India’s AI developer community is heavily dependent on open-source technology, with 96.2% of developers surveyed saying they use open-source tools in their work, according to a study by The Dialogue. The finding highlights the central role that freely available software, models, frameworks and developer infrastructure play in India’s rapidly expanding artificial intelligence ecosystem.
The finding is significant because open-source technology can lower the cost of experimenting with AI, allow developers to customise systems and reduce dependence on a small number of proprietary technology providers. India already has a strong presence in global open-source AI development: Stanford’s AI Index found that developers in India accounted for 24.2% of AI-related GitHub projects in 2022, ahead of the European Union and UK at 17.3% and the US at 14%.
96.2% Of Indian AI Developers Use Open-Source Tools
The Dialogue study’s headline finding is that 96.2% of India’s AI developers rely on open-source tools.
The high adoption rate points to open source becoming a foundational part of the country’s AI development ecosystem rather than a niche alternative used only by hobbyists or research teams.
Open-source tools can include software libraries, frameworks, models and other components that developers can access, modify or integrate into their own applications.
India’s AI Developer Open-Source Adoption
| Indicator | Figure |
|---|---|
| Indian AI developers using open-source tools | 96.2% |
| Developers not represented by the 96.2% figure | 3.8% |
| AI GitHub projects contributed by India in 2022 | 24.2% |
| EU + UK share of AI GitHub projects | 17.3% |
| US share of AI GitHub projects | 14.0% |
| China share of AI GitHub projects | 2.4% |
The 24.2%, 17.3%, 14.0% and 2.4% figures come from Stanford’s AI Index 2023 and should not be interpreted as results from The Dialogue’s developer survey.
The combination of the two datasets provides useful context: India’s developer community has both high open-source adoption and a substantial presence in global AI software development.
Open Source Has Become A Major Part Of India’s AI Stack
The importance of open source extends beyond individual developers.
AI applications increasingly depend on multiple layers of technology, from programming frameworks and machine-learning libraries to model infrastructure and deployment tools.
Developers can combine these components rather than building every part of an AI system from scratch.
How Open Source Fits Into AI Development
OPEN-SOURCE SOFTWARE ↓AI FRAMEWORKS & LIBRARIES ↓OPEN MODELS / MODEL TOOLS ↓DEVELOPER EXPERIMENTATION ↓AI APPLICATIONS ↓COMMERCIAL PRODUCTS
This modular approach can reduce development costs and allow startups and smaller teams to experiment with AI without having to build an entire technology stack themselves.
India Is Already A Major Contributor To AI Software
The Dialogue study’s 96.2% adoption figure comes against a backdrop of India’s growing contribution to open-source AI software.
Stanford’s AI Index reported that India accounted for 24.2% of AI-related GitHub projects in 2022, making it the largest geographic contributor in the dataset. The European Union and UK accounted for 17.3%, while the US represented 14%.
Share Of AI GitHub Projects
AI-Related GitHub Projects By Geographic Area
Share of AI-related GitHub projects in 2022, according to Stanford’s AI Index.0%15%30%45%IndiaEU + UKUSChinaRest of world
Source: Stanford AI Index 2023; GitHub and OECD.AI data.
The data shows that India’s contribution to AI-related open-source software was already substantial before the latest wave of generative AI adoption.
India Had 347,934 AI GitHub Projects In 2022
The overall number of AI-related GitHub projects has also grown dramatically.
Stanford’s AI Index found that the number of AI-related GitHub projects increased from just 1,536 in 2011 to 347,934 in 2022.
That represents an increase of more than 200 times over the period.
| Year | AI-Related GitHub Projects |
|---|---|
| 2011 | 1,536 |
| 2022 | 347,934 |
| Increase | ~226X |
This expansion demonstrates how dramatically the open-source AI software ecosystem has grown over the past decade.
AI GitHub Project Growth
20111,536 projects │ │ │ ▼2022347,934 projects≈226X increase
The growth has created a much larger pool of reusable software and technical knowledge for developers around the world.
Why Indian Developers Prefer Open Source
The popularity of open source can be explained by several practical advantages.
For developers and startups, open-source technology can reduce upfront costs and provide greater flexibility than proprietary systems. Developers can inspect available code, modify components and build applications around technology that is not entirely controlled by a single vendor.
Key Advantages
| Advantage | Why It Matters |
|---|---|
| Lower entry costs | Easier for startups and individual developers to experiment |
| Customisation | Developers can modify technology for specific applications |
| Flexibility | Reduces dependence on one provider |
| Community support | Large developer communities can contribute improvements |
| Experimentation | Makes it easier to test different approaches |
| Local deployment | Some tools can be deployed within a company’s infrastructure |
These benefits are particularly relevant in a fast-moving AI market where developers may need to experiment with multiple models and frameworks.
Open Source Can Reduce AI Development Costs
Developing frontier AI models from scratch can require enormous amounts of computing power, data and specialist talent.
Most Indian startups and developer teams cannot replicate the resources available to the world’s largest AI companies.
Open-source models and software can therefore provide an alternative route.
Instead of developing a foundation model independently, a company can start with an existing model or software component and focus its resources on fine-tuning, application development, user experience and deployment.
Proprietary Vs Open-Source Approach
| Factor | Proprietary Technology | Open-Source Technology |
|---|---|---|
| Initial access | Often paid | Often freely available |
| Customisation | Provider dependent | Generally greater |
| Vendor dependence | Higher | Potentially lower |
| Source visibility | Limited | Often available |
| Deployment flexibility | Depends on provider | Often broader |
| Community contributions | Limited to provider ecosystem | Can be extensive |
The precise licensing terms differ significantly between open-source projects, so “open source” should not automatically be interpreted as completely free for every commercial use.
Open Source Is Also Important For AI Startups
For startups, the ability to build on existing technology can shorten development cycles.
A small team can combine an open model with existing software libraries and cloud infrastructure to create an AI product without having to build every component internally.
This could help explain why open-source adoption is particularly important in India’s startup ecosystem.
Startup Development Model
OPEN-SOURCE COMPONENTS +DEVELOPER SKILLS +COMPUTING INFRASTRUCTURE ↓ AI MVP ↓USER TESTING ↓CUSTOMISATION ↓COMMERCIAL PRODUCT
This model allows startups to concentrate capital and engineering resources on differentiation rather than rebuilding foundational software.
Open-Source AI Does Not Mean OpenAI Models
One important distinction is necessary when discussing open-source AI.
Open-source software refers to technology whose source code and licensing permit forms of access, modification and redistribution. Open-weight AI models, meanwhile, may make model parameters available without necessarily satisfying every definition of open-source software.
The terms are therefore sometimes used interchangeably in industry discussions even though they can describe different levels of openness.
For developers, the exact licence and usage restrictions matter.
India’s Open-Source Strength Could Support AI Innovation
The high adoption rate could become an advantage as India attempts to build a larger domestic AI ecosystem.
A large developer community familiar with open-source technology creates a potential base for experimentation across sectors such as financial services, healthcare, education, manufacturing and software development.
India’s earlier performance on GitHub also suggests that the country’s developers are not merely consuming open-source AI tools but are contributing to the wider ecosystem. Stanford’s AI Index data placed India ahead of the US in the share of AI-related GitHub projects in 2022.
India’s Open-Source AI Position
HIGH DEVELOPER ADOPTION +LARGE SOFTWARE TALENT BASE +24.2% SHARE OF AI GITHUB PROJECTS +GROWING AI STARTUP ECOSYSTEM ↓Potentially Strong Open-Source AI Base
The 96.2% figure therefore fits into a broader pattern of strong developer participation in open-source AI.
Open Source Could Help Reduce Vendor Lock-In
Another advantage is the ability to avoid becoming completely dependent on a single AI provider.
Businesses using proprietary APIs may become dependent on a provider’s pricing, availability, model roadmap and technical architecture.
Open-source alternatives can provide another option, particularly when companies want greater control over where models are deployed or how they are modified.
| Concern | Open-Source Potential |
|---|---|
| Provider pricing changes | Ability to consider alternatives |
| API availability | Local deployment may be possible |
| Model customisation | Greater flexibility |
| Data control | Potential for private deployment |
| Vendor lock-in | More options across providers |
However, open-source systems can also create their own costs, including infrastructure, maintenance, security and specialist engineering requirements.
Open Source Still Has Challenges
High adoption does not mean open-source technology is without limitations.
Companies using open-source AI may have to manage their own infrastructure, monitor security vulnerabilities, evaluate model performance and maintain software dependencies.
There can also be uncertainty around licensing, model quality and long-term project support.
Open-Source Trade-Off
| Benefits | Challenges |
|---|---|
| Lower entry barrier | Infrastructure costs |
| Customisation | Maintenance |
| Flexibility | Security responsibility |
| Community innovation | Variable quality |
| Reduced vendor dependence | Licensing complexity |
| More deployment options | Technical expertise required |
For large enterprises, the total cost of ownership can therefore be substantially different from the initial cost of downloading an open-source model or tool.
India’s Developer Ecosystem Could Shape AI Adoption
The 96.2% figure is particularly significant because developers are the people who determine which technologies eventually become part of commercial applications.
If a large majority of India’s AI developers already use open-source tools, the ecosystem could continue to favour technology that is modular, customisable and accessible.
That could also influence the development of domestic AI startups.
Potential Ecosystem Effect
More Open-Source Adoption ↓More Developer Experience ↓More AI Experimentation ↓More Startups & Applications ↓More Contributions ↓Larger Open-Source Ecosystem ↓More Adoption
This creates a potential network effect in which developers, startups and research institutions reinforce one another.
India Has A Strong Open-Source Developer Base
The Dialogue’s latest finding is not an isolated indicator of India’s relationship with open-source technology.
Stanford’s AI Index found that India represented 24.2% of AI-related GitHub projects in 2022. The same report recorded 347,934 AI-related GitHub projects globally that year, up from 1,536 in 2011.
These figures indicate that Indian developers have become an important part of the global open-source AI software ecosystem.
Key Numbers
96.2%Indian AI developers using open-source tools24.2%India's share of AI-related GitHub projects in 2022347,934AI-related GitHub projects globally in 20221,536AI-related GitHub projects globally in 2011~226XIncrease in AI GitHub projects, 2011–2022
The figures come from different datasets and years, so they should not be combined as if they were measurements from the same survey.
The Bigger Picture
The Dialogue’s finding that 96.2% of India’s AI developers rely on open-source tools highlights how important open technology has become to the country’s AI development ecosystem. Open-source software can lower barriers to experimentation, provide developers with greater flexibility and help startups build AI applications without developing every component from scratch.
The finding also aligns with India’s broader position in open-source AI development. Stanford’s AI Index found that India accounted for 24.2% of AI-related GitHub projects in 2022, while the global number of AI-related projects had grown to nearly 348,000.
Looking Ahead
India’s high reliance on open-source AI tools could become an important advantage as developers and startups seek affordable ways to experiment with increasingly capable AI systems. The country’s large developer base and strong contribution to AI-related GitHub projects provide a foundation for building applications, customising models and contributing back to the global technology ecosystem.
The next challenge will be turning adoption into durable innovation. Developers and companies will need to balance the benefits of open technology with infrastructure costs, security, licensing and maintenance requirements. If India can combine its strong open-source developer culture with domestic computing capacity, research and startup investment, open-source AI could become an increasingly important pillar of the country’s broader AI ambitions.
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