Vertex AI model families evolve rapidly. New model versions (e.g., transitions within the Gemini family) frequently introduce improvements in efficiency, quality, and capability. When workloads continue using older, legacy, or deprecated models, they may consume more tokens, produce lower-quality results, or experience higher latency than necessary. Because generative workloads often scale quickly, even small efficiency gaps between generations can materially increase token consumption and cost. Teams that do not actively track model updates, or that set model types once and never revisit them, often miss opportunities to improve performance-per-dollar by upgrading to the most current supported model.
Vertex AI Generative AI usage is billed per input and output token. While newer model versions may have similar per-token pricing, they often deliver more accurate outputs, require fewer tokens to achieve the same results, and provide better latency and throughput. Continuing to run older models can increase overall cost and degrade output quality.
