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Comparison: TensorZero vs. Langfuse

TensorZero and Langfuse both provide open-source tools that streamline LLM engineering workflows. TensorZero focuses on inference and optimization, while Langfuse specializes in powerful interfaces for observability and evals. That said, you can get the best of both worlds by using TensorZero alongside Langfuse.

Similarities

  • Open Source & Self-Hosted. Both TensorZero and Langfuse are open source and self-hosted. Your data never leaves your infrastructure, and you don’t risk downtime by relying on external APIs. TensorZero is fully open-source, whereas Langfuse gates some of its features behind a paid license.

  • Built-in Observability. Both TensorZero and Langfuse offer built-in observability features, collecting inference in your own database. Langfuse offers a broader set of advanced observability features, including application-level tracing. TensorZero focuses more on structured data collection for optimization, including downstream metrics and feedback.

  • Built-in Evaluations. Both TensorZero and Langfuse offer built-in evaluations features, enabling you to sanity check and benchmark the performance of your prompts, models, and more — using heuristics and LLM judges. TensorZero LLM judges are also TensorZero functions, which means you can optimize them using TensorZero’s optimization recipes. Langfuse offers a broader set of built-in heuristics and UI features for evaluations.
    → TensorZero Evaluations Tutorial

Key Differences

TensorZero

  • Unified Inference API. TensorZero offers a unified inference API that allows you to access LLMs from most major model providers with a single integration, with support for structured outputs, tool use, streaming, and more. Langfuse doesn’t provide a built-in LLM gateway.
    → TensorZero Gateway Quick Start

  • Built-in Inference-Time Optimizations. TensorZero offers built-in inference-time optimizations (e.g. dynamic in-context learning), allowing you to optimize your inference performance. Langfuse doesn’t offer any inference-time optimizations.
    → Inference-Time Optimizations with TensorZero

  • Optimization Recipes. TensorZero offers optimization recipes (e.g. supervised fine-tuning, RLHF, DSPy) that leverage your own data to improve your LLM’s performance. Langfuse doesn’t offer built-in features like this.
    → Optimization Recipes with TensorZero

  • Automatic Fallbacks for Higher Reliability. TensorZero offers automatic fallbacks to increase reliability. Langfuse doesn’t offer any such features.
    → Retries & Fallbacks with TensorZero

  • Built-in Experimentation (A/B Testing). TensorZero offers built-in experimentation features, allowing you to run experiments on your prompts, models, and inference strategies. Langfuse doesn’t offer any experimentation features.
    → Experimentation (A/B Testing) with TensorZero

Langfuse

  • Advanced Observability & Evaluations. While both TensorZero and Langfuse offer observability and evaluations features, Langfuse takes it further with advanced observability features. Additionally, Langfuse offers a prompt playground, which TensorZero doesn’t offer (coming soon!).

  • Access Controls. Langfuse offers access controls, which TensorZero doesn’t offer. That said, some of Langfuse’s access control features (e.g. SSO) are only available in their paid plans.

  • Managed Service. Langfuse offers a paid managed (hosted) service in addition to the open-source version. TensorZero is fully open-source and self-hosted.

Combining TensorZero and Langfuse

You can combine TensorZero and Langfuse to get the best of both worlds.

A leading voice agent startup uses TensorZero for inference and optimization, alongside Langfuse for more advanced observability and evals.