Horizontal AI vs Vertical AI
Last updated
Last updated
Examples: GPT, Llama, Gemini, DeepSeek
Purpose: Built to handle a wide range of generic tasks. These models aren’t specialized for one single job and serve most users in many fields.
Key Point: Super versatile, but sometimes not as accurate or effective when a task requires deep, specialized knowledge.
Why it matters: A “horizontal” AI can act like a general-purpose assistant for multiple industries but may lose precision when tackling very specific use cases.
Definition: A foundational (horizontal) AI that has been fine-tuned for a particular industry or task.
Example Use Cases: AI specialized in medical imaging, financial risk assessment, or supply-chain optimization.
Key Point: Offers deeper domain expertise, which leads to more powerful results in specialized areas.
Why it matters: By focusing on a specific industry (healthcare, finance, marketing, etc.), vertical AI can deliver superior accuracy and performance—unlocking valuable, real-world applications faster.
