Open-source adoption meets enterprise hurdles in India
Open-source technologies are gaining ground across India’s enterprise sector as companies accelerate artificial intelligence deployments, but ageing systems, cybersecurity concerns, regulatory obligations and shortages of specialised skills are complicating the shift.
Businesses are increasingly evaluating open-source software, AI models and cloud-native platforms as alternatives to proprietary technologies. The attraction is being reinforced by the need to control costs, avoid dependence on individual vendors and customise AI systems around industry-specific requirements. Yet the transition is exposing weaknesses in enterprise technology estates that were built over decades and were not designed for data-intensive AI workloads.
Legacy infrastructure remains one of the biggest obstacles. Banks, telecommunications companies, manufacturers and large service providers often operate critical applications on older platforms that cannot easily communicate with modern open-source frameworks. Moving such workloads can involve extensive application redesign, data migration and testing while companies must continue running essential operations without disruption.
The challenge has become more prominent as enterprises seek to take AI projects beyond experimentation. Nearly three-quarters of organisations in India have encountered difficulties moving AI initiatives beyond the proof-of-concept stage, with fragmented data, outdated infrastructure and processes designed for earlier generations of technology among the principal constraints.
Open source is nevertheless playing a growing role in AI development. About 76% of startups in India use open-source AI, reflecting the lower entry costs and greater flexibility offered by openly available models and development tools. The country’s AI market is projected to expand from about $6 billion in 2024 to almost $32 billion by 2031 as companies, startups and public institutions increase deployment.
Enterprise interest has followed the same direction. A study of technology decision-makers found that 71% of companies surveyed planned to increase their use of open-source solutions, while almost half said more than half of the AI solutions they were already using were based on open-source technologies. Governance, AI expertise and technology integration ranked among the main barriers to expansion.
Security is a particularly sensitive issue for enterprises adopting open-source components. Companies must track vulnerabilities across libraries, frameworks and software dependencies while ensuring that updates do not disrupt production systems. The growing use of generative and agentic AI adds further concerns around access controls, sensitive corporate information, model behaviour and the provenance of training or application data.
Information-security spending in India is projected to reach $3.4 billion during 2026, an increase of 11.7% from the previous year, as companies respond to AI-enabled threats and tougher regulatory requirements. Identity attacks, credential compromise and deepfake-enabled fraud are pushing businesses towards stronger detection, governance and resilience measures.
Compliance is also influencing technology choices. Enterprises handling personal or sensitive information must consider requirements created by the Digital Personal Data Protection framework, sector-specific regulations and data-residency obligations. Multinational companies face an additional layer of complexity because AI applications may process information across jurisdictions with different privacy and governance rules.
These requirements are driving demand for architectures that combine open technologies with enterprise-grade management, security and support. Technology suppliers are consequently positioning hybrid platforms as a way to give companies the flexibility of open ecosystems while retaining controls over models, data and infrastructure.
IBM and Yotta Data Services, for example, announced plans this year for an agentic AI platform hosted on Yotta’s Shakti Cloud, aimed at helping enterprises and government organisations deploy AI while addressing data residency, security and regulatory compliance requirements.
Cloud investment is simultaneously accelerating. Public-cloud spending in India is forecast to reach $17.5 billion in 2026, rising 28.1% from $13.7 billion in 2025. Platform-as-a-service expenditure alone is expected to reach about $6.4 billion as organisations modernise technology foundations for AI and integrate data across applications.
Skills could prove just as important as infrastructure. Enterprises need engineers capable of managing containers, Kubernetes environments, open-source databases, machine-learning frameworks, cybersecurity tools and increasingly complex AI stacks. Demand is also shifting from general software skills towards specialists who understand data engineering, model governance, application modernisation and AI operations.
India possesses a large technology workforce and has recorded strong demand for AI professionals, but enterprise requirements are evolving faster than many organisations can retrain employees. The resulting gap is encouraging companies to expand internal training, recruit specialised engineers and work more closely with technology vendors and open-source communities.
Businesses are increasingly evaluating open-source software, AI models and cloud-native platforms as alternatives to proprietary technologies. The attraction is being reinforced by the need to control costs, avoid dependence on individual vendors and customise AI systems around industry-specific requirements. Yet the transition is exposing weaknesses in enterprise technology estates that were built over decades and were not designed for data-intensive AI workloads.
Legacy infrastructure remains one of the biggest obstacles. Banks, telecommunications companies, manufacturers and large service providers often operate critical applications on older platforms that cannot easily communicate with modern open-source frameworks. Moving such workloads can involve extensive application redesign, data migration and testing while companies must continue running essential operations without disruption.
The challenge has become more prominent as enterprises seek to take AI projects beyond experimentation. Nearly three-quarters of organisations in India have encountered difficulties moving AI initiatives beyond the proof-of-concept stage, with fragmented data, outdated infrastructure and processes designed for earlier generations of technology among the principal constraints.
Open source is nevertheless playing a growing role in AI development. About 76% of startups in India use open-source AI, reflecting the lower entry costs and greater flexibility offered by openly available models and development tools. The country’s AI market is projected to expand from about $6 billion in 2024 to almost $32 billion by 2031 as companies, startups and public institutions increase deployment.
Enterprise interest has followed the same direction. A study of technology decision-makers found that 71% of companies surveyed planned to increase their use of open-source solutions, while almost half said more than half of the AI solutions they were already using were based on open-source technologies. Governance, AI expertise and technology integration ranked among the main barriers to expansion.
Security is a particularly sensitive issue for enterprises adopting open-source components. Companies must track vulnerabilities across libraries, frameworks and software dependencies while ensuring that updates do not disrupt production systems. The growing use of generative and agentic AI adds further concerns around access controls, sensitive corporate information, model behaviour and the provenance of training or application data.
Information-security spending in India is projected to reach $3.4 billion during 2026, an increase of 11.7% from the previous year, as companies respond to AI-enabled threats and tougher regulatory requirements. Identity attacks, credential compromise and deepfake-enabled fraud are pushing businesses towards stronger detection, governance and resilience measures.
Compliance is also influencing technology choices. Enterprises handling personal or sensitive information must consider requirements created by the Digital Personal Data Protection framework, sector-specific regulations and data-residency obligations. Multinational companies face an additional layer of complexity because AI applications may process information across jurisdictions with different privacy and governance rules.
These requirements are driving demand for architectures that combine open technologies with enterprise-grade management, security and support. Technology suppliers are consequently positioning hybrid platforms as a way to give companies the flexibility of open ecosystems while retaining controls over models, data and infrastructure.
IBM and Yotta Data Services, for example, announced plans this year for an agentic AI platform hosted on Yotta’s Shakti Cloud, aimed at helping enterprises and government organisations deploy AI while addressing data residency, security and regulatory compliance requirements.
Cloud investment is simultaneously accelerating. Public-cloud spending in India is forecast to reach $17.5 billion in 2026, rising 28.1% from $13.7 billion in 2025. Platform-as-a-service expenditure alone is expected to reach about $6.4 billion as organisations modernise technology foundations for AI and integrate data across applications.
Skills could prove just as important as infrastructure. Enterprises need engineers capable of managing containers, Kubernetes environments, open-source databases, machine-learning frameworks, cybersecurity tools and increasingly complex AI stacks. Demand is also shifting from general software skills towards specialists who understand data engineering, model governance, application modernisation and AI operations.
India possesses a large technology workforce and has recorded strong demand for AI professionals, but enterprise requirements are evolving faster than many organisations can retrain employees. The resulting gap is encouraging companies to expand internal training, recruit specialised engineers and work more closely with technology vendors and open-source communities.