TensorZero

We’re building TensorZero Autopilot, an automated AI engineer that analyzes LLM observability data, optimizes prompts and models, sets up evals, and runs A/B tests. It dramatically improves the performance of LLM agents on every single benchmark we’ve tried. Read more below.

Just wanted to give a huge shoutout to @TensorZero! you guys have really done an amazing job. De facto recommendation on every client project we work on right now.

it was a full house last night! (pun intended) inaugural @TensorZero ai poker night co-hosted with @BessemerVP

You might be overpaying 5.3x+ for Claude Opus 4.7! Our CEO @gabrielbianconi found out that on tool-heavy workloads, you're paying 5.3x more for Claude Opus 4.7 than GPT 5.4. The common metric is to compare cost per million tokens. But different providers use different tokenizers, and we found the same input produces wildly different token counts. Read our blog below to find out how you are being charged and how to actually know what you're paying for. 👇

LLM evaluators are often noisy and weakly correlated with real-world outcomes. Noisy evaluators have limited value for production decisions that hinge on judging a single output (e.g. guardrails). However, even (very) noisy evaluators can reliably tell you which agent is better on average, meaning they can still help you pick the best variant to deploy and improve it over time. Learn how ↓

Michelle Hui is joining us with a focus on developer relations. She recently graduated from Cornell with BS & MS degrees in computer science, during which she organized large tech events, conducted ML research, and held product internships (Alphabet / Wing, UN). Welcome to the team, @michellehui!

"If your security relies on your code being obfuscated, you're telling yourself a fake story." Our CTO Viraj Mehta (@thebigmehtaphor) chats about AI scaling, open source, and being a technical founder following his PhD in Reinforcement Learning at CMU. In our in-depth conversation, we discuss: 🔸What to look for in engineering hires when coding is largely solved 🔸How AI has increased the hiring bar and what you can do to meet it 🔸Why you should build in Rust to make your code more secure and error-free 🔸Why open source is still an important business model 🔸 Why AI is still scaling... and there's continued "juice to squeeze" in AI Full conversation in the thread ↓

TensorZero is committed to open source. We sit down with our CTO Viraj @thebigmehtaphor. Takeaways: 1. Thinking closed-source code == security is a LIE. You still need to build from first principles and fundamentally secure code. 2. Open-source keeps more eyes on your code, ultimately finding more bugs and vulnerabilities. 3. Open-source code allows developers and their agents to understand your codebase and integrate with it better. Close-source hamstrings users to look only at documentation. @michellehui

Learn more: tensorzero.com/blog/automated…

Can an automated AI engineer autonomously debug and optimize an LLM pipeline in 5 minutes? Last night, ours did: it cut errors in ~half during its first live demo. TensorZero Autopilot (our automated AI engineer) analyzed hundreds of historical LLM traces to identify failure modes, tuned the prompt, and verified improvements with an LLM judge — autonomously, in <5 minutes. With more time, it can do much more: from model selection to fine-tuning to adaptive experimentation, TensorZero Autopilot dramatically improves the performance of LLM agents across diverse tasks. Learn more below ↓

TensorZero Autopilot is powered by our open-source LLMOps platform that unifies an LLM gateway, observability, optimization, evaluation, and experimentation. The open-source project is used by companies ranging from frontier AI startups to the Fortune 10 and powers ~1% of the global LLM API spend today. github.com/tensorzero/ten…

Read more about this work on the TensorZero Blog: tensorzero.com/blog/automated…

We’re building TensorZero Autopilot, an automated AI engineer that analyzes LLM observability data, optimizes prompts and models, sets up evals, and runs A/B tests. It dramatically improves the performance of LLM agents on every single benchmark we’ve tried. Read more below.