Enterprise AI Spend Gets A Reality Check

The cost of utilizing AI is rapidly decreasing, with processing a million tokens becoming significantly cheaper every year. The market now uses DeepSeek V4 Flash at $0.14 per million input tokens, OpenAI's GPT-5.6 Luna at $0.20, and Meta's Muse Spark 1.2 at $1.25. While cheaper intelligence could potentially lead to more AI usage, enterprise leaders are shifting their focus beyond the affordability of AI to its productivity gains, revenue impact, and business outcomes.

They are setting budgets for AI features, measuring which teams consume the most tokens, and determining which tasks require expensive frontier models versus cheaper alternatives. As AI becomes more cost-effective, the question remains: what justifies spending on an AI token? Enterprises are moving away from using more tokens for the sake of it and are instead implementing financial controls to ensure AI tokens are spent on valuable tasks.

Companies like TiDB and Eightfold AI are adopting value-first approaches, approving AI usage based on the value it produces, and tracking token consumption with monthly budgets. This shift in AI budgeting is leading organizations to treat AI usage more like a budget request and less like an open-ended experiment. While cheaper models are being used for routine tasks, expensive frontier models are reserved for complex reasoning or sensitive work.

Indian enterprises are particularly interested in open-weight models for high-volume, less sensitive workloads, as they can reduce inference costs by 30-70%.