What CEOs Should Understand About the Costs of Adopting GenAI?

Posted on : August 12th 2024

Adopting Generative AI (GenAI) is rewarding for any organization, but it comes with significant costs. CEOs must grasp the financial, data, and workforce implications to ensure a successful implementation. Understanding these factors is crucial for leveraging GenAI effectively and avoiding costly pitfalls. This blog discusses the key factors businesses should consider regarding the costs of GenAI adoption.

GenAI Surge

• By 2026, over 80% of organizations are expected to use generative AI (GenAI), up from less than 5% in 2023, but only 16% will succeed.
• Successful GenAI implementation depends on its financial value, the availability of relevant data, and its impact on the workforce.
• Business leaders should focus on using GenAI to solve real problems, not just for the sake of adoption.

The Crucial Interplay of Cost and Strategy in Adopting GenAI

With the rise of GenAI, CEOs must recognize the connection between costs and strategy. GenAI is set to drastically disrupt business models, and failure to adapt can lead to obsolescence.

Cost considerations are essential for developing effective GenAI strategies, as many companies face unexpected expenses during cloud migration. This highlights the need to account for full lifecycle costs in strategic planning.

CEOs, as architects of their company’s future, must understand these potential costs comprehensively to ensure successful GenAI implementation.

The Complex Landscape of GenAI Costs

Inference Cost

Inference cost refers to the expense incurred when using a large language model (LLM) to generate responses. This process, powered by GPU compute, is essential for engaging a trained LLM. For example, generating text with GPT-4 costs around $0.006 per 1,000 output tokens and $0.003 per 1,000 input tokens. Similarly, generating an image with DALL-E 2 costs about $0.18 per image.

Reducing inference costs can involve using smaller models, hosting open-source LLMs, or optimizing the inference process. CEOs can arrange optimal strategies by conducting a comparative analysis of different approaches.

Fine-tuning Cost

Fine-tuning adapts a pre-trained generative AI model to specific tasks or domains, involving additional training on a new dataset. The cost depends on factors like model size, data volume, and training iterations. For example, fine-tuning GPT-3.5 Turbo on a dataset with 100,000 tokens over three epochs costs about $2.40. Techniques like transfer learning and distributed training can help reduce these costs.

Prompt Engineering Cost

Prompt engineering involves structuring text for effective interpretation by GenAI models. This process can significantly improve model output quality but requires careful planning and investment. CEOs must balance prompt engineering and fine-tuning based on accuracy, precision, budget, and available expertise.

Cloud Expense

When considering cloud expenses, CEOs must look beyond hosting costs. A holistic view of the overall cloud architecture post-GenAI implementation is essential. For instance, healthcare institutions in the US may need to expand local private cloud storage for massive data generated by GenAI-driven chatbots. CEOs must refine cloud strategies to navigate the complexities of public, private, and multi-cloud solutions effectively.

Talent Costs

Talent is the cornerstone of GenAI strategies. CEOs must avoid a short-term rush for talent, which can escalate costs. GenAI will create new job categories, necessitating medium and long-term talent plans. Collaboration with CHROs to develop these plans is crucial. Identifying which talents can be sourced externally and which require internal promotion and training is essential.

Operational Costs and Hidden Costs

MLOps

MLOps (Machine Learning Operations) streamline workflows and automate machine learning deployments, reducing costs by enhancing efficiency and error detection. CEOs must understand key considerations like continual retraining, self-learning models, and data dispersion to navigate MLOps effectively.

Infrastructure Overhaul

GenAI demands substantial infrastructure modifications, including enhanced computing power and efficient data storage solutions. CEOs must anticipate potential bottlenecks and allocate budgetary space for technological patches.

Data Security

GenAI projects face the highest data security risks. CEOs must implement strategies to prevent data leakage, counter misinformation, and address biases. Creating a trusted environment, proactive workforce training, transparency, and leveraging human oversight are vital.

Controlling Costs: A Strategic Approach for CEOs

Integrating Cost Control

CEOs must integrate cost control into the decision-making process by establishing clear parameters for decision-making roles, metrics, guidelines, and timeframes. A structured framework ensures strategic control over GenAI project costs.

Monitoring Costs

A comprehensive dashboard to monitor all GenAI projects, tracking expenses from model training to cloud usage and operational costs, is essential. This dashboard allows CEOs to provide feedback and make informed decisions.

Empowering Leadership and Teams

Constructing a well-structured GenAI talent pool saves costs and fuels innovation. Collaboration between CEOs and CHROs is necessary to address the scarcity of generative AI experts. Upskilling existing employees and implementing adaptable training programs are cost-effective long-term strategies.

Which LLM Model should we use?

One of the key challenges for technology leaders, including heads of GenAI and CIOs, is finding effective ways to leverage LLMs (large language models) for innovation while managing costs and ensuring quality. The cost-quality tradeoff can be visualized by plotting models on a graph with cost on the x-axis and quality, measured by an ELO score, on the y-axis.

For instance, GPT-4 costs $5 per million tokens. Some models, like Gemini 1.5 Flash8, are both superior in quality and cheaper, placing them in the “frontier model” category. On the other hand, models like Llama 27b are outperformed or outpriced by others, making them less favorable.

Frontier models, which currently include Llama 38b (lowest cost), CLA 3 HighQ, Gemini 1.5 Flash, J 1.5 Pro, and GPT-4, are the preferred choices. Other models, represented in red or gray on the graph, either offer no advantage or are not worth using.

To illustrate the cost differences, consider CLA 3 HighQ, GPT-4 Turbo, and GPT-4. For 1 million tokens (about 4 megabytes), CLA 3 HighQ costs 25 cents, while GPT-4 Turbo costs $10, which is 40 times more, and the older GPT-4 model costs $30, which is 120 times more. A $10,000 budget with CLA 3 HighQ could escalate to a $1.2 million budget with the older GPT-4 model, highlighting the importance of choosing the right model for cost efficiency and quality.

Watch this video to understand the costs of LLM models.

Conclusion

Straive offers comprehensive GenAI solutions that help businesses navigate the complexities of adoption. From initial cost assessments to implementation and optimization, we ensure that your investment in GenAI delivers maximum ROI, streamlining operations, enhancing customer engagement, and driving significant business value. With our expertise, you can confidently embrace GenAI and transform your organization.