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Open source is one factor helping India’s AI market grow: it can lower barriers to experimentation, let organizations adapt AI to local languages and needs, and give them more control over where systems run and how data is handled. A February 2026 Linux Foundation Research report presents it alongside startup activity, talent, investment, digital public infrastructure, and skills—not as the market’s sole driver. Its figures include a projection, and its examples illustrate possible applications rather than prove universal results.
What the Linux Foundation report says about India’s AI market
The Linux Foundation Research report AI for Economic and Social Good in India: Scaling Inclusive Growth for Entrepreneurs, Creators, and Local Economies, published with Meta in February 2026, estimates India’s AI market at USD 3.2 billion in 2020 and USD 6 billion in 2024, and projects it could reach almost USD 32 billion by 2031. The 2031 figure is a projection, not a measured current market size. The report is the sixth in a sponsored series, by Hilary Carter and Anna Hermansen, and combines a literature review with semi-structured interviews with 12 leaders across sectors in India. Read the report.
The statistics it cites describe different years, populations, and measures, so they should not be treated as a single comparable survey. The report says 76% of Indian startups had built solutions using open source AI; it attributes that figure to the Competition Commission of India, rather than to a Linux Foundation survey. It also reports more than 200,000 Indian startups at the end of 2025 and says India ranked fourth globally for newly funded AI companies in 2024.
For enterprise adoption, the report cites NASSCOM’s 2024 adoption index: 87% of Indian enterprises were actively using AI solutions. That index was based on a survey of 500 companies. These figures point to a growing ecosystem, but they do not establish that open source alone caused the growth.
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The report’s case for open source rests on practical options rather than a promise that every open model is cheaper or better. Organizations may be able to inspect and adapt a model, choose where it runs, and avoid relying entirely on a proprietary platform or third-party API. The benefits depend on the model, infrastructure, staffing, and governance required for a particular deployment.
#1 Best Overall
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- Lower barriers to experimentation: Startups and smaller organizations can try models and tools without committing to the cost structures of a proprietary platform. Compute, integration, and maintenance still cost money.
- Local adaptation: Teams can tune or build around models to better fit Indian languages, workflows, and cultural contexts. This can matter for public services and consumer products used by people who do not primarily use English.
- Deployment and data control: Local hosting can help organizations meet sensitive-data or data-location requirements and reduce dependence on external APIs. It also means the organization must operate and secure the system itself.
- Transparency and modification: Access to model components and documentation can support inspection, study, and changes. The degree of openness depends on the actual license and what materials the publisher releases.
For this report, an “open model” follows the Generative AI Commons’ Model Openness Framework: a machine-learning model whose architecture, parameters—including pretrained weights and biases—and documentation are released under permissive licenses that allow use, study, modification, and redistribution. A product described as “open” does not necessarily meet that definition; check its license and released materials.
Examples the report highlights
The report uses examples from several sectors to show where AI systems may be applied. These are selected case studies, not a comparative evaluation or an independent audit of outcomes.
Courts and legal workflows
Adalat AI applies models and tools to courtroom workflows such as transcription and documentation, with the aim of improving throughput and reducing delays. Its co-founder Arghya Bhattacharya says: “Open source is the only way this works. We cannot send data outside the country or rely on third-party APIs, so we build on open models, fine-tune them, and host everything in-house.”
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- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
Clinical decision support
Caze Labs’ MeTProAI uses locally hosted models for physician-support tasks, including summarizing standard treatment procedures based on patient details. The report describes decision-support tools, not replacements for clinicians’ judgment. Caze Labs co-founder Sanil Kumar says: “For a startup like ours, open source is what makes innovation possible—we can experiment, customize, and use smaller models where large ones are unnecessary, all without the cost structures of proprietary platforms.”
Agriculture and agroforestry
Farmers for Forests connects AI-supported monitoring and computer vision with smallholder farmers’ transition toward agroforestry and fruit trees. The Linux Foundation’s release says the organization’s work can increase incomes by up to 3–5x. That is the release’s description of this case example, not a national result or an independently established effect across farmers. Read the release.
Multilingual access and creators
Bhashini and Sarvam AI are cited as multilingual systems intended to reduce language barriers and expand access to digital services. The report also says AI tools can lower production costs for creators and help them make culturally and linguistically relevant material. These are opportunities described by the report, not guarantees for every creator or service.
Rank #3
- Single core ARM Cortex-A7 32-bit core, integrated with NEON and FPU
- Built in Micro's self-developed 4th generation NPU, with high computational accuracy and support for mixed quantization of int4, int8, and int16. Among them, int8 has a computing power of 0.5 TOPS and int4 has a computing power of up to 1.0 TOPS
- Built in self-developed 3rd generation ISP3.2, supports 4 million pixels, and supports various image enhancement and correction algorithms such as HDR, WDR, and multi-level denoisin
- It has powerful encoding performance, supports intelligent encoding, adapts to save bit rates according to the scene, and saves more than 50% of the bit rate compared to conventional CBR mode, making the captured images high-definition, smaller in size, and doubling the storage space
- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
What open source does not solve
Open source is an enabling approach, not a shortcut around the resources and risks involved in deploying AI. The report identifies unequal access to compute, differences in digital literacy, urban-rural divides, and potential workforce disruption as constraints. Local hosting may improve control over deployment but requires the skills and infrastructure to maintain a secure, reliable system.
The Linux Foundation’s February 2026 release summarizes an estimate that 45–69% of jobs in manufacturing, customer service, and retail could potentially be affected by automation by 2030. “Affected” indicates potential exposure; it does not mean that all those jobs will disappear. The report argues for applied AI training and reskilling as adoption expands. It cites Skill India Digital Hub as an example of a service that helps people find training centers and jobs in local languages. Sarvam’s head of Edge AI, Tushar Goswamy, says: “When the Prime Minister speaks about skilling and digital empowerment, it sends a signal that AI is not just for technology companies, but for every citizen who wants to participate in India’s future workforce.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What organizations should weigh before choosing an approach
The report does not provide a controlled comparison of open and proprietary products. For a real project, compare the deployment options against the requirements of the use case rather than assuming that one label determines the outcome.
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- Cost and infrastructure: Include compute, implementation, ongoing operations, and maintenance—not just model access.
- Data and deployment: Decide whether sensitive information can leave the organization and whether locally hosted systems are necessary.
- Language and context: Establish which languages, regional variations, and workflows the system must handle, then evaluate whether it can be adapted sufficiently.
- Licensing and transparency: Verify which components, weights, architecture, and documentation are actually available, and what the license permits.
- People and governance: Ensure there are staff to evaluate, secure, update, and monitor the system, with clear accountability for its decisions and effects.
What the report recommends for inclusive growth
The report’s recommendations extend beyond model availability. It calls for investment in applied AI training and reskilling, better access to localized and multilingual infrastructure, support for open models and tools, and help for small and medium-sized businesses adopting AI. It also recommends measuring AI’s economic impact, supporting secure and responsible AI research, and building policy frameworks that involve multiple stakeholders.
Those proposals reflect the report’s central balance: open tools may broaden who can build and adapt AI, but inclusive growth also depends on access to compute, skills, infrastructure, and safeguards. Because the report is a literature review and qualitative interview study—not a randomized trial or census of Indian deployments—its projections and case examples should be read as evidence of opportunity and reported experience, not proof that every organization will see the same results.
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