Exploring Scalability and Costs of GPT AI Product with Zano and WeWeb: An OpenAI Integration Discussion
The State Changers' meeting included several important insights:
- The meeting focused on the scalability of a GPT AI product built with Zano and Web Flow.
- They discussed about AI and how it differs from other applications due to its computational requirements and related costs. Unlike other applications, most of the demand from AI is computational and can amount to considerable expenses as users interact heavily with the system.
- The implications of AI for the economics of a product were highlighted. While SaaS applications could boast profit margins of around 95% on marginal costs, AI-based businesses might see different figures given the computational requirements.
- The cost from providers such as OpenAI was identified as an important factor in running an AI-based product. As users interact with the AI, the cost of computation (or 'grinding') becomes relevant. This is different from classic SaaS models where user interaction would not drastically affect cost.
- The meeting highlighted that the tech stack with Zano and Web Flow is scalable, but implementing intelligent controls on resource expenditure is crucial when using AI-centric software.
- The group discussed rate limiting and how it will become important for financial management in this context.
- A trial period for customers was mentioned, highlighting its differing cost implications compared to other SaaS products that may have been built in the past.
Overall, the meeting offered deep insights into the challenges and nuances of building and scaling an AI-based product with Zano and Web Flow.