Paytm Partners with U.S.-based Groq to Power Real-Time AI Across Its Payments Platform

Paytm has entered into a partnership with U.S.-based Groq to accelerate real-time artificial intelligence (AI) capabilities across its financial and merchant services ecosystem. The collaboration marks a major step in Paytm’s efforts to deploy faster, more scalable, and cost-efficient AI systems, aimed at enhancing transaction processing, fraud detection, and customer engagement on its platform.

Under the agreement, Paytm and its associate entities will integrate GroqCloud, a cloud service powered by Groq’s purpose-built Language Processing Unit (LPU) architecture. The technology enables significantly faster and more cost-effective AI inference compared with conventional GPU-based systems, allowing large-scale applications to process data in real time with lower latency.

In a statement, Narendra Singh Yadav, Chief Business Officer at Paytm, said the partnership reinforces the company’s long-term focus on deep AI integration within its payment infrastructure. “We have been steadily advancing our AI capabilities to make payments faster, more reliable, and deeply intelligent,” Yadav said. “This collaboration with Groq strengthens our technology foundation by enabling real-time AI inference at scale. It marks another step in our journey to build India’s most advanced AI-driven payment and financial services platforms.”

Groq, a California-based company recognized for its leadership in low-latency AI inference, has been rapidly expanding its presence in Asia-Pacific. Scott Albin, General Manager of APAC for Groq, described the partnership as a milestone in deploying AI at national scale. “Groq is proud to support Paytm in driving real-time AI innovation at national scale,” Albin said. “Core to our mission is delivering broad compute capacity to serve the world’s biggest problems which AI will uniquely solve. Paytm’s ambition closely aligns with our own to make AI useful and accessible.”

The alliance arrives at a time when Indian fintech companies are racing to embed AI across critical functions—from fraud detection and credit scoring to personalization and customer service. For Paytm, the move builds upon its ongoing investments in AI-driven risk modeling, customer onboarding, and transaction security, areas where it has already leveraged automation and machine learning to manage millions of daily interactions. By adopting Groq’s high-performance inference technology, Paytm aims to expand those capabilities into new domains such as real-time anomaly detection, predictive analytics, and dynamic fraud prevention.

Paytm’s AI push also comes amid intensifying competition in the digital payments market, where players such as PhonePe, Google Pay, and Amazon Pay are expanding their data analytics and AI models to capture user insights and strengthen fraud control mechanisms. The company’s emphasis on leveraging specialized processors like Groq’s LPUs reflects a broader industry trend of moving away from traditional GPU-heavy models to domain-specific architectures optimized for low-latency workloads.

For Groq, the deal with Paytm represents a strategic foothold in one of the world’s fastest-growing digital economies. The company’s hardware and cloud solutions are designed to deliver deterministic performance—meaning results are both fast and predictable—a critical feature for real-time financial systems handling millions of microtransactions per second.

The collaboration also aligns with India’s broader vision of building a more intelligent financial infrastructure. As digital transactions surge and data volumes multiply, the need for efficient, AI-powered platforms becomes essential to maintaining both scalability and security. With Groq’s inference technology underpinning its infrastructure, Paytm appears poised to strengthen its role as a technology leader in India’s evolving financial ecosystem.

In the coming months, the companies are expected to work on expanding the deployment of GroqCloud across Paytm’s various verticals, including payments, lending, and insurance distribution. If successful, the initiative could set a benchmark for how large-scale AI inference can be deployed to power national financial networks in emerging markets.