TensorZero
Builds open-source tools for production-grade LLM applications
What is TensorZero?
TensorZero is an AI Observability tool that builds open-source tools for production-grade LLM applications. It is aimed at developers and data scientists.
TensorZero provides tools for LLM gateway, observability, optimization, evaluations, and experimentation. It is designed for developers creating production-grade applications with large language models.
Built for developers and data scientists.
Features
LLM Gateway
Provides a unified interface for interacting with large language models
Observability Tools
Optimization Framework
Evaluation System
Experimentation Platform
Use cases
- Develop and deploy production-grade LLM applications
- Monitor and analyze LLM performance
- Optimize LLM workflows and processes
Who it's for
Pros & cons
Pros
- Open-source framework
- Supports production-grade LLM applications
Cons
No documented limitations yet.
Developer & API
Open source· Apache-2.0· 11.7K stars· 970 forks
FAQ
What is TensorZero?
TensorZero is an AI Observability tool that builds open-source tools for production-grade LLM applications. TensorZero provides tools for LLM gateway, observability, optimization, evaluations, and experimentation. It is designed for developers creating production-grade applications with large language models.
Who should use TensorZero?
TensorZero is built for developers and data scientists. Common uses: develop and deploy production-grade LLM applications, monitor and analyze LLM performance and optimize LLM workflows and processes.
Is TensorZero open source?
Yes, TensorZero is open source under the Apache-2.0 license.
What are the best alternatives to TensorZero?
Similar tools on AiByts include Braintrust, Opik, AIWatch, Fireworks AI and Respan.
TensorZero news & updates
Even (very) noisy LLM evaluators are useful for improving AI agents
Noisy evaluators have limited value for production decisions that hinge on judging a single output (e.g. guardrails).
TensorZero Blog14 min readStop comparing price per million tokens: the hidden LLM API costs
Most engineers know they need to evaluate models on their specific task because performance varies. But so does cost: the same input can cost several times more on one provider than another, even when their list prices look similar.
TensorZero Blog3 min readWe're building an automated AI engineer, and it works
We’re building an automated AI engineer that dramatically improves performance of LLM agents on every single benchmark we’ve tried.
TensorZero Blog11 min readBandits in your LLM Gateway: Improve LLM Applications Faster with Adaptive Experimentation (A/B Testing)
Developing LLM applications involves numerous design decisions: which model to use, how to structure prompts, what temperature and sampling parameters to set, etc.
TensorZero Blog19 min readIs OpenAI's Reinforcement Fine-Tuning (RFT) Worth It?
We benchmarked OpenAI’s supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) workflows on three tasks:Data Extraction: RFT improved performance with limited data, though SFT on a larger dataset achieved better results at a still much lower cost.Agentic Coding: RFT improved performance…
TensorZero Blog54 min readTensorZero Raises $7.3M Seed Round to Build an Open-Source Stack for Industrial-Grade LLM Applications
We’re excited to announce a $7.3M seed round to support the development of 11.5KTensorZero, an open-source stack for industrial-grade LLM applications.
TensorZero Blog4 min readDistillation with Programmatic Data Curation: Smarter LLMs, 5-30x Cheaper Inference
Every LLM application builder faces a fundamental tradeoff: large models deliver superior performance at higher costs, while small models are affordable but often underperform. This choice becomes critical when scaling to production workloads where inference costs can quickly explode.
TensorZero Blog38 min readAutomatically Evaluating AI Coding Assistants with Each Git Commit
AI coding assistants are performing an ever-increasing share of software engineering work. Generic LLM coding benchmarks fail to capture the effectiveness of a coding assistant for specific use cases and workflows.
TensorZero Blog11 min readReverse Engineering Cursor's LLM Client
We wired 11.5KTensorZero between Cursor and the LLMs to see every token fly by… and bend those API calls to our own will.
TensorZero Blog10 min readFrom NER to Agents: Does Automated Prompt Engineering Scale to Complex Tasks?
Crafting good system prompts is one of the trickiest parts of working with language models — especially when you’re dealing with complex, multi-step tasks. Doing it manually doesn’t scale, and the results are often hit-or-miss.
TensorZero Blog125 min readCase Study: Automating Code Changelogs at a Large Bank with LLMs
One of the largest European banks used 11.5KTensorZero to build an LLM-powered changelog generator for GitLab merge requests. They optimized model quality, deployed fully on-premise, and iterated quickly — without needing a team of ML engineers.
TensorZero Blog3 min readThink of LLM Applications as POMDPs — Not Agents
LLM engineering is a young field. Practitioners suffer from a lack of clarity about their problem space, resulting in tools and applications that are hard to reason about, manage, and iterate on — slop.
TensorZero Blog13 min read
Details are compiled from TensorZero's website and public sources, last verified Oct 3, 2026. Features and pricing change — confirm on the official site before you buy.
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