Why Open Source Is the Way to Go for AI in India
In 2025, over five million Marathi-speaking farmers began receiving real-time crop alerts, weather predictions, and agri-market updates delivered in their native language. These updates weren’t powered by a global tech firm or hidden behind expensive paywalls. They ran on open-source language models trained on regional datasets and deployed through publicly funded infrastructure.
This was a nationwide service scaled through the Bhashini platform and fine-tuned by agritech startups working in tandem with state departments. And it worked, not because of billion-parameter models, but because the system understood the farmer’s context, language, and urgency.
Across India, similar stories are unfolding.
Open-source AI is being used to diagnose tuberculosis, optimize vaccine delivery, triage customer support, train developers, and localize governance. This shift reflects a growing national consensus: open-source AI is the most practical, scalable, and democratic way to meet India’s needs.
A foundation designed for inclusion
India’s AI infrastructure is being built around openness—of code, datasets, access, and goals. The Bhashini platform, for instance, already supports over 400 open-source models across 22 Indian languages. These models enable speech recognition, translation, and natural language understanding across sectors like healthcare, agriculture, education, and citizen services.
In a pilot at SGPGI Lucknow, voice-enabled hospital kiosks, powered by Bhashini-compatible open models, allowed patients to navigate departments and access reports in Bhojpuri and Awadhi. This reduced queue times and increased outpatient satisfaction in a hospital that serves over 12,000 patients daily.
The IndiaAI Mission, launched with a ₹10,300 crore allocation, reinforces this commitment at the infrastructure level. The government is deploying 18,000 GPUs across research and public service institutions and funding 67 AI projects, including 22 large language models and 45 domain-specific tools. These include AI models for medical imaging, soil health, financial inclusion, and school-level learning diagnostics.
This ecosystem is guided by a clear principle: make compute and data widely accessible, then allow communities to shape how AI is applied. Prime Minister Modi’s address at the 2025 Paris AI Summit summed it up with clarity: openness ensures participation, and participation builds trust.
At the Making an Adbhut India symposium, Nandan Nilekani emphasized India’s differentiated approach: “We’re not in the race to build the biggest model. We’re building for relevance, efficiency, and access. And we’re doing it with shared infrastructure.”
Tangible impact across sectors
Open-source AI has moved from potential to performance. According to a 2025 IBM India report, 76% of Indian companies using open-source AI tools reported positive ROI, with 71% planning to expand adoption.
Take Flipkart, for example. The company integrated Meta’s open LLaMA models into its customer service pipeline. Instead of routing queries to call centers, the AI handled routine interactions across multiple Indian languages, reducing human workload and cutting support costs by 40%. Importantly, the system was tailored to handle colloquial speech, regional idioms, and delivery-specific questions—something generic closed models struggled to manage.
In the telecom sector, IBM and Tata Elxsi partnered to deploy edge AI in rural areas with patchy connectivity. Their open-source-powered latency optimization tools improved data transmission speeds by 68%, enabling better call quality and access to emergency services in areas previously underserved.
Kissan AI’s Dhenu 1.0, built using the OpenHathi Hindi LLM as its backbone, delivers region-specific agricultural advice in 10 languages, including Maithili, Marathi, and Telugu. Farmers receive tailored information about sowing schedules, pest risks, and market prices.
During the 2024 kharif season, the system reported a 25% increase in yield across pilot districts, driven by timely planting recommendations and better pesticide usage.
These results stem from a common trait: flexibility. Open models allow Indian developers to localize, fine-tune, and redeploy with speed without needing to wait for global roadmaps or negotiate API restrictions.
The direction is set
India’s roadmap for open-source AI is already shaping up into a robust, future-facing ecosystem. Several national platforms are on the horizon:
A regulatory sandbox for AI tools in fintech and healthtech, supporting faster approval cycles for early-stage innovations.
The launch of BharatGPT, a multimodal model capable of powering text, voice, and image-based interactions across Indian languages.
The Srijan Center at IIT Jodhpur, which has already trained 8,000 developers on LLaMA-based model building.
India’s open approach is attracting attention abroad. Japan is adapting India’s open payments architecture for its own population-scale services, and interest from Southeast Asian governments in the Bhashini framework continues to grow.
A future built in the open
India’s AI strategy is grounded in deployment, shaped by users, and scaled through collaboration. Every major success, from hospital navigation to farm productivity, has come from systems that are transparent, adaptable, and community-informed.
AI built in the open encourages participation, strengthens trust, and keeps the focus on real-world use. India's model prioritizes reach, adaptability, and public good outcomes. That clarity of purpose is its biggest strength.