Navigating Costs in the Generative AI Landscape

Generative AI has caught the attention of the business world as a potential game-changer and driver of revenue. The popularity of ChatGPT since 2022 has propelled the development of Generative AI at a rapid pace, with major players such as Google, Amazon, and Microsoft all diving in to create their own offerings, while countless smaller companies are also making their own mark in the Generative AI landscape.

Indeed, Generative AI is gaining momentum and is expected to play a significant role in our future. According to a recent Salesforce study, 67% of IT leaders surveyed have prioritised Generative AI for their business within the next 18 months. This highlights the ever-evolving Generative AI landscape and its potential to transform the way we work.

With the Generative AI landscape rapidly evolving, it's important to implement a cost-effective strategy that aligns with your specific use case. This can be challenging given the variety of AI options available in the market, and the potential for small differences to impact your budget – from open-source versus closed-source software, to customisation options, deployment, hosting, data management, scalability and more. Without proper due diligence, costs can quickly spiral out of control.

In this article, we explore the critical factors to consider when examining the Generative AI landscape to help you determine the value of, and manage the costs of a potential purchase.

Consider the costs of different Generative AI solutions

The prices for Generative AI solutions can vary wildly. They can range from free or low-cost services to monthly subscriptions and expensive bespoke packages that offer high levels of customisation.

Open source: Open source models are solutions that require infrastructure setup, but in turn enable a high degree of customisation. Open sources tend to be less costly but may require more know-how to get them working correctly for your organisation.

Closed source: Typically offered by major tech companies, closed source models provide ready to use Generative AI solutions that are based on cloud infrastructure. Using closed source models is often the most convenient and effective approach, but it may come with significant costs.

Generative AI as-a-service: Outsourcing Generative AI development and deployment to a service provider has several advantages, including highly tailored solutions and access to expertise that your organisation may lack. However, there is also a risk of ballooning costs, depending on what the provider requires, as well as becoming too dependent on the supplier, as with most as-a-service solutions.

Case specific Generative AI technology: The Generative AI landscape offers many solutions that enhance specific operations and processes - such as data analysis or email marketing. While some solutions may be initially cheaper, case specific Generative AI solutions are typically harder to scale and may contribute to technical debt.

In-house Generative AI: When it comes to developing custom solutions and having complete control, in-house development is the way to go. However, it's no secret that this option requires highly specialised and expensive skills, which can be hard to come by. Additionally, the costs associated with computing architecture and training models can easily skyrocket into the millions of pounds. Therefore, opting for third-party solutions can often be the more practical choice.

Key factors to consider before investing in Generative AI solutions

Before buying Generative AI solutions, it's important to consider factors like your use case, data sizes, and complexity. In this article, we'll explore the expenses associated with these solutions and highlight key considerations to keep in mind to unlock the potential of Generative AI.

● Use case: Like with any significant change to your organisation, it's critical to clearly define the use case for Generative AI and ensure it's embedded in your purchasing decision. Although Generative AI tools use the same technology, the outcomes can vary greatly. For instance, a Generative AI tool used to create content will differ from one used for data analysis, and each will have different cost considerations.

● Model size: Larger models with more variables and parameters will need a higher level of resources and computer power - which could lead to higher costs.

● Pre-training: Pre-training is the process of the LLM gaining an understanding of the language via exposure to vast amounts of text and data from sources such as websites and articles. Processes such as cleaning the data to remove noise, gathering data, and tokenizing data (converting text into numbers that the model can understand) can take up time and money.

● Inference: AI models are designed to generate responses based on the inputs provided. For instance, a chatbot that handles customer service can correctly answer a query like "where is my order?" However, the costs associated with inference can vary depending on the complexity of the use case.

● Fine tuning a model: Once a model has been pre-trained, it must be fine-tuned to perform specific tasks. This process involves supervised learning and requires high-quality, task-specific data that may be expensive or difficult to obtain.

● Hosting for your Generative AI model: When deciding where to host your Generative AI model, there are two main options in the Generative AI landscape: on-premises or cloud-based. Both methods have their advantages and disadvantages, but each comes with costs. On-site hosting requires infrastructure and maintenance expenses, while cloud-based hosting often involves subscription fees.

Managing the costs of implementing and maintaining Generative AI

While we have covered some of the costs to consider before making a purchase, what about the costs of implementing and maintaining a Generative AI solution.

● Costs of implementation: When calculating the costs of implementing Generative AI, it's important to consider various factors, including software, hardware, licensing or subscriptions, upfront expenses, and deployment fees. These expenses can add up and should be taken into account before making a decision.

● Maintaining and updating Generative AI: Generative AI technology requires regular maintenance and is rarely a one-and-done solution. There are various costs to consider, including software updates, cloud hosting, and personnel to maintain, monitor, and test the Generative AI to ensure it is operating correctly for your specific use case.

● Data Management Costs: When managing large amounts of data, the expenses of data storage, data cleaning, and cybersecurity measures to ensure proper handling of data can quickly add up and consume a significant portion of your budget.

● Staying compliant with regulations and laws: In light of upcoming legislation, such as the EU's AI Act and the UK's Online Safety Act, regulatory compliance is a major concern for businesses diving into the Generative AI landscape. Users will be held responsible for ensuring that their use of Generative AI is in line with the proposed frameworks. To meet these guidelines, organisations must take measures, such as ensuring that data sets are unbiased and appropriate, developing methods to tag Generative AI, and adhering to data privacy and security regulations. Breaching regulations can result in steep fines.

● Team training: In order to guarantee that Generative AI runs smoothly, a skilled team that is familiar with the technology is essential. Tasks of a Generative AI facing team include updating data sets, troubleshooting, and retraining the AI. The team must keep on top of best practice, whether through development programs, courses, or other forms of training. While this can be a time-consuming and costly process, it is critical to ensure that the team has the necessary skills to implement Generative AI effectively.

● Scaling Generative AI: Incorporating Gen AI into your current systems and accommodating an increase in demands may involve customization and additional expenses.

● Billing: There are two main types of billing methods for commercially Generative AI tools: token-based and character-based. Character-based billing charges are based on the number of characters used in both the input and output text. On the other hand, token-based billing turns output data into tokens, with the price adjusted based on the LLM used and the quality of the output.

What can you do next: Navigating the costs of Generative AI landscape

If you're delving into the world of Generative AI, it's important to consider the potential costs, as they can quickly mount up. Be sure to scrutinise variables such as whether the Generative AI solution is closed or open source, as well as deployment, hosting, training data quality, computing architecture, customization, and more. Each of these elements can impact overall expenses.

If you want to keep on top of the Generative AI landscape to understand how to best navigate costs, register to attend Generative AI Summit 2024. This conference is the strategic, practical hub for leaders in AI, Data, Technology, and Innovation sectors, focusing on the transition from initial AI trials to robust, enterprise-wide applications that deliver real value.

● Cost-Effective AI Strategies: Insights into optimising investment and resource allocation for maximum efficiency in AI projects.

● Realising AI Value: In-depth case studies and panels focusing on tracking and enhancing the ROI from Generative AI investments.

● Optimising Infrastructure: Ensuring your organisation's technological backbone is prepared for seamless integration and performance.

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