AI Startups & Funding

Simplismart raises $14 million to expand AI inference software

TechCrunchOct 27, 2026

Nvidia participated in Simplismart’s $14 million Series B, funding software that tunes AI model deployments to customer workloads and existing infrastructure.

Bengaluru-based Simplismart has raised $14 million in an all-primary Series B round that included Nvidia, the startup said on October 27. Dallas Venture Capital led the investment, with Accel, Shastra VC, Micromax, and Nvidia also participating. The round values the four-year-old company at about $100 million, including the new capital.

Simplismart sells software that sits between AI models and the computing systems running them. Its platform adjusts deployments to customer requirements, including response speed, request volume, and compute consumption. Customers can use it through Simplismart’s platform or within their own cloud and on-premises environments; the company also offers model fine-tuning, automatic resource scaling, and performance monitoring.

The startup says its tools helped AI video platform InVideo reduce GPU serving costs by 56%. Other customers named in the report include HeyGen, Higgsfield, and enterprise AI company Ema. Indian cloud provider Yotta also uses Simplismart technology for the serverless inference service in its Shakti Studio platform.

CEO and co-founder Amritanshu Jain told TechCrunch that annualized revenue run rate increased 14-fold over six months to an eight-figure amount. The figure reflects recent customer usage, not contracted recurring revenue, and excludes revenue passed through to infrastructure providers; Jain did not disclose an exact total. He said more than half of revenue comes from the United States, although Simplismart has more customers in India.

Nvidia has also worked with Simplismart on technical integrations, including those involving Nvidia’s Dynamo inference software, Jain said. The startup plans to use the funding to expand in the U.S., hire, and develop its software further. Its pitch is to help businesses tune the balance between cost, speed, and performance for particular applications, rather than simply maximize speed or minimize expense. It competes in a market that includes Nvidia’s own tools, major cloud providers, and other inference-focused startups.

Industry Impact

More focus on efficient AI deployment

Enterprise AI, cloud computing, and AI infrastructure

Overview

Simplismart’s $14 million Series B backs software that helps businesses balance AI response speed, request volume, and compute costs across cloud and on-premises infrastructure. For developers and ML engineers, the platform offers tools for deployment tuning, scaling, and performance monitoring. For business leaders, the reported 56% reduction in InVideo’s GPU serving costs illustrates a potential cost benefit, though results may vary by workload. The company’s U.S. expansion and reported revenue growth point to rising demand, while competition from cloud providers and Nvidia’s own tools remains significant.

Key Impact Points

The funding supports U.S. expansion, hiring, and further product development.

Simplismart’s reported customer example suggests inference optimization can reduce GPU serving expenses.

Nvidia’s investment and technical integrations connect the startup to a major AI infrastructure provider, while also highlighting competitive overlap.

Opportunities

Optimize AI inference for real workloads

Enterprise AI and AI infrastructure

The article points to a practical opportunity for businesses running AI applications: tune deployments to workload requirements and existing infrastructure instead of relying on one-size-fits-all settings. Simplismart offers deployment adjustments, fine-tuning, automatic resource scaling, and performance monitoring through its platform or within customer cloud and on-premises environments. Teams can evaluate these capabilities against their own cost, speed, and volume targets, using customer examples as a reason to test rather than as a guaranteed outcome. For vendors, the funding and reported revenue growth indicate commercial interest in inference optimization, but the article does not disclose pricing, margins, or contracted recurring revenue.

Workload-specific inference optimization
Problem: AI deployments must balance response speed, request volume, and compute consumption, and GPU serving can be costly.
Solution: Use software to tune model deployments to customer workloads and existing cloud or on-premises infrastructure.
Target Users: Businesses operating AI applications and the developers and ML engineers responsible for their deployments.
Business Value: Better alignment of performance and compute use; Simplismart says its tools helped InVideo reduce GPU serving costs by 56%.
Revenue Potential: Simplismart reports an eight-figure annualized revenue run rate, but exact revenue, pricing, and contracted recurring revenue were not disclosed.

Reference link

https://techcrunch.com/2026/10/27/nvidia-backs-indian-startup-helping-companies-run-ai-models-with-fewer-gpus

Image Source Link

https://techcrunch.com/wp-content/uploads/2026/01/GettyImages-2256813240.jpg

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