Enterprises have widely adopted large language models, but teams building LLM applications lack integrated tools for complex cognitive and infrastructure needs and often stitch together immature solutions. AI models are growing smarter and execute more complex workflows, requiring engineers to reason about end-to-end systems and real-world consequences rather than evaluating isolated inferences. TensorZero supplies an open-source, industrial-grade stack that combines an LLM gateway, observability, optimization, evaluation, and experimentation into a self-reinforcing loop. The platform optimizes applications using production metrics and human feedback while meeting enterprise requirements such as sub-millisecond latency, high throughput, and full self-hosting. TensorZero raised $7.3M in seed funding and reached the #1 trending GitHub repository.
Despite widespread adoption of large language models across enterprises, companies building LLM applications still lack the right tools to meet complex cognitive and infrastructure needs, often resorting to stitching together early-stage solutions available on the market. The challenge intensifies as AI models grow smarter and take on more complex workflows, requiring engineers to reason about end-to-end systems and their real-world consequences rather than judging business outcomes by examining individual inferences.
TensorZero addresses this gap with an open-source stack for industrial-grade LLM applications that unifies an LLM gateway, observability, optimization, evaluation, and experimentation in a self-reinforcing loop. The platform enables companies to optimize complex LLM applications based on production metrics and human feedback while supporting the demanding requirements of enterprise environments including sub-millisecond latency, high throughput, and complete self-hosting capabilities. The company hit the #1 trending repository spot globally on GitHub and already powers cutting-edge LLM products at frontier AI startups and large organizations,
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