About
TensorZero is a comprehensive open-source LLMOps platform designed to help engineering teams build, monitor, and continuously improve production-grade LLM applications. At its core is an LLM gateway that provides a unified API across every major provider with sub-millisecond p99 latency, making it easy to switch or combine models without rewriting application code. The platform bundles five tightly integrated capabilities: a gateway for routing and provider abstraction; observability tooling for programmatic and UI-based monitoring of inference pipelines; evaluation frameworks for benchmarking individual inferences or end-to-end workflows; optimization workflows covering prompt engineering, fine-tuning, reinforcement learning, and distillation; and built-in experimentation with A/B testing and automatic fallbacks. TensorZero Autopilot extends the stack with an autonomous AI engineering agent—analogous to Claude Code for LLM engineering—that scans millions of inference logs, surfaces error patterns, recommends models and inference strategies, generates and refines prompts, drives fine-tuning workflows, and closes the feedback loop through automated A/B tests. The platform is compatible with the OpenAI SDK and OpenTelemetry, enabling incremental adoption alongside existing tooling. It is built in Rust for performance and reliability, and is backed by FirstMark, Bessemer, and Bedrock. TensorZero is ideal for ML engineers, platform teams, and AI-forward companies that need deep control over LLM quality, cost, and latency at scale.
Key Features
- Unified LLM Gateway: Access every major LLM provider through a single API with under 1ms p99 latency, supporting fallbacks and provider switching without code changes.
- LLM Observability: Monitor inference pipelines programmatically or via a built-in UI, capturing structured logs across millions of inferences for analysis and debugging.
- Evaluation Framework: Benchmark individual inferences or full end-to-end workflows, align LLM judges to real-world scenarios, and prevent regressions in production.
- Prompt & Model Optimization: Automate prompt generation and refinement using human feedback, metrics, and evals, and drive fine-tuning, reinforcement learning, and distillation workflows.
- Built-in A/B Experimentation: Deploy changes with native A/B testing to validate prompt and model updates, identify winners, and continuously close the feedback loop.
Use Cases
- Engineering teams routing production LLM traffic across multiple providers through a single low-latency gateway with automatic fallbacks.
- ML engineers monitoring millions of inferences to surface error patterns, cost anomalies, and quality regressions in real time.
- AI platform teams running structured A/B tests to validate prompt and model changes before full production rollout.
- Data scientists automating prompt optimization and fine-tuning workflows using inference logs, human feedback, and evaluation metrics.
- Enterprise AI teams standardizing LLM observability and evaluation across multiple internal applications with a self-hosted, open-source stack.
Pros
- Fully Open Source: The entire stack is open source (11.2K+ GitHub stars), giving teams full transparency, self-hosting capability, and no vendor lock-in.
- All-in-One LLMOps Platform: Gateway, observability, evaluation, optimization, and experimentation are tightly integrated, eliminating the need to stitch together multiple tools.
- Incremental Adoption: Compatible with the OpenAI SDK and OpenTelemetry, so teams can adopt individual components gradually alongside their existing infrastructure.
- High-Performance Rust Core: Built in Rust, the gateway delivers sub-millisecond p99 latency, making it suitable for high-throughput, latency-sensitive production workloads.
Cons
- Requires Self-Hosting Expertise: As a self-hosted open-source platform, teams need DevOps resources to deploy, maintain, and scale the infrastructure effectively.
- Steep Learning Curve: The breadth of the platform—gateway, evals, optimization, experimentation—means a significant upfront investment in understanding and configuring the system.
- Autopilot is an Add-On: TensorZero Autopilot (the automated AI engineer layer) requires a demo/sales engagement and is not a simple self-serve feature of the open-source stack.
Frequently Asked Questions
What is TensorZero?
TensorZero is an open-source LLMOps platform that combines an LLM gateway, observability, evaluation, optimization, and experimentation into one unified stack for building production-grade LLM applications.
What is TensorZero Autopilot?
TensorZero Autopilot is an automated AI engineering agent that analyzes LLM observability data, sets up evaluations, optimizes prompts and models, runs A/B tests, and drives fine-tuning workflows—similar to Claude Code but for LLM engineering.
Is TensorZero free to use?
Yes, the core TensorZero stack is fully open source and free to self-host. Enterprise support and Autopilot features may require contacting the team directly.
Which LLM providers does TensorZero support?
TensorZero supports every major LLM provider through its unified gateway API. It is also compatible with the OpenAI SDK, so existing OpenAI-based applications can integrate with minimal changes.
How does TensorZero handle latency?
The LLM gateway is built in Rust and delivers under 1ms p99 overhead latency, making it suitable for high-throughput, latency-sensitive production environments.
