As the global artificial intelligence boom places unprecedented strain on computing capacity and hardware supply chains, tech giant Nvidia has thrown its support behind Simplismart, an Indian AI infrastructure startup. Nvidia has participated in a $14 million funding round for the company, betting heavily on specialized software designed to help businesses run complex AI models far more efficiently—and, in select cases, with significantly fewer graphical processing units (GPUs).
The investment marks a significant milestone for the four-year-old startup, valuing the firm at approximately $100 million inclusive of the new capital injection. Nvidia joined an all-primary Series B funding round alongside prominent venture capital firms Accel, Shastra VC, and Micromax, with Dallas Venture Capital leading the syndicate.
The broader technology landscape has seen corporations pouring billions of dollars into acquiring and deploying high-end GPUs to power generative AI applications, machine learning pipelines, and large language models. However, moving beyond the initial deployment phase to run these resource-intensive models reliably, economically, and at true enterprise scale remains a persistent logistical and financial hurdle. Different enterprise workloads demand distinct, carefully balanced trade-offs between processing speed, operational cost, and overall performance. Deploying and operating AI models to seamlessly meet rigorous business requirements frequently demands deeply complex technical adjustments.
Simplismart positions its proprietary software as a bridge that automates much of that intricate labor. Instead of forcing corporate customers to manually manage low-level infrastructure configurations, the startup’s platform intelligently tailors how AI models execute on existing computing hardware.
"Nobody actually needs the fastest or the cheapest inference," explained Amritanshu Jain, co-founder and chief executive officer of Simplismart, during an interview discussing the core philosophy behind the company’s technology. He noted that enterprises instead require a pragmatic balance, needing to run AI models at speeds and cost structures that make sound economic sense for their specific use cases and applications.
Operating as an intelligent middleware layer, Simplismart’s software sits directly between deployed AI models and the underlying computing infrastructure. It dynamically adjusts how models are managed to satisfy specific customer Key Performance Indicators (KPIs). According to the startup, the technology optimizes critical operational factors such as response latency, request throughput, and energy or computing power consumption. By doing so, it enables organizations to extract substantially higher performance out of their existing hardware stacks without necessitating immediate, massive hardware upgrades.
Based in Bengaluru, Simplismart provides a versatile platform that allows corporations to deploy and execute AI models either through the startup’s native platform or directly within their own existing cloud and on-premises environments. Beyond deployment, the company offers a suite of developer tools designed to fine-tune models, automatically scale computing resources up or down as traffic demands fluctuate, and monitor real-time system performance. Additionally, the startup’s software is built to empower cloud providers to construct and operate their own dedicated AI inference services directly on top of raw GPU infrastructure.
The startup’s technology has already gained traction among prominent digital media and enterprise tech companies. Simplismart reports that its software enabled AI video-generation platform InVideo to slash its GPU serving costs by an impressive 56%. The enterprise roster also includes collaborations with other major AI video firms like HeyGen and Higgsfield, alongside enterprise AI startup Ema. Furthermore, Indian cloud service provider Yotta utilizes Simplismart’s technology to power the serverless AI inference service underpinning its Shakti Studio platform.
Commercial momentum has translated into rapid financial growth. Jain disclosed that Simplismart’s annualized revenue run rate has surged 14-fold over the preceding six months, elevating the metric to an eight-figure sum. This growth calculation is derived from recent customer usage patterns rather than strict contracted recurring revenue, and it carefully excludes revenue passed through directly to underlying infrastructure providers, according to company clarifications. Jain opted not to disclose the precise dollar figure. Notably, despite having a larger volume of individual customers located in India, more than half of Simplismart’s total revenue originates from the United States market.
Beyond the financial backing, Nvidia has actively collaborated with Simplismart on deep technical integrations. This engineering partnership includes joint work surrounding Nvidia’s Dynamo inference software. The cooperative effort brings together Simplismart’s internal engineering talent and Nvidia’s specialized solutions architects. Furthermore, Nvidia has leveraged its industry position to introduce the startup to prospective enterprise customers and strategic industry partners, according to Jain.
This strategic alignment introduces an apparent market paradox: Nvidia, universally recognized as the dominant supplier of physical AI chips and hardware accelerators, is actively financing software that enables companies to execute certain workloads using fewer GPUs. Jain, however, argued that this dynamic is mutually beneficial rather than contradictory. By making AI inference drastically more efficient and cost-effective, the software ultimately bolsters the value proposition of Nvidia’s hardware, making the chips more financially accessible and attractive to a broader base of enterprises, thereby encouraging wider, more sustainable adoption of AI applications globally.
Simplismart is navigating an increasingly crowded and competitive market for AI inference software. The Bengaluru firm competes not only with proprietary offerings from hardware manufacturers like Nvidia itself, but also with massive cloud hyperscalers including Amazon Web Services, Google Cloud, and Microsoft Azure. Concurrently, a burgeoning wave of venture-backed startups is developing specialized tooling aimed at making AI models faster and cheaper to execute, spurred by explosive demand for enterprise AI applications and the acute economic necessity of optimizing scarce, expensive GPU resources.
"Where we tend to win is where people have to be more stringent on their SLAs [service-level agreements] and TCO [total cost of ownership]," Jain remarked, pointing to scenarios dictated by demanding latency requirements and tight budgetary constraints. Unlike competitors that offer rigid, preset options geared solely toward either raw speed or maximum cost-reduction, Simplismart focuses on engineering a bespoke balance tailored precisely to each individual customer’s operational parameters.
Simplismart was established in 2022 by Jain, a former Oracle engineer, and Devansh Ghatak, a former Google engineer. The co-founders crossed paths while studying at BITS Pilani, drawing upon their collective professional experience in building and deploying complex machine learning systems to launch the enterprise. Today, the startup maintains a workforce of 58 employees.
Looking ahead, Simplismart intends to deploy its newly secured capital to aggressively expand its operational presence in the United States, scale its internal engineering and sales teams, and pour further investment into the ongoing research and development of its core inference software. Jain emphasized that the U.S. market remains a foundational pillar for the company’s future growth strategy as it looks to capitalize on its established customer base there.
The funding disclosure follows a media report published by Indian daily newspaper The Economic Times, which claimed that Simplismart had previously been in discussions to raise between $40 million and $50 million at a vastly higher valuation ranging from $300 million to $400 million. Asked to address the report, Jain declined to comment on the figures, stating that the startup was not actively fundraising at the moment and noting that the current Series B round is formally closed and will not be extended.
