TensorZero is an open-source infrastructure stack designed for building industrial-grade LLM applications. At its core, it offers a unified API that connects to every major model provider with sub-millisecond latency, allowing teams to standardize their workflows without being locked into a single vendor. Beyond simple access, TensorZero provides the observability layer that most AI teams are missing—capturing inferences, metrics, and human feedback in a way that can be analyzed programmatically or through a clean interface.
Where TensorZero really shines is in optimization and evaluation. By turning production metrics and real user feedback into structured signals, teams can continuously refine prompts, models, and inference strategies. Built-in tools for benchmarking, LLM-judged evaluations, and automated A/B testing let companies experiment with confidence, making it easier to ship better-performing applications at lower cost. Unlike ad-hoc setups, TensorZero creates a disciplined feedback loop where every deployment makes the system smarter, faster, and more efficient.
What sets TensorZero apart is its commitment to performance and openness. Written in Rust, it delivers low latency and high throughput while supporting type safety and GitOps workflows. It unifies the entire LLMOps stack—gateway, observability, optimization, evaluation, and experimentation—into a single framework that compounds in value over time. Fully self-hosted under Apache 2.0, TensorZero is free to use, giving developers an enterprise-grade foundation for scaling AI without the lock-in or overhead of closed platforms.
