If building your own AI model is so hard, why do it at all?
The short answer: because in certain situations, it’s the only way to unlock the full strategic or commercial value of AI. But those situations are rare and they require clear thinking, measurable benefits, and a long-term view.
If you have access to a unique, high-quality dataset that no one else does and that dataset directly powers a product or insight, building your own model gives you full control over how it’s used and monetised.
If AI is part of what you sell (not just something you use internally), owning the model can become an IP asset and a competitive advantage. Think AI-native companies in fintech, healthcare, or logistics that need domain-specific performance.
In-house models require robust data governance, tooling, and the ability to support continuous improvement. If you’ve already invested in MLOps and have the infrastructure to support it, building becomes more feasible.
Certain industries (e.g. defence, health, finance) may not be allowed to use third-party APIs or externally hosted models. Full ownership allows for end-to-end control, data residency, and explainability.
If you're operating at scale or require ultra-low latency or cost-sensitive inference (e.g. edge devices, real-time systems), training and deploying your own lightweight models can beat general-purpose ones.
Building your own model should eventually:
It’s not just about avoiding OpenAI token fees. It’s about owning the strategic lever that differentiates your business.
Before you commit:
If the answers aren’t compelling, don’t build—integrate.
DIY AI isn’t the wrong move, it’s just not the default move. It’s the right decision in high-value, high-risk, or high-control environments where owning the full model lifecycle offers strategic leverage.
In the next post, we’ll explore how to combine the best of both worlds using pre-built models in proprietary ways.
