TensorZero
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TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.
TensorZero is an open-source platform designed to optimize the performance of Large Language Model (LLM) applications by creating a continuous feedback loop. It unifies data, learning, and optimization into a single, streamlined system, allowing you to transform real-world production data into models that are smarter, faster, and more cost-effective. Its key features include built-in observability, inference-time optimizations, A/B testing capabilities, support for multi-step workflows, and structured data extraction, among many others.
TensorZero is geared towards engineers and developers looking to build, manage, and optimize next-generation AI applications that learn from real-world experience. The platform provides a framework for easily setting up LLM applications, from simple chatbots to complex RAG systems and data extraction pipelines. By enabling experimentation with various configurations such as prompts, models, and parameters, TensorZero empowers teams to build and continuously improve AI systems with robust optimization and observability. It supports various models, including proprietary and self-hosted options, offering great flexibility.
