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Comparison: TensorZero vs. OpenPipe

TensorZero and OpenPipe both provide tools that streamline fine-tuning workflows for LLMs. TensorZero is open-source and self-hosted, while OpenPipe is a paid managed service (inference costs ~2x more than specialized providers supported by TensorZero). That said, you can get the best of both worlds by using OpenPipe as a model provider inside TensorZero.

Similarities

  • LLM Optimization (Fine-Tuning). Both TensorZero and OpenPipe focus on LLM optimization (e.g. fine-tuning, DPO). OpenPipe focuses on fine-tuning, while TensorZero provides a complete set of tools for optimizing LLM systems (including prompts, models, and inference strategies).
    → Optimization Recipes with TensorZero

  • Built-in Observability. Both TensorZero and OpenPipe offer built-in observability features. TensorZero stores inference data in your own database for full privacy and control, while OpenPipe stores it themselves in their own cloud.

Key Differences

TensorZero

  • Open Source & Self-Hosted. TensorZero is fully open source and self-hosted. Your data never leaves your infrastructure, and you don’t risk downtime by relying on external APIs. OpenPipe is a closed-source managed service.

  • No Added Cost (& Cheaper Inference Providers). TensorZero is free to use: your bring your own LLM API keys and there is no additional cost. OpenPipe charges ~2x on inference costs compared to specialized providers supported by TensorZero (e.g. Fireworks AI).

  • 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.
    OpenPipe supports a much smaller set of LLMs.
    → 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. OpenPipe doesn’t offer any inference-time optimizations.
    → Inference-Time Optimizations with TensorZero

  • Automatic Fallbacks for Higher Reliability. TensorZero is self-hosted and provides automatic fallbacks between model providers to increase reliability. OpenPipe can fallback their own models to other OpenAI-compatible APIs, but if OpenPipe itself goes down, you’re out of luck.
    → Retries & Fallbacks with TensorZero

  • Built-in Experimentation. TensorZero offers built-in experimentation features, allowing you to run experiments on your prompts, models, and inference strategies. OpenPipe doesn’t offer any experimentation features.

  • Batch Inference. TensorZero supports batch inference with certain model providers, which significantly reduces inference costs. OpenPipe doesn’t support batch inference.
    → Batch Inference with TensorZero

  • Schemas, Templates, GitOps. TensorZero enables a schema-first approach to building LLM applications, allowing you to separate your application logic from LLM implementation details. This approach allows your to more easily manage complex LLM applications, benefit from GitOps for prompt and configuration management, counterfactually improve data for optimization, and more. OpenPipe only offers the standard unstructured chat completion interface.
    → Prompt Templates & Schemas with TensorZero

OpenPipe

  • Evals. OpenPipe offers evals for your fine-tuned models. TensorZero doesn’t offer built-in evals (coming soon!), and instead requires you to manage them yourself.

  • Caching. OpenPipe offers caching for your fine-tuned models, which can improve latency and reduce costs. TensorZero doesn’t offer caching (coming soon!), and instead requires you to manage caching yourself.

  • Guardrails. OpenPipe offers guardrails (runtime AI judges) for your fine-tuned models. TensorZero doesn’t offer built-in guardrails, and instead requires you to manage them yourself.

Combining TensorZero and OpenPipe

You can get the best of both worlds by using OpenPipe as a model provider inside TensorZero.

OpenPipe provides an OpenAI-compatible API, so you can use models previously fine-tuned with OpenPipe with TensorZero. Learn more about using OpenAI-compatible endpoints.