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TensorZero TensorZero is an open-source LLMOps platform that unifies: Gateway: access every LLM provider through a unified API, built for performance (<1ms p99 latency) Observability: store inferences and feedback in your database, available programmatically or in the UI Evaluation: benchmark individual inferences or end-to-end workflows using heuristics, LLM judges, etc. Optimization: collect metrics and human feedback to optimize prompts, models, and inference strategies Experimentation: ship with confidence with built-in A/B testing, routing, fallbacks, retries, etc. You can take what you need, adopt incrementally, and complement with other tools. It plays nicely with the OpenAI SDK , OpenTelemetry , and every major LLM provider . TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today. Website · Docs · Twitter · Slack · Discord Quick Start (5min) · Deployment Guide · API Reference · Configuration Reference Demo tensorzero-demo.mp4 Features Note 🆕 TensorZero Autopilot TensorZero Autopilot is an automated AI engineer powered by TensorZero that analyzes LLM observability data, sets up evals, optimizes prompts and models, and runs A/B tests. It dramatically improves the performance of LLM agents across diverse tasks: Learn more → 🌐 LLM Gateway Integrate with TensorZero once and access every major LLM provider. Call any LLM (API or self-hosted) through a single unified API Infer with tool use , structured outputs (JSON) , batch , embeddings , multimodal (images, files) , caching , etc. Create prompt templates and schemas to enforce a structured interface between your application and the LLMs Satisfy extreme throughput and latency needs, thanks to 🦀 Rust: <1ms p99 latency overhead at 10k+ QPS Ensure high availability with routing, retries, fallbacks, load balancing, granular timeouts, etc. Track usage and cost and enforce custom rate limits with granular scopes (e.g. tags) Set up auth for TensorZero to allow clients to access models without sharing provider API keys Supported Model Providers Anthropic , AWS Bedrock , AWS SageMaker , Azure , DeepSeek , Fireworks , GCP Vertex AI Anthropic , GCP Vertex AI Gemini , Google AI Studio (Gemini API) , Groq , Hyperbolic , Mistral , OpenAI , OpenRouter , SGLang , TGI , Together AI , vLLM , and xAI (Grok) . Need something else? TensorZero also supports any OpenAI-compatible API (e.g. Ollama) . Usage Example You can use TensorZero with any OpenAI SDK (Python, Node, Go, etc.) or OpenAI-compatible client. Deploy the TensorZero Gateway (one Docker container). Update the base_url and model in your OpenAI-compatible client. Run inference: from openai import OpenAI # Point the client to the TensorZero Gateway client = OpenAI ( base_url = "http://localhost:3000/openai/v1" , api_key = "not-used" ) response = client . chat . completions . create ( # Call any model provider (or TensorZero function) model = "tensorzero::model_name::anthropic::claude-sonnet-4-6" , messages = [ { "role" : "user" , "content" : "Share a fun fact about TensorZero." , } ], ) See Quick Start for more information. 🔍 LLM Observability Zoom in to debug individual API calls, or zoom out to monitor metrics across models and prompts over time — all using the open-source TensorZero UI. Store inferences and feedback (metrics, human edits, etc.) in your own database Dive into individual inferences or high-level aggregate patterns using the TensorZero UI or programmatically Build datasets for optimization, evaluation, and other workflows Replay historical inferences with new prompts, models, inference strategies, etc. Export OpenTelemetry traces (OTLP) and export Prometheus metrics to your favorite application observability tools Soon: AI-assisted debugging and root cause analysis; AI-assisted data labeling 📈 LLM Optimization Send production metrics and human feedback to easily optimize your prompts, models, and inference strategies — using the UI or programmatically. Optimize your models with supervised fine-tuning , RLHF, and other techniques Optimize your prompts with automated prompt engineering algorithms like GEPA Optimize your inference strategy with dynamic in-context learning , best/mixture-of-N sampling, etc. Enable a feedback loop for your LLMs: a data & learning flywheel turning production data into smarter, faster, and cheaper models Soon: synthetic data generation 📊 LLM Evaluation Compare prompts, models, and inference strategies using evaluations powered by heuristics and LLM judges. Evaluate individual inferences with inference evaluations powered by heuristics or LLM judges (≈ unit tests for LLMs) Evaluate end-to-end workflows with workflow evaluations with complete flexibility (≈ integration tests for LLMs) Optimize LLM judges just like any other TensorZero function to align them to human preferences Soon: more built-in evaluators; headless evaluations Evaluation » UI Evaluation » CLI docker compose run --rm evaluations \ --evaluation-name extract_data \ --dataset-name hard_test_cases \ --variant-name gpt_4o \ --concurrency 5 Run ID: 01961de9-c8a4-7c60-ab8d-15491a9708e4 Number of datapoints: 100 ██████████████████████████████████████ 100/100 exact_match: 0.83 ± 0.03 (n=100) semantic_match: 0.98 ± 0.01 (n=100) item_count: 7.15 ± 0.39 (n=100) 🧪 LLM Experimentation Ship with confidence with built-in A/B testing, routing, fallbacks, retries, etc. Run adaptive A/B tests to ship with confidence and identify the best prompts and models for your use cases. Enforce principled experiments in complex workflows, including support for multi-turn LLM systems, sequential testing, and more. & more! Build with an open-source stack well-suited for prototypes but designed from the ground up to support the most complex LLM applications and deployments. Build simple applications or massive deployments with GitOps-friendly orchestration Extend TensorZero with built-in escape hatches, programmatic-first usage, direct database access, and more Integrate with third-party tools: specialized observability and evaluations, model providers, agent orchestration frameworks, etc. Iterate quickly by experimenting with prompts interactively using the Playground UI Frequently Asked Questions How is TensorZero different from other LLM frameworks? TensorZero enables you to optimize complex LLM applications based on production metrics and human feedback. TensorZero supports the needs of industrial-grade LLM applications: low latency, high throughput, type safety, self-hosted, GitOps, customizability, etc. TensorZero unifies the entire LLMOps stack, creating compounding benefits. For example, LLM evaluations can be used for fine-tuning models alongside AI judges. Can I use TensorZero with ___? Yes. Every major programming language is supported. It plays nicely with the OpenAI SDK , OpenTelemetry , and every major LLM provider . Is TensorZero production-ready? Yes. TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and powers ~1% of the global LLM API spend today. Here's a case study: Automating Code Changelogs at a Large Bank with LLMs How much does TensorZero cost? TensorZero (LLMOps platform) is 100% self-hosted and open-source. TensorZero Autopilot (automated AI engineer) is a complementary paid product powered by TensorZero. Who is building TensorZero? Our technical team includes a former Rust compiler maintainer, machine learning researchers (Stanford, CMU, Oxford, Columbia) with thousands of citations, and the chief product officer of a decacorn startup. We're backed by the same investors as leading open-source projects (e.g. ClickHouse, CockroachDB) and AI labs (e.g. OpenAI, Anthropic). See our $7.3M seed round announcement and coverage from VentureBeat . We're hiring in NYC . How do I get started? You can adopt TensorZero incrementally. Our Quick Start goes from a vanilla OpenAI wrapper to a production-ready LLM application with observability and fine-tuning i