As the global artificial intelligence boom strains computing capacity and drives up infrastructure expenses, semiconductor giant Nvidia has thrown its support behind Bengaluru-based startup Simplismart. The company has secured $14 million in a new funding round, betting heavily on specialized software that helps enterprises run machine learning models more efficiently and, in crucial use cases, with fewer graphics processing units.
Nvidia participated in the all-primary Series B funding round alongside returning and new institutional backers, including Accel, Shastra VC, and Micromax, with Dallas Venture Capital leading the investment. The financial transaction values the four-year-old startup at approximately $100 million, a figure that factors in the newly injected capital.
Modern enterprises are allocating substantial budgets toward acquiring and deploying GPUs to power sophisticated AI applications. However, scaling those models so they run reliably, quickly, and economically remains a formidable operational challenge. Different enterprise workloads demand distinct trade-offs between processing speed, operational cost, and overall output performance. Deploying and operating AI models to seamlessly meet unique business requirements frequently demands complex infrastructural adjustments and continuous monitoring.
Simplismart argues that its proprietary software automates a significant portion of that heavy lifting. Instead of forcing customers to manually handle complex configurations and fine-tuning, the startup’s platform intelligently tailors how models run on existing computing infrastructure.
"Nobody actually needs the fastest or the cheapest inference," explained Amritanshu Jain, co-founder and chief executive officer of Simplismart, during a recent interview. He emphasized that what companies actually require is the ability to run AI models at speeds and cost thresholds that make strategic and financial sense for their specific commercial applications.
Simplismart’s software architecture positions itself squarely between foundational AI models and the underlying computing infrastructure. It dynamically adjusts how models are deployed and managed to adhere strictly to customer requirements. The platform optimizes several critical metrics, including how quickly a model responds to inputs, the volume of simultaneous requests it can successfully process, and the total amount of computing power it consumes. By doing so, it helps organizations extract significantly more value out of their existing GPU hardware stack.
Operating out of Bengaluru, the startup offers businesses a versatile deployment model. Companies can choose to run AI models either through Simplismart’s managed platform or directly within their own existing cloud and on-premises infrastructure. Additionally, the startup provides a robust suite of tools that allow organizations to fine-tune models, automatically scale computing resources up or down as live demand fluctuates, and monitor real-time performance. Furthermore, Simplismart enables regional cloud providers to build and operate their own dedicated AI inference services directly on top of GPU infrastructure utilizing the startup’s underlying technology.
The real-world efficacy of the platform has already attracted a growing roster of enterprise clients. Simplismart states that its software successfully helped AI video-generation platform InVideo slash its GPU serving costs by 56 percent. The startup also maintains active commercial relationships with prominent industry players such as AI video companies HeyGen and Higgsfield, alongside enterprise AI startup Ema, among others. Moreover, Indian cloud infrastructure provider Yotta leverages Simplismart’s technology to power the serverless AI inference service underpinning its Shakti Studio platform.
This commercial momentum has translated into notable financial growth. Simplismart’s annualized revenue run rate has surged 14-fold over the past six months, scaling up to a robust eight-figure sum, Jain shared. This metric is calculated based on recent customer usage data rather than traditional contracted recurring revenue, and it specifically excludes revenue passed through directly to underlying infrastructure providers, according to the company. Jain declined to disclose the exact financial figure. Interestingly, despite maintaining a larger customer base domestically 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 technical integrations. These joint engineering efforts center around Nvidia’s Dynamo inference software, according to Jain. The technical partnership involves close coordination between Simplismart’s engineering teams and Nvidia’s specialized solutions architects. Additionally, Nvidia has actively supported the startup by introducing its leadership team to prospective enterprise customers and strategic partners.
This strategic alignment presents an intriguing market paradox. Nvidia, which stands as the dominant global supplier of advanced AI chips, is actively investing in software that can potentially enable companies to execute certain workloads using fewer GPUs. Jain, however, counters that making AI inference significantly more efficient will ultimately benefit Nvidia in the long run. By lowering the operational barriers and financial costs of running models, efficiency tools make hardware investments more attractive to risk-averse corporate buyers, thereby encouraging wider, more sustainable industry adoption of AI applications.
Simplismart is navigating an increasingly crowded and competitive market for AI inference software. The startup competes directly with native offerings from Nvidia itself, as well as a formidable array of major cloud computing providers and hyperscalers, including Amazon Web Services, Google Cloud, and Microsoft Azure. At the same time, a growing ecosystem of venture-backed startups is racing to develop software tools designed to make AI models faster and cheaper to run, fueled by relentless market demand and the imperative to optimize expensive, scarce GPU resources.
"Where we tend to win is where people have to be more stringent on their SLAs and TCO," Jain noted, using industry acronyms for service-level agreements and total cost of ownership. He pointed out demanding performance requirements and tight cost constraints as the company’s primary differentiators. Unlike competitors that typically provide rigid, preset options geared exclusively toward either faster speeds or lower costs, Simplismart focuses on engineering a customized performance balance tailored specifically to each client’s operational needs.
Simplismart was founded in 2022 by Jain, a former Oracle engineer, and Devansh Ghatak, a former Google engineer. The co-founders met while undergraduate students at BITS Pilani, drawing inspiration for the enterprise from their shared professional experiences building and deploying complex machine-learning systems. Today, the startup has expanded its workforce to 58 employees.
Looking ahead, Simplismart plans to allocate its newly acquired capital toward accelerating its commercial expansion within the United States, scaling its engineering and sales teams, and driving further research and development into its core inference software. Jain affirmed that the U.S. market remains a primary strategic frontier as the company looks to build extensively upon its existing customer base there.
The funding disclosure follows a recent report by Indian financial daily The Economic Times, which claimed that Simplismart was actively engaged in discussions to raise between $40 million and $50 million at a valuation ranging from $300 million to $400 million. When asked about the report, Jain declined to comment on the figures, maintaining that the startup was not actively fundraising at the moment. He clarified that the Series B funding round has officially closed and will not be extended.
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