Skip to content

OpenPipe

What it is

OpenPipe is a data-driven fine-tuning platform that allows developers to replace generic, expensive LLMs (like GPT-4) with smaller, faster, and cheaper specialized models. It works by capturing requests and completions from existing models and using them to train custom models.

What problem it solves

It lowers the cost and latency of LLM applications without sacrificing quality by automating the process of distillation and fine-tuning. It simplifies the pipeline from data collection to model deployment.

Where it fits in the stack

Infrastructure / Fine-tuning

Typical use cases

  • Distilling GPT-4 level performance into a specialized Mistral or Llama-based model.
  • Reducing costs for high-volume LLM tasks like classification or extraction.
  • Improving latency for real-time applications by using smaller models.
  • "Golden Dataset" generation from production traffic.

Technical Capabilities

  • Drop-in SDK: Wraps the official OpenAI SDK, logging requests to OpenPipe with zero code changes to core logic.
  • Model Distillation: Tools to compare "Teacher" (e.g., GPT-4o) vs. "Student" (e.g., Llama-3-8B) performance.
  • Data Pruning: Automatically removes duplicate system prompts and redundant context to minimize training tokens.
  • OpenAI-Compatible Hosting: Deploys fine-tuned models to an endpoint that responds to standard OpenAI API calls.

Strengths

  • Easy "drop-in" replacement for OpenAI's SDK.
  • Automated data collection and curation for fine-tuning.
  • Integrated evaluation to compare fine-tuned models against base models.
  • Support for multiple base models (Mistral, Llama 3, etc.).

Limitations

  • Requires an initial "teacher" model to generate data.
  • Performance depends on the quality and variety of captured data.
  • Primarily focused on specialized tasks rather than general-purpose chat.

When to use it

  • When you have a stable production task and want to reduce costs or latency.
  • When you want to own your weights but start with OpenAI-grade performance.

When not to use it

  • For highly exploratory tasks where the prompt is changing frequently.
  • If you don't have enough volume to justify the fine-tuning effort or cost.

Getting started

Installation

pip install openpipe

Minimal Example

from openpipe import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Count to three"}],
    openpipe={"tags": {"purpose": "testing"}}
)

print(completion.choices[0].message.content)

CLI examples

# Log in to your OpenPipe account
openpipe login --api-key your_api_key_here

# Record a single request/response pair manually
openpipe record --prompt "Hello" --completion "Hi there!"

# List your fine-tuned models
openpipe models list

API: Production Data Collection

The primary value of OpenPipe is capturing real-world "Teacher" model outputs to build a training set.

import os
from openpipe import OpenAI

# OpenPipe is a drop-in replacement for the OpenAI SDK
client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    openpipe={"api_key": os.environ.get("OPENPIPE_API_KEY")}
)

# Requests are automatically logged for fine-tuning
# You can tag requests to filter your dataset later in the dashboard
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Extract technical specs from this manual..."}],
    openpipe={
        "tags": {
            "pipeline": "manual-ingestion",
            "version": "v2.1",
            "is_production": "true"
        },
        "log_request": True # Explicitly ensure this request is logged
    }
)

API: Switching to Fine-Tuned Model

Once a model is trained, switching is as simple as changing the model string.

# Switch from GPT-4o to your fine-tuned model
# The interface remains identical
response = client.chat.completions.create(
    model="openpipe:my-fine-tuned-llama-model",
    messages=[{"role": "user", "content": "Extract technical specs..."}],
    # Tagging still works for monitoring the performance of the student model
    openpipe={"tags": {"model_type": "student"}}
)

Licensing and cost

  • Open Source: Yes (Client SDK and some components)
  • Cost: Paid (Usage-based pricing for training and hosting)
  • Self-hostable: Partial (SDK is open, training platform is managed)

Sources / References

Contribution Metadata

  • Last reviewed: 2026-06-02
  • Confidence: high