Indian enterprises turn to Chinese AI models as cloud costs bite

As Indian enterprises move into a more cost-conscious phase of AI adoption, a growing number are turning to Chinese large language models as a cheaper alternative to leading US systems, particularly for high-volume and multilingual workloads.

Indian companies are increasingly leaning on models from DeepSeek, Alibaba’s Qwen and Moonshot AI’s Kimi to rein in spending, according to Nikkei Asia, even as the shift raises fresh questions about the country’s ambitions for AI sovereignty. The appeal is largely economic: open-weight Chinese models can run 60 percent to 90 percent cheaper than comparable offerings from OpenAI and Anthropic, per data cited by Nikkei and CNBC, while newer releases such as Zhipu AI’s GLM-5.2 are reportedly priced at a fraction of rival flagship models while matching them on several benchmarks.

The trend reflects broader budget strain across the industry. Surveys of enterprise AI spending in 2026 show that a large share of organizations have exceeded their original AI budgets, with inference, not training, now accounting for the bulk of ongoing costs. That has pushed CIOs and CTOs to match “good enough” models to routine tasks such as customer support, internal copilots and content generation, reserving premium U.S. models for higher-stakes work.

Chinese LLMs’ multilingual capabilities add to their appeal in India, where companies handling diverse regional languages and dialects say the models perform competitively without the premium price tag of Western systems. Open licensing terms that allow self-hosting are a further draw, letting enterprises run models inside their own environments, an option some see as a privacy and data-control advantage over closed, cloud-hosted alternatives.

But the shift is not without risk. Both Washington and Beijing are increasingly treating frontier AI as a strategic national asset. Reuters and other outlets have reported that China’s Ministry of Commerce has held talks with firms including Alibaba, ByteDance and Z.ai about restricting overseas access to their most advanced models, some of which have not yet been publicly released. Any such curbs could leave Indian firms that have built workflows around Chinese models scrambling for alternatives, on top of unresolved questions about data governance and security when enterprise data flows through foreign AI stacks.

The dynamic is feeding a broader debate in India over whether to keep relying on imported frontier models, American or Chinese, or invest more heavily in domestic alternatives, even if that means slower progress in the near term. For now, most enterprises appear to be hedging: keeping premium U.S. models for critical applications, testing cheaper Chinese and open-source models for routine workloads, and pressing all vendors for better price-performance. How that balance settles will shape not just individual companies’ AI strategies but India’s broader position in a global market where affordable, capable AI is fast becoming a competitive edge in its own right.

CT Bureau