The inference opportunity for India
The country has one of the world's largest pools of software developers and digital creators. GitHub says India already has 21.9 million developers and added 5.2 million new developers in 2025 alone. These users are the people adopting AI fastest: coding assistants, content generation, video workflows, design, research, documentation, translation, marketing and automation.
Most of that spend happens not at the model-training layer but at the inference layer. Inference is where trained models are actually used to generate outputs: predictions, text, code, images, audio or video. NVIDIA defines inference as the process where a trained model applies learned knowledge to new inputs in real time.
That is where the money is flowing every day.
For Indian users, much of this spend currently goes to foreign platforms: ChatGPT, Claude, Gemini, Cursor, Midjourney, Runway, ElevenLabs, OpenRouter, Replicate and other global API and app providers. For heavy users this is already meaningful. A single professional paying $20, $100 or $200 a month is not unusual. At national scale, developer and creator AI usage is becoming a serious recurring forex outflow.
The lowest-hanging opportunity is not necessarily to build a frontier model from scratch. That is expensive, uncertain and research-heavy. The more practical opportunity is to build a sovereign Indian inference cloud: ready-made API endpoints for high-quality open-source models, optimised for coding, content generation, Indian languages, OCR and document AI, voice, and business automation.
This is a better first business than foundation-model training because:
- Demand already exists. Developers and creators are already paying.
- Open-source models are good enough for many workloads. Coding, summarisation, content generation, translation, OCR, extraction, voice and agent workflows do not always need the most expensive frontier model.
- Billing can be prepaid. API credits, prepaid wallets and monthly packs create strong cash flow.
- Gross margin improves with utilisation. Once GPU infrastructure is purchased or leased, the business becomes a utilisation game.
- Backward integration remains optional. Cursor is a good example of an application-layer company that created monetisation first, then gained leverage to move deeper into models and infrastructure. Public reporting suggests Cursor scaled extremely fast, crossing very large ARR milestones within a short period.
India is already moving in this direction. The IndiaAI Mission says eligible users can access subsidised AI compute, with 18,000+ GPUs mentioned on the compute portal, while later government communications reported 38,000+ GPUs onboarded under the mission. Sarvam is building Indian-language sovereign models. Companies like Yotta, E2E and Neysa are building AI and GPU infrastructure.
But the gap is still open: India needs a simple, reliable, developer-friendly inference product, not just GPU rental.
The winning product would look like this:
OpenAI-compatible APIs, billed in rupees, hosted in India, with optimised open-source models for coding, content, Indian languages, OCR, speech and enterprise automation.
The real moat would not be GPUs alone. The moat would be model routing and cost optimisation; low-latency serving; fine-tuned models for Indian business use cases; SDKs and OpenAI-compatible APIs; prepaid rupee billing; enterprise data-residency positioning; and reliability and throughput.
The challenges are real: capex, GPU procurement, power, cooling, utilisation, serving efficiency, model benchmarking, abuse control, support and pricing discipline. But these are execution challenges, not unsolved research problems.
India should not wait to win the frontier-model race before participating in the AI economy. The immediate opportunity is to win the inference layer for Indian developers, creators, startups, SMEs and enterprises. That layer is where usage happens, where revenue is recurring, where forex is leaking, and where a strong domestic platform can be built before moving upstream into proprietary models.