Your AI Cost Problem Isn’t Training. It’s Inference.

While the costs of AI systems are often attributed to the training phase due to its visible, measurable, and resource-intensive nature, the true financial burden lies in the inference phase, where AI operates continuously and scales with usage. Unlike training, which is a bounded process with known computational limits, inference is unbounded and compounds over time as systems grow and more workflows depend on them. This results in non-linear cost increases, as each request requires heavy computation to process context, resolve relationships, and determine relevance. Inefficient inference not only accelerates costs but also poses sustainability challenges due to increased energy demand, highlighting the need for efficient systems that minimize unnecessary computation. Solutions such as moving relationship-oriented computations out of language models and into specialized systems like TigerGraph can help reduce the computational load by providing structured information upfront, thus optimizing the AI's performance and reducing its overall energy consumption.

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