Understanding the costs associated with AI and its supporting infrastructure is vital for measuring ROI. Techniques like token compression and retrieval-augmented generation (RAG) can help manage inference costs, but infrastructure expenses can be significant. Best practices such as FinOps and cost attribution aid in identifying inefficiencies and controlling costs. The article highlights the iterative nature of AI development, where user feedback informs continuous improvement. Ultimately, successful companies balance bold innovation with practical resource management, focusing on value and efficiency to maximize AI's potential without compromising their bottom line.
Key takeaways:
- Balance innovation with cost control by building and optimizing AI solutions based on customer feedback and measurable value.
- Adopt a "fail fast" mindset to focus on promising ideas and avoid wasting resources on unsuccessful projects.
- Benchmark AI models for precision, accuracy, and context-specific performance using frameworks like Ragas to measure effectiveness.
- Understand and manage total ownership costs, including infrastructure and data expenses, to accurately measure AI ROI.