There are many AI services and products available today: OpenAI's ChatGPT and GPT-4, Anthropic's Claude, AWS Bedrock, Azure OpenAI, Google Cloud Vertex AI, open-source models on Huggingface, and more. Should you use these services through their APIs? Should you deploy open-source models? Or should you train your own AI model? Let's explore these options.
The Buy vs. Build Decision
Choosing between buying and building technology is a key decision for management. It affects development efforts, future support costs, and potential risks and limitations. Buying—using an out-of-the-box product or a third-party API—means you can benefit immediately, unlike the delayed advantages of building your own. With purchased technologies, development risks are minimal, and support efforts are low. Often, buying is also cheaper at scale when considering the support costs of in-house solutions. So, why consider building your own technology at all?
Building Competitive Advantage
Technological innovations can speed up processes, reduce costs, and improve your bottom line—think of new databases, cloud services, or even better laptops for staff. AI can do this too. However, unlike databases or cloud technologies, AI can provide a competitive advantage by making your product harder to copy.
Modern AI relies on machine learning, which combines data and algorithms—the model architecture, training setup, and so on. We train machine learning models with data to produce the final, trained model. In this combination, data is paramount.
Consider two companies, A and B, both aiming to train their own AI models. Company A invests heavily in in-house research, developing proprietary neural network architectures and training techniques, using publicly available datasets. Company B, on the other hand, invests in generating and acquiring large, high-quality proprietary datasets and plans to train an open-source neural network architecture with this data. Which company will end up with the better model?
The answer is Company B. High-quality data outweighs algorithm design. Training an inferior algorithm with superior data yields a better model than training a superior algorithm with inferior data.
Owning the best data allows you to train the best model. Now, consider the following product strategy:
Your product generates data from user interactions.
You use this data to train your AI models.
Your AI models improve your product.
The improved product attracts more users and generates more data.
This creates a positive feedback loop—a network effect. The more users you have, the better your AI models become. And the better your models, the more users you attract. Using this approach, products like Google Search and YouTube have conquered their hundred-billion-dollar market shares. Competing with them directly is impossible because you cannot replicate their products without their data, and you cannot obtain their data without their products.
Buy vs. Build Decision in AI
When deciding whether to buy or build AI solutions, it's crucial to define your business objectives. Are you aiming to speed up processes, cut costs, and improve your bottom line? Or do you want to make your product harder to copy and gain a competitive edge? Your answer will guide your AI strategy and clarify the buy vs. build decision.
If Your Goal Is to Improve the Bottom Line, Buy
To optimize costs and time to market, consider buying. Use out-of-the-box products and APIs like GPT-4 and Claude whenever possible. If data security is a concern, deploy open-source models such as Llama 3.2 and Mistral on your private cloud or on-premises GPU servers. This approach accelerates time to market and ensures low development costs. As models and hardware become cheaper over time, long-term costs may also be lower compared to building your own models. Only consider training your own AI model if nothing similar is available on the market, and even then, prefer low-code or no-code model training platforms.
If Your Goal Is to Create a Competitive Advantage, Build
If you want to make your product harder to copy, building your own AI is the way forward. Establishing your own virtuous AI cycle requires close collaboration between your product and AI teams. They must ensure your product collects the right data and that your AI models enhance product quality to attract more users.
Regularly retraining your models—perhaps weekly or monthly—is essential. This means setting up in-house practices for data monitoring and cleaning, model training, quality assurance, monitoring, and support. You need to maximize the value from the data your product generates and ensure your product benefits fully from the AI models you develop.
Since your data becomes your key asset and the foundation of your competitive advantage, you should not share it with third-party APIs or products. After all, you cannot buy a competitive advantage off the shelf.
Buy vs Build is just the first decision in the exciting journey of AI technology management. You can check our other posts to get an extensive explanation of what the network effect is and how AI enables it, how to build an AI competitive advantage for your company, what culture helps you build the right AI products, what to avoid in your AI strategy and execution, and more.
If you need help in building an AI product for your business, look no further. Our team of AI technology consultants and engineers have decades of experience in helping technology companies like yours build sustainable competitive advantages through AI technology. From data collection to algorithm development, we can help you stay ahead of the competition and secure your market share for years to come.
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