China's Cheap AI vs India's Sovereign AI: What It Means for Business

1. Why Chinese AI Is So Cheap, and Can It Last?

Since early 2026, Chinese AI labs have started a global price war. They charge a small fraction of what leading US models cost per request (API inference: paying per piece of text processed). The aim is to become the default, low-cost AI engine for business automation worldwide.

What the Prices Look Like

• DeepSeek: A major catalyst in the price war, with models like DeepSeek V4-Flash reaching as low as $0.14 per 1M input tokens. DeepSeek aggressively utilizes cache-hit pricing, saving developers massive amounts on repetitive workloads.
• Alibaba (Qwen): On Alibaba Cloud's Model Studio, Qwen3.5 Flash is priced at near-zero rates: ~$0.10 per 1M input tokens.

Real Efficiency or Government Subsidy?

• Genuine Efficiency (Valid): Chinese labs prioritize inference economics over raw scaling. They use Mixture-of-Experts (MoE) designs, where only part of the model works on each request, so each answer needs much less computing power.
• State-Subsidized "Dumping": To offset the "inefficiency tax" of using domestic AI chips, local Chinese governments offer electricity subsidies of up to 50%. The national "East Data, West Computing" initiative directs compute to provinces rich in cheap renewables.
• Conclusion: The cheap prices are economically "real" for the end user, but sustained by a dual-engine: elite architectural efficiency paired with state-backed infrastructural subsidies.

2. India's Answer: Its Own Models and Data Centres

India does not want to depend fully on "renting" AI from a few US companies or on Chinese state-backed models. Its "Sovereign AI" push focuses on Indian data, models that understand Indian languages and culture, and large data centres inside the country.

A. Indian AI Models

• Krutrim: Developed by Ola Krutrim AI Labs, this foundational LLM is trained on the largest known collection of Indic data.
• Sarvam AI: Backed by the government's IndiaAI Mission, Sarvam builds models from scratch (e.g., Sarvam-105B) optimized for Indian languages.
• Jio Brain: Reliance’s enterprise AI/ML platform integrating tightly with its 5G edge network.
• Bhashini: A DPI project providing open-source APIs for speech-to-text and translation.

B. Indian Data Centres and Computing Power

• Adani Group: Committing $100 billion by 2035, Adani is building an "energy-compute" model, powering high-density AI infrastructure with massive solar/renewable grids.
• Reliance Industries: Committing roughly $110 billion, Reliance is building multi-gigawatt data centers in Jamnagar (including a hub leased to Meta).
• Yotta Data Services: Operating the Shakti Cloud, Yotta is scaling to over 85,000 NVIDIA GPUs, hosting the sovereign Government Community Cloud.

C. Developers and Digital Public Infrastructure

• Developer Boom: India is currently #2 globally on GitHub (projected #1 by 2028) and constitutes roughly 10.4% of total traffic on Hugging Face. This grassroots army is shifting India from an IT services consumer to an open-source AI contributor.
• DPI (Digital Public Infrastructure): Just as India scaled Aadhaar and UPI, it is treating foundational AI models as digital public goods. India is actively exporting this DPI framework to the Global South as a democratic alternative.