Prototyping AI apps can be an exciting journey for entrepreneurs, but it comes with unique challenges. In this post, we’ll highlight two key considerations to keep in mind when transitioning from idea to working AI software. First, we’ll explore the critical differences between experimenting with ChatGPT and building on OpenAI’s API—and why shifting to API-based development early is essential. Then, we’ll address the impact of OpenAI’s usage tiers on new developers, along with practical workarounds and alternatives. By understanding these challenges, you’ll be better prepared to prototype efficiently and bring your AI applications to market smoothly.
Taking an AI app from concept to working software can be an exciting but challenging process. Through hands-on experience we’ve identified two significant factors that developers and entrepreneurs should have in mind when trialing new ideas.
The OpenAI API is not the same as ChatGPT
Many entrepreneurs and developers start experimenting with AI by testing ideas in ChatGPT. This makes sense – it’s an easy way to explore potential use cases. However, it’s crucial to understand that ChatGPT and OpenAI’s API are fundamentally different.
The chat app is a polished product with a personality, it has been tuned to respond in a certain way, its parameters and tools configured to be useful, and it adapts its behavior based on your past interactions.
The API on the other hand is a raw tool: you have to configure behaviour, you have to define tools, you have to learn the history of your user and use it to provide the right context, and you have to craft the right system prompt to get the right style of output.
Simply copying a prompt that worked well in ChatGPT into an API call will not yield the same results. While ChatGPT is useful for validating whether an LLM might be suitable for your problem, moving to API-based development early on is essential. This will ensure your prototype behaves consistently and you are not over-optimising to ChatGPT’s nuances.
OpenAI usage tiers are a problem if you are new
The other issue businesses experimenting with AI for the first time will face are the OpenAI usage tiers. These barriers (that require certain levels of spending and time on the platform) restrict both the volume of API calls you make, and more crucially, the models that you can access.
At the time of writing you cannot use the full o1 model or o3-mini unless you are in usage tier 3 or higher, and that requires at least a $100 of spending. You also have to have at least a week of payment history.
Thankfully in this case pre-paying for credits can unlock the barriers. But that does require a small upfront commitment and a week of waiting before you can do any experimentation on the newer models.
An alternative to consider is using an open source model like Llama 3 or DeepSeek via a platform such as AWS Bedrock or Groq. We’d always suggest trialing different models anyway, as you never quite know which is going to perform best for your specific task.
Working with a partner like Storm to develop your AI application can be helpful as we can prototype ideas on our account while your access levels increase. We’ve also got Ruby on Rails app templates with AI API code ready to go, so your idea can get off the ground as quickly as possible.
Final Thoughts
Prototyping AI apps is an iterative process, and understanding these two challenges can help you move from idea to implementation more smoothly. By moving beyond ChatGPT early and planning for OpenAI’s tiered access system, businesses can bring AI-powered solutions to market quickly and efficiently.
