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.
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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 Overview
Key Differences
TensorZero
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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 Quickstart -
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, MIPRO) 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.
Is TensorZero missing any features that are really important to you? Let us know on GitHub Discussions, Slack, or Discord.