DeepSeek vs. IndiaAI: Can India Create Cheaper AI? | IndiaAITrends

China's AI sector just received a boost with the arrival of DeepSeek AI, the LLM, which rose to global fame quickly. A reportedly $5 million development cost had DeepSeek AI challenge the cost structures of OpenAI and others and set the question - Can India make an AI model cheaper than that of China?

The Indian government has ambitious plans to create its own LLM under the IndiaAI Mission, with the aim of not only competing but also providing an affordable AI model tailored to India’s linguistic and cultural diversity.

In this article, we’ll explore how India plans to achieve this and whether the country has the resources and capabilities to build an AI model that competes with DeepSeek, both in performance and cost.

DeepSeek vs. IndiaAI: Can India Create Cheaper AI?

• What is in Article?

1. What is DeepSeek AI?
2. India's AI Landscape: Strengths and Challenges
3. Key Factors for AI Cost Reduction
4. Critical Challenges
5. Conclusion: Can India Compete?

What is DeepSeek AI?

DeepSeek AI, developed in China, is a highly advanced AI assistant designed to challenge Western AI systems such as OpenAI’s GPT models. What sets DeepSeek apart is its reported development cost of only $5 million a fraction of what major AI companies like OpenAI have invested in models like ChatGPT. This has sparked many raised eyebrows across the tech industry, as countries, including India, think about how they can create AI models that are not only competitive but also cost-efficient.

DeepSeek AI: Key Features

• Multimodal Capabilities: DeepSeek supports a variety of functions, such as text, image, and code generation.
• Efficient Training: DeepSeek was trained using a relatively modest 2,000 GPUs, allowing for significant cost reduction.
• Competitive Performance: Yet, DeepSeek is less expensive and still offers performance comparable to other AI models, or even those developed by the formidable OpenAI.

As a follow-up to China's remarkable AI assistant, its cost overrun then challenges India: Can it make an AI model more economical than China did for DeepSeek and simultaneously maintain quality for relevancy in the local regional markets?

India's AI Landscape: Strengths and Challenges

Strength

India has entered the AI space because of several strengths that give it an edge in competing with models like DeepSeek AI.

1. Strong IT and Research Sector: India has a robust IT sector with a vast pool of skilled engineers, developers, and researchers. Top-tier universities and research institutions are working on AI technologies in this country for years.
2. Government Support: The Indian government has introduced the IndiaAI Mission, with a budget of ₹10,370 crore ($1.2 billion), with the objective of speeding up AI development in the country. This is being achieved through funding for AI research, infrastructure development, and policy initiatives, such as establishing AI hubs across the country.
3. Massive Linguistic and Cultural Data: One of the unique advantages India has is its linguistic and cultural diversity. With over 22 officially recognized languages and hundreds of dialects, India can create AI models that cater specifically to its local languages and cultural nuances. This could make India’s AI models more relevant and accurate for Indian users compared to global models like OpenAI’s GPT.

Challenges

Despite these advantages, India still faces a few major drawbacks in the construction of its AI model that will make it cheaper than DeepSeek in China.

1. Hardware dependence: India is still heavily dependent on global suppliers for key hardware components such as GPUs. Even as India ramps up its infrastructure in AI, because it's so reliant upon imported hardware, the cost would skyrocket.
2. Energy Costs: AI training is an energy-intensive process, and India's energy prices could make this aspect of development expensive. However, the government is working on initiatives to address energy efficiency by promoting renewable energy sources.
3. Data Collection and Privacy: Ethical AI development requires large-scale, unbiased datasets. Ensuring data privacy and security while collecting such datasets is a challenge that India must navigate carefully, especially given the country’s diverse demographic.

Also Read :

• 5 AI Trends That Will Shape 2025
• India's AI Mission: Creating an indigenous generative AI model

Key Factors for AI Cost Reduction

To develop a competitive and affordable AI model, India must optimize several factors that directly impact the cost of AI development.

1. Computational Infrastructure: The AI infrastructure of India is still growing, but the government is showing major progress. The country has already chosen companies to provide 18,000+ GPUs for the IndiaAI Mission. Much could still be achieved by way of indigenous chips as it can further lower the costs in the field by cutting down dependency on foreign hardware.
2. Energy Efficiency: India can save much by incorporating renewable energy sources like solar and wind energy in the AI training system. This may help bring down the cost of operations, particularly when scaling the AI models.
3. Open-Source AI Integration: The use of open-source AI models and frameworks can significantly reduce the cost of development. India can use the existing AI tools and models as a base and build upon them, rather than starting from scratch.
4. Public-Private Partnerships: With the support of tech giants such as TCS, Infosys, and Wipro, already experienced in AI and machine learning, India would be able to speed up AI capabilities. The public-private partnerships can bring in funding and expertise, cutting down costs and enhancing efficiency.
5. Government Subsidies: IndiaAI Mission has announced a 40% subsidy on GPU access for researchers and academia. It can significantly bring down the expenses involved in the training of AI and model building, which makes India more advantageous in developing cheaper AI solutions.

Critical Challenges

Though India is making great progress, there are still many issues to be solved before it will be able to develop an AI model that could be compared to DeepSeek AI.

1. Hardware Supply Chain: Since currently most of the chips used for AI training are coming from US or Taiwan suppliers, development or access to affordable and high-performance hardware could be the key with which India reduces overall costs.
2. Scalability Issues: As the complexity of AI models increases, training them demands enormous computing resources. India must ensure that its AI infrastructure is scalable enough to support large models without incurring excessive costs.
3. Bias and Fairness: Considering the vast linguistic, cultural, and demographic diversity of India, the AI model developed here must be free from all bias and cultural insulation. The AI of India, therefore, has to portray the people with whom it will be interacting-with diversity and fairness in its predictions and outputs.

The above key challen must be addressed by India to guarantee it creates an AI model cheaper than China's DeepSeek.

Conclusion: Can India Compete?

India's AI aspirations are enormous, and with IndiaAI Mission, it is poised to develop an AI model that could compete not only with China's DeepSeek but also turn out to be less expensive. Given its deep talent pool, supportive government policies, and the drive to fill cultural and linguistic requirements, India will emerge as one of the big players in AI.

However, the biggest drawback is hardware dependence, which India can overcome by developing indigenous chips and optimizing GPU. If India manages to integrate open-source models, reduce energy usage, and further strengthen its relationship with the private sector, it is likely to build an AI model cheaper than China's DeepSeek.

The race is on, and the coming years will determine if India can truly build an AI model that offers both affordability and high performance, creating a competitive and accessible AI ecosystem for the world.