
  • Continuous monitoring and cost analytics, along with robust AI governance frameworks, ensure cost efficiency without compromising compliance and security.
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