Why you should not build your application on top of OpenAI’s APIs

How to use OpenAI API: be wary of the risks

With its several offerings, like the GPT-3.5 Turbo and GPT-4 APIs or the Whisper API, OpenAI has given developers the possibility to build an array of interactive tools, from chatbots to study or creative writing assistants. This being said, building an application solely on OpenAI’s APIs can be a risky endeavor, for several reasons. Let’s take a look at what those are.

Limited control and reliability

Inconsistent performance When using the OpenAI API, you might face reliability issues like timeouts and outages. These problems can disrupt your app's performance and lead to a poor user experience. For apps that are essential, this unpredictability could be a real problem.

Rate limits OpenAI sets strict limits on API usage, which can really hold back scalability. Depending on the model you select, there are specific limits on requests and tokens per minute. This can be a challenge for mobile or web apps that are built to manage high traffic or intensive computations.

Cost and scalability concerns

Expensive scaling As your user base grows, API costs can start to skyrocket. Each interaction (input and output) incurs a charge, which means that applications that cater to a large audience might find themselves facing unsustainable operational expenses.

Unpredictable pricing OpenAI can change its pricing structure whenever they see fit. This, of course, brings an element of uncertainty to your business model, which could suddenly become hard to maintain.

Technological limitations

Inferior API capabilities Some developers argue that the APIs available through OpenAI aren’t as powerful and advanced as the direct services you can access on their platform. This gap can lead to limited features or to an app of an overall lower quality.

Lack of integration Although OpenAI provides multiple services, such as ChatGPT for conversational AI and DALL-E for image generation, the APIs lack seamless integration. Developers often need to create custom solutions to connect functionalities, which increases development time and complexity.

Business risks

Dependency on a single provider Depending only on the Open AI API can make your app vulnerable to factors you can't control. If there are any changes in how the API is available, its pricing, or the terms of service, it could really impact your application in a way that's completely out of your hands.

Competitive disadvantage The fact that OpenAI's technology is so readily available makes it tricky for apps to distinguish themselves. In a crowded marketplace where everyone has access to the same tools, standing out can be a bit of a challenge.

What are the solutions then?

While these risks are significant, they are not insurmountable. Developers have the opportunity to take proactive measures to reduce them.

Diversify your AI providers Reduce dependency by integrating multiple AI providers or considering hybrid models. This approach combines different AI sources and architectures to create a well-rounded system tailored to the specific requirements of your application.

Implement error handling and fallback mechanisms Ensure your application can gracefully handle API outages or rate limit issues with retry logic or alternative workflows.

Referring to the process of automatically trying failed API calls again, retry logic helps to restore operations when there are temporary issues or brief unavailability of the API.

On the other hand, by implementing alternative workflows, your application can stay operational even when the API is down or when rate limits are exceeded. This might involve using fallback systems, offering partial features, or managing tasks through a queue to process them when the rate limit resets.

Optimize API usage Using APIs efficiently is crucial for cutting costs and enhancing performance in AI applications. Begin by creating clear and targeted prompts that contain only the necessary information to minimize token usage. Another great approach is batch processing, where you group several tasks into one API call, which helps decrease the overall number of requests. Additionally, consider implementing caching to prevent unnecessary calls, particularly for common queries or expected results.

Explore open-source or self-hosted models Choosing open-source or self-hosted models can be a smart and flexible choice over proprietary APIs. With options like GPT-J, Stable Diffusion, and BERT, you can fine-tune these models for specific applications, giving you more control over how they perform and how they can be customized. Self-hosted configurations are particularly useful for tasks that require high frequency or involve sensitive data, as they reduce reliance on third-party services.

Building your AI-driven app the right way

The APIs from OpenAI hold great promise for AI-based applications, yet they bring along considerable risks tied to costs, reliability, and business reliance. By being aware of these issues and employing strategic approaches, developers can leverage AI's potential while keeping their applications resilient and flexible.

At Miyagami, we’re here to make the process easier for you. Whether it’s designing a system that blends APIs and self-hosted models or fine-tuning AI to fit your specific needs, we help turn your ideas into reality. Our team knows how to balance performance, cost, and flexibility to build applications that stand out and deliver real value. Contact us today, and let’s partner to create AI-powered solutions that are not only smart but built to thrive today and tomorrow.