OPINION | India's AI Boom Has A Missing Piece: Affordable, Reliable Cloud Infrastructure

India's AI ambitions need more than GPUs. Affordable domestic cloud infrastructure could help businesses overcome rising costs, integration hurdles and data governance challenges.

India needs affordable domestic cloud infrastructure to turn growing AI computing capacity into enterprise adoption.

Source : ABPLIVE AI

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Key points generated by AI, verified by newsroom

• India's AI enterprise deployment struggles despite computing power access.
• High costs, integration, unreliable infrastructure, and data management hinder AI.
• Domestic cloud infrastructure offers affordable, integrated, compliant AI solutions.

By Manoj Dhanda

India is expanding its artificial intelligence capabilities, with growing access to computing power and GPU infrastructure. However, the increase in computing power has not translated into large-scale deployment of AI in enterprises. Many companies in India are struggling with high cost unpredictability, integration issues of AI with current systems, unreliable infrastructure, data management, and technical complexities of implementing AI solutions.

This is where Indian cloud infrastructure plays its role. Domestic cloud service providers could assist in addressing these gaps by providing affordable computing power, adaptable GPU resources, data hosting facilities, and infrastructure for the deployment of AI in a seamless manner. The future of Indian AI development lies not only in increased availability of computing power but also in the development of a domestic cloud environment to help businesses deploy AI.

Growing Compute Capacity Is Only the First Step

India has made progress in expanding access to AI computing. Increased accessibility to GPUs in India is a positive development as far as the adoption of AI technology goes, but having access to computing power does not necessarily mean successful application of AI in enterprises. For a large number of Indian companies, the issue is not so much about access to GPU but rather how well one can utilize the available computing power for the actual production of AI technologies.

With the applications moving out of the experimental phase, companies have to deal with increasing demands on their computing power while still keeping their costs predictable. This becomes especially difficult due to the dollar-based pricing in clouds, where a 5 percent rupee depreciation can make a month's cloud bill increase by hundreds of thousands of rupees. The rupee-based domestic infrastructure eliminates this currency risk.

Unpredictable Costs Can Slow Enterprise Adoption

One of the major constraints for adopting artificial intelligence technology on a wider scale is the cost of processing workloads. Training and tuning of machine learning algorithms may take a lot of GPU power, but making inferences with a trained algorithm will lead to ongoing computation costs. Other expenses include costs of storage, network connectivity, and data transport.

In the case of enterprises that have limited funds for technology solutions, such expenses are not easy to evaluate in terms of the profit that the implementation of the technology can bring. An application that works fine on a short-term trial basis may turn out to be costly upon increased usage.

In this case, domestic cloud providers may offer the solution due to reasonable pricing, appropriate GPU setups, and computing flexibility. Enterprises should be able to choose resources according to their workload requirements, increase computing capacity upon increased demand, and avoid any unnecessary spending on infrastructure.

Businesses Need More Than GPU Access

The use of artificial intelligence applications does not happen in isolation but requires connection with databases, CRM, ERP systems, and other enterprise applications. These AI applications also need secure storage capabilities, networking and data pipelines.

If this integration fails, companies could experience delays and poor performance, besides, integration itself could be costly. Small companies may also lack specialized IT professionals to deal with this infrastructure. Local cloud computing infrastructures offer the ability to create an environment for these connected workloads. The opportunity to have access to different types of GPUs will allow companies to allocate proper resources for their needs.

Data Governance Makes Domestic Infrastructure More Relevant

AI applications often process sensitive information, including customer records, financial details, employee data and proprietary business information. Organisations must understand where this information is stored, how it is processed and who can access it.

Using domestic infrastructure can give businesses greater clarity and control over data location and operational arrangements. It can also help organisations design systems around applicable Indian regulatory requirements, including those relevant to personal data protection. For sectors handling sensitive information, the ability to choose an appropriate domestic hosting environment can therefore be an important consideration when moving AI applications into production.

Building an Indian Cloud Ecosystem for Enterprise AI

India requires a cloud infrastructure that takes care of the issues faced by companies in implementing AI in terms of cost, integration, reliability, scalability and data management. It goes beyond just providing GPUs. It requires building an ecosystem where computing, storage, network and technical support come together.

Providing India-hosted cloud and GPU infrastructure along with clear pricing and technical support will enable Indian clouds to assist in implementing AI workloads and have control over their infrastructural decisions. The larger goal is to make advanced computing available to businesses of all sizes, instead of restricting the implementation of AI workloads to big-budget companies with specialized infrastructure teams.

From AI Experimentation to Enterprise Deployment

The increased computing capacity in India is an essential foundation for India’s upcoming phase of AI adoption. However, success in its implementation will be determined by the extent to which companies in India manage to leverage their computing capacity for building solutions to their operational challenges and for decision-making.

Increased availability of local cloud infrastructure and GPU capacity will go towards closing this gap by providing access to cost-effective, robust, and locally compliant solutions. India’s next phase of AI adoption requires the development not just of models but also of the infrastructure for implementing them.

(The author is the Founder and CEO of Utho Cloud)

Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.

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