Unit Economics of AI Products

Understanding what it costs to serve each customer, process each transaction, or generate each output is fundamental to building a viable AI business - and it's where many AI products quietly fall apart. Unlike traditional software, where the marginal cost of serving an additional user is essentially zero, AI products often have meaningful per-use costs: compute for inference, API calls to third-party models, data processing, and storage. These costs can vary dramatically depending on the complexity of each request, making them harder to predict and manage. If you're building an AI product, you need to understand your cost per query, cost per customer, and how these change as you scale. Some AI products that look profitable at low volumes become unsustainable as usage grows, because costs scale linearly while revenue doesn't. Others improve dramatically with scale as you amortise fixed costs across more users. Getting unit economics right means modelling your costs carefully, testing pricing against real usage patterns, and building in mechanisms to manage runaway costs - like caching, tiered service levels, or usage caps.