Indian organisations across sectors now have the option to store and process data locally, thanks to Google’s newly introduced Gemini 1.5 large language model (LLM). This development, announced during a media roundtable, marks a significant step forward in meeting the growing demand for data sovereignty and security within India, particularly in industries handling sensitive information like public services and financial institutions.
Bikram Bedi, Vice President and Country Managing Director at Google Cloud India, emphasised that the ability to keep data at rest and conduct machine learning processing within India addresses one of the major concerns for enterprises operating in highly regulated environments.
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Data Protection Laws In Play
This move also comes at a time when Indian businesses are increasingly looking for ways to comply with data protection laws while maintaining operational efficiency. By offering localised processing under the Gemini 1.5 model, Google provides a solution that enables organisations to meet regulatory requirements without compromising performance or cost efficiency.
Bedi further highlighted that startups in India will particularly benefit from this shift. The local processing capabilities of the Gemini 1.5 model are expected to lower expenses, improving operational speed and efficiency for various sectors.
Google's Local Push
What sets Google apart, according to Bedi, is its end-to-end AI stack. Unlike other providers that rely on third-party GPUs or AI technologies, Google delivers the entire infrastructure directly.
Bedi attributed the push for localised processing to both regulatory pressures and strong demand from the industry. With data protection laws tightening globally and the need for AI-powered solutions on the rise, Google's Gemini 1.5 LLM offers a timely solution for Indian enterprises looking to leverage AI without sending data outside the country.