Indian enterprises are increasingly adopting a hybrid AI approach, using multiple artificial intelligence models to balance performance, cost, data security and greater control over their technology infrastructure.
Rather than relying on a single AI model or provider, companies are evaluating different models based on the nature of the task. More advanced proprietary models can be reserved for complex reasoning and high-value applications, while smaller or open-weight models can be deployed for routine workloads where affordability and flexibility are more important.
The shift is gaining traction as the AI market becomes increasingly competitive and businesses look beyond experimentation toward large-scale deployment. Running sophisticated models across multiple business functions can involve significant infrastructure and operational costs, making model selection an important part of an enterprise’s AI strategy.
Open-weight models are also strengthening the case for a multi-model approach. As these systems become more capable, enterprises have more options to customise, deploy and manage AI according to their specific requirements. This can be particularly relevant for businesses that want greater control over their data, infrastructure and deployment environment.
Data governance is another key factor behind the trend. Companies handling sensitive customer, financial and operational information are increasingly looking for AI architectures that give them stronger oversight of how data is processed. A hybrid setup can allow organisations to select models and deployment environments according to the sensitivity of individual workloads.
The strategy also reflects a change in how businesses evaluate AI technology. Instead of searching for one model that can handle every requirement, companies are increasingly matching specific models to specific business problems.
For India Inc, this approach could provide a more practical path toward scaling AI. As model capabilities continue to evolve and pricing remains an important consideration, using a mix of proprietary and open-weight systems could help enterprises optimise spending while maintaining performance, security and flexibility.
