tensorzero open source analysis

TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation.

Project overview

⭐ 10767 · Rust · Last activity on GitHub: 2026-01-06

GitHub: https://github.com/tensorzero/tensorzero

Why it matters for engineering teams

TensorZero addresses the challenge of managing large language model (LLM) applications in production environments by providing a unified open source tool for engineering teams. It combines an LLM gateway with observability, optimisation, evaluation, and experimentation features, enabling machine learning and AI engineering teams to deploy and maintain models more efficiently. Its maturity and robust design make it a production ready solution suitable for industrial-grade applications. However, TensorZero may not be the best choice for teams seeking a fully managed cloud service or those with simpler LLM needs, as it requires a self hosted option and some operational overhead.

When to use this project

TensorZero is a strong choice when teams require full control over their LLM infrastructure and need comprehensive tools for optimisation and monitoring. Teams looking for a quick, managed deployment or minimal maintenance should consider alternative platforms.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from TensorZero, using it to build, test, and optimise large language models in production. It is commonly employed in products involving generative AI, deep learning, and custom LLM deployments where a self hosted option ensures data privacy and operational flexibility.

Best suited for

Topics and ecosystem

ai ai-engineering anthropic artificial-intelligence deep-learning genai generative-ai gpt large-language-models llama llm llmops llms machine-learning ml ml-engineering mlops openai python rust

Activity and freshness

Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.