4 Things to Consider When Integrating Generative AI – DevOps.com

4 Things to Consider When Integrating Generative AI
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Considering the current state of VC funding, the surge of investment dollars in AI companies is remarkable. Microsoft’s $13 billion investment in OpenAI and AWS’ $4 billion investment in Anthropic (who themselves just raised a $850 million Series C round) demonstrate the tremendous excitement around Generative AI and how it will shape the future of tech.
As newer companies like Adept and Cohere are emerging, existing SaaS companies are integrating generative AI capabilities into their products and services. However, in the race to join the hype, many companies are building first and strategizing later. Before diving headfirst into building a generative AI solution, companies should carefully consider a few crucial factors, including model selection, usage tracking, monetization and customer transparency.
Companies must first consider whether to build a generative AI model from scratch, or whether to use a pre-existing model from one of the major AI vendors. This decision depends on the level of customization and control desired, as well as the resources and expertise available.
Building a from-scratch model is likely infeasible due to the inaccessibility of the necessary hardware. The GPUs needed for cutting-edge model training and inference are currently only built by NVIDIA, and there is stiff competition and long lead times for any company looking to acquire the product. What’s more, large cloud and AI companies like OpenAI, Google, Meta and Tesla already have built systems with up to 100,000 of these chips. New companies looking to enter the scene would face a steep climb to assemble the infrastructure needed to even begin to compete with the current classes of models.
Small or new companies do not have the funding or time to invest in building their own infrastructure or generative AI models, so a key business decision is to select the best vendor and model for the use case. While ChatGPT is the most well-known, there are other specialized models that can be better suited for certain use cases. For example, OpenAI’s DALL-E is built for image generation, while Anthropic’s Claude 2 is built to perform better with specialized tasks such as legal and math and to handle long-form inputs.
Regardless of the AI vendor you choose, you will be charged based on your usage with them, which, by extension, is your customers’ usage. Tracking this usage is critical to your generative AI solution becoming profitable.
Implementing usage metering for generative AI services is crucial at both the individual user level and at the global or system-wide level. This is because usage tracking is valuable to all business functions and is a critical element of any modern PLG strategy.
For example, customer-facing teams can leverage usage data to proactively track account health and to contextually recommend new solutions and approaches. Usage history provides valuable intelligence to account teams for up-selling, cross-selling, and contract renewal. Product teams can identify usage patterns and customer segments, as well as areas of friction or improvement for new feature development.
Usage data also plays a major role in developing pricing plans that align with customer expectations and overall market trends. It can be used to inform pricing plan development and optimization to ensure the rates are sensible for the level of consumption and value being realized. Without tracking usage, this becomes a guessing game or a futile exercise in “gut feel.”
Usage data is imperative to calculating your internal costs. Vendors commonly charge according to a usage-based model for these generative services–per word, per token or per generation are some common models. In any case, having accurate, granular usage data is a must-have for calculating internal costs and, in turn, customer-facing pricing.
In the rush to beat competitors to market and offer generative AI services to customers, do not neglect to lay the groundwork for monetization from day zero. It is much easier and more efficient to build with monetization in mind from the start than it is to backtrack and try to monetize a service once it has been offered, particularly when usage-based pricing is involved.
Usage-based pricing has emerged as the optimal pricing strategy in the AI domain. This is because service usage varies over time for each customer. These services are priced by the vendors according to a usage-based pricing model. Since the backend costs of the service are elastic, the frontend, customer-facing pricing should also be elastic.
As a roadmap to monetization, follow these steps:
-Meter service usage to understand segments and patterns
-Calculate internal costs of usage using vendor pricing
-Using the information from steps 1 and 2, build customer-facing pricing
-Track revenue and margins and continue to iterate with pricing as needed
Prioritizing transparency and clear communication with customers is non-negotiable. Ensuring that usage and billing data can be seen at any point in the billing cycle helps foster trust between the company and its users and eliminates billing surprises when the invoice is delivered.
Invest in creating a customer-facing application, portal or experience that allows for on-demand access to usage and billing data. The beauty of combining this level of visibility with usage-based pricing is that customers are empowered to pay for only what they use while viewing usage statistics on demand and modulating usage as needed in real-time to keep the spend within an acceptable range.
In practice, usage data is available on a per-query level from the vendor’s APIs. For example, for any generation action, OpenAI will show the precise number of tokens consumed for both the prompt and response, as well as all details around the model type and context needed to identify the per-token rate from the vendor pricing. It is important to collect this data and build a user-friendly way to display it back to customers in an app, portal, or website that allows for on-demand access and browsing.
While planning to build a product with generative AI, take all of the above factors into account. Do not make the mistake of racing to deliver a technology solution without also considering the business implications and monetization strategy. With all the hype, excitement and investment around generative AI in 2023, it is akin to a modern-day gold rush. Don’t show up to the gold rush without a shovel.
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