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TensorZero, a startup building open-source infrastructure for large language model applications, announced Monday it has raised $7.3 million in seed funding led by FirstMark, with participation from Bessemer Venture Partners, Bedrock, DRW, Coalition, and dozens of strategic angel investors.
The funding comes as the 18-month-old company experiences explosive growth in the developer community. TensorZero’s open-source repository recently achieved the “#1 trending repository of the week” spot globally on GitHub, jumping from roughly 3,000 to over 9,700 stars in recent months as enterprises grapple with the complexity of building production-ready AI applications.
“Despite all the noise in the industry, companies building LLM applications still lack the right tools to meet complex cognitive and infrastructure needs, and resort to stitching together whatever early solutions are available on the market,” said Matt Turck, General Partner at FirstMark, who led the investment. “TensorZero provides production-grade, enterprise-ready components for building LLM applications that natively work together in a self-reinforcing loop, out of the box.”
The Brooklyn-based company addresses a growing pain point for enterprises deploying AI applications at scale. While large language models like GPT-5 and Claude have demonstrated remarkable capabilities, translating these into reliable business applications requires orchestrating multiple complex systems for model access, monitoring, optimization, and experimentation.
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How nuclear fusion research shaped a breakthrough AI optimization platform
TensorZero’s approach stems from co-founder and CTO Viraj Mehta’s unconventional background in reinforcement learning for nuclear fusion reactors. During his PhD at Carnegie Mellon, Mehta worked on Department of Energy research projects where data collection cost “like a car per data point — $30,000 for 5 seconds of data,” he explained in a recent interview with VentureBeat.
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