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TensorZero

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An open-source framework for building production-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluations, and experimentation. ![GitHub Repo stars](https://img.shields.io/github/stars/tensorzero/tensorzero?style=social)

FreeFree tier
Outputs: text
Type
Open Source
Visit Website GitHub (40 stars)

About TensorZero

TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation for building production-grade LLM applications. It provides a unified API to access every major LLM provider (Anthropic, AWS Bedrock, Azure, DeepSeek, Google AI Studio, Groq, etc.) with low latency (1ms p99 overhead at 10k+ QPS) and high availability features like routing, retries, fallbacks, and load balancing. The platform supports tool use, structured outputs (JSON), batch, embeddings, multimodal inputs (images, files), caching, and prompt templates. Observability stores inferences and feedback in the user's database, available via UI or programmatically. Evaluation includes heuristics and LLM judges for individual inferences or end-to-end workflows. Optimization collects metrics and human feedback to improve prompts, models, and inference strategies. Experimentation enables A/B testing, routing, fallbacks, and retries. TensorZero also offers TensorZero Autopilot, an automated AI engineer that analyzes observability data, sets up evals, optimizes prompts and models, and runs A/B tests. It is used by companies from frontier AI startups to Fortune 10 and fuels approximately 1% of global LLM API spend.

Key Features

Unified LLM Gateway with support for every major LLM provider
Observability storing inferences and feedback in your database
Evaluation using heuristics and LLM judges for individual or end-to-end workflows
Optimization of prompts, models, and inference strategies with metrics and human feedback
Experimentation with A/B testing, routing, fallbacks, and retries
TensorZero Autopilot for automated analysis, eval setup, and optimization
1ms p99 latency overhead at 10k+ QPS
High availability via routing, retries, fallbacks, load balancing, and granular timeouts
Usage and cost tracking with custom rate limits and tags
Auth for client access to models without sharing provider API keys

Pros & Cons

Pros
  • Open source and completely free to use
  • Extremely low latency overhead (1ms p99 at 10k+ QPS) thanks to Rust implementation
  • Unified API across all major LLM providers, reducing vendor lock-in
  • Comprehensive observability and evaluation capabilities built in
  • Supports advanced features like tool use, structured outputs, and multimodal inputs
  • Adopted by both startups and large enterprises (Fortune 10 users)
  • Active community on GitHub with 11.7k stars and 4,100+ commits
Cons
  • Requires self-hosting and infrastructure management (no managed cloud version mentioned)
  • Setting up and configuring all features may have a steep learning curve
  • Repository is currently archived and read-only (as per GitHub status) – future development uncertain

Best For

Building production-grade LLM applications with multi-provider integrationOptimizing prompts and models based on real usage data and human feedbackA/B testing different LLM inference strategies (models, prompts, settings)Monitoring and debugging LLM application performance with observabilityImplementing robust fallback and routing logic for high availabilityAutomating LLM evaluation and optimization with TensorZero Autopilot

FAQ

What is TensorZero?
TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation for building production-grade LLM applications.
Is TensorZero free to use?
Yes, TensorZero is open source and free to use. There is no paid tier; you self-host the platform.
What LLM providers does TensorZero support?
It supports Anthropic, AWS Bedrock, AWS SageMaker, Azure, DeepSeek, Fireworks, GCP Vertex AI, Google AI Studio, Groq, and others.
Does TensorZero support tool use and structured outputs?
Yes, TensorZero supports tool use, structured JSON outputs, batch processing, embeddings, multimodal inputs (images, files), and caching.
How does TensorZero help with LLM optimization?
It collects metrics and human feedback to optimize prompts, models, and inference strategies, and includes an automated AI engineer (TensorZero Autopilot) for analysis, eval setup, and A/B testing.