AI companies turn to cheaper Chinese models to slash inference costs

Indian technology firms are increasingly adopting Chinese large‑language models such as DeepSeek, Alibaba’s Qwen family and Moonshot AI to reduce AI inference expenses, with some reporting cost gaps of more than 70 % compared with Western alternatives. The shift is driven by tight budgets in Bangalore, Hyderabad and Pune and raises geopolitical concerns about data flowing through Chinese‑controlled infrastructure.

In the United States, companies are also moving toward Chinese models, citing similar cost advantages that have kept weekly usage above 30 % since early 2026. The trend is reflected in broader industry analysis that predicts over 90 % of all AI token consumption will come from open‑weight models within the next 18–24 months, compressing inference margins for frontier labs.

Enterprises such as Salesforce are cutting inference spend by right‑sizing models, tuning open‑source LLMs for specific tasks instead of relying on expensive frontier models. Gartner expects task‑specific AI agents to appear in 40 % of enterprise applications by the end of 2026, up from under 5 % a year earlier, as firms prioritize cost, control and task suitability over raw model size.

The overall AI efficiency shift is moving the margin to whoever can run inference cheapest, with Chinese providers offering low‑cost alternatives that challenge U.S. and European AI leaders and could reshape pricing dynamics across the global AI market.