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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.
“That problem leads to a huge amount of concern about where to focus our limited resources,” Mehta said. “We were going to only get to run a handful of trials total, so the question became: what is the marginally most valuable place we can collect data from?” This experience shaped TensorZero’s core philosophy: maximizing the value of every data point to continuously improve AI systems.
The insight led Mehta and co-founder Gabriel Bianconi, former chief product officer at Ondo Finance (a decentralized finance project with over $1 billion in assets under management), to reconceptualize LLM applications as reinforcement learning problems where systems learn from real-world feedback.
“LLM applications in their broader context feel like reinforcement learning problems,” Mehta explained. “You make many calls to a machine learning model with structured inputs, get structured outputs, and eventually receive some form of reward or feedback. This looks to me like a partially observable Markov decision process.”
Why enterprises are ditching complex vendor integrations for unified AI infrastructure
Traditional approaches to building LLM applications require companies to integrate numerous specialized tools from different vendors — model gateways, observability platforms, evaluation frameworks, and fine-tuning services. TensorZero unifies these capabilities into a single open-source stack designed to work together seamlessly.
“Most companies didn’t go through the hassle of integrating all these different tools, and even the ones that did ended up with fragmented solutions, because those tools weren’t designed to work well with each other,” Bianconi said. “So we realized there was an opportunity to build a product that enables this feedback loop in production.”
The platform’s core innovation is creating what the founders call a “data and learning flywheel” — a feedback loop that turns production metrics and human feedback into smarter, faster, and cheaper models. Built in Rust for performance, TensorZero achieves sub-millisecond latency overhead while supporting all major LLM providers through a unified API.
Major banks and AI startups are already building production systems on TensorZero
The approach has already attracted significant enterprise adoption. One of Europe’s largest banks is using TensorZero to automate code changelog generation, while numerous AI-first startups from Series A to Series B stage have integrated the platform across diverse industries including healthcare, finance, and consumer applications.
“The surge in adoption from both the open-source community and enterprises has been incredible,” Bianconi said. “We’re fortunate to have received contributions from dozens of developers worldwide, and it’s exciting to see TensorZero already powering cutting-edge LLM applications at frontier AI startups and large organizations.”
The company’s customer base spans organizations from startups to major financial institutions, drawn by both the technical capabilities and the open-source nature of the platform. For enterprises with strict compliance requirements, the ability to run TensorZero within their own infrastructure provides crucial control over sensitive data.
How TensorZero outperforms LangChain and other AI frameworks at enterprise scale
TensorZero differentiates itself from existing solutions like LangChain and LiteLLM through its end-to-end approach and focus on production-grade deployments. While many frameworks excel at rapid prototyping, they often hit scalability ceilings that force companies to rebuild their infrastructure.
“There are two dimensions to think about,” Bianconi explained. “First, there are a number of projects out there that are very good to get started quickly, and you can put a prototype out there very quickly. But often companies will hit a ceiling with many of those products and need to churn and go for something else.”
The platform’s structured approach to data collection also enables more sophisticated optimization techniques. Unlike traditional observability tools that store raw text inputs and outputs, TensorZero maintains structured data about the variables that go into each inference, making it easier to retrain models and experiment with different approaches.
Rust-powered performance delivers sub-millisecond latency at 10,000+ queries per second
Performance has been a key design consideration. In benchmarks, TensorZero’s Rust-based gateway adds less than 1 millisecond of latency at 99th percentile while handling over 10,000 queries per second. This compares favorably to Python-based alternatives like LiteLLM, which can add 25-100x more latency at much lower throughput levels.
“LiteLLM (Python) at 100 QPS adds 25-100x+ more P99 latency than our gateway at 10,000 QPS,” the founders noted in their announcement, highlighting the performance advantages of their Rust implementation.
The open-source strategy designed to eliminate AI vendor lock-in fears
TensorZero has committed to keeping its core platform entirely open source, with no paid features — a strategy designed to build trust with enterprise customers wary of vendor lock-in. The company plans to monetize through a managed service that automates the more complex aspects of LLM optimization, such as GPU management for custom model training and proactive optimization recommendations.
“We realized very early on that we needed to make this open source, to give [enterprises] the confidence to do this,” Bianconi said. “In the future, at least a year from now realistically, we’ll come back with a complementary managed service.”
The managed service will focus on automating the computationally intensive aspects of LLM optimization while maintaining the open-source core. This includes handling GPU infrastructure for fine-tuning, running automated experiments, and providing proactive suggestions for improving model performance.
What’s next for the company reshaping enterprise AI infrastructure
The announcement positions TensorZero at the forefront of a growing movement to solve the “LLMOps” challenge — the operational complexity of running AI applications in production. As enterprises increasingly view AI as critical business infrastructure rather than experimental technology, the demand for production-ready tooling continues to accelerate.
With the new funding, TensorZero plans to accelerate development of its open-source infrastructure while building out its team. The company is currently hiring in New York and welcomes open-source contributions from the developer community. The founders are particularly excited about developing research tools that will enable faster experimentation across different AI applications.
“Our ultimate vision is to enable a data and learning flywheel for optimizing LLM applications—a feedback loop that turns production metrics and human feedback into smarter, faster, and cheaper models and agents,” Mehta said. “As AI models grow smarter and take on more complex workflows, you can’t reason about them in a vacuum; you have to do so in the context of their real-world consequences.”
TensorZero’s rapid GitHub growth and early enterprise traction suggest strong product-market fit in addressing one of the most pressing challenges in modern AI development. The company’s open-source approach and focus on enterprise-grade performance could prove decisive advantages in a market where developer adoption often precedes enterprise sales.
For enterprises still struggling to move AI applications from prototype to production, TensorZero’s unified approach offers a compelling alternative to the current patchwork of specialized tools. As one industry observer noted, the difference between building AI demos and building AI businesses often comes down to infrastructure — and TensorZero is betting that unified, performance-oriented infrastructure will be the foundation upon which the next generation of AI companies is built.
