OpenPipe
Developer platform to capture production prompts and fine-tune small models
About OpenPipe
OpenPipe is a developer-centric model distillation and fine-tuning platform that enables engineering teams to replace expensive, slow frontier LLMs (like GPT-4.5 and Claude 3.7) with specialized, fine-tuned smaller models that run faster and cost up to 90% less. Rather than manually gathering synthetic training datasets, OpenPipe acts as a drop-in proxy wrapper around your existing OpenAI or Anthropic SDK calls. It automatically logs real-world production prompts and completions in the background. Once sufficient production data is collected, OpenPipe allows you to trigger automated fine-tuning runs on open-weights architectures (such as Llama 3 and Mistral) with one click, delivering equivalent task accuracy at sub-100ms inference latency.
OpenPipe provides seamless drop-in SDKs for Python and TypeScript that match the standard OpenAI client signature. Developers simply change their base URL or SDK import, enabling asynchronous logging with zero added runtime latency. The platform includes automated dataset curation filters, allowing engineers to prune low-quality completions, filter out duplicate inputs, and split data into training and validation sets. Once fine-tuning completes, OpenPipe deploys the fine-tuned model checkpoint on dedicated serverless GPU infrastructure with built-in fallback routing to frontier models if input edge cases occur.
Replace your OpenAI SDK import with OpenPipe's drop-in Python/TypeScript client.
Your production application continues sending prompts to GPT-4 while OpenPipe logs input/output pairs.
Curate your logged dataset and trigger a one-click fine-tuning run on Llama 3 or Mistral.
Evaluate the fine-tuned model against GPT-4 on test benchmarks inside the OpenPipe dashboard.
Switch your production endpoint to the fine-tuned model to reduce inference costs by 80-90%.
Capabilities & Features
Common Use Cases
model-fine-tuning
llm-cost-reduction
latency-optimization
prompt-logging
Frequently Asked Questions
How does OpenPipe reduce AI API costs?
OpenPipe distills complex tasks performed by expensive frontier models (GPT-4) into specialized smaller open-source models (Llama 3, Mistral) that achieve identical accuracy for 80-90% lower inference costs.
Does OpenPipe add latency to my live production application?
No, OpenPipe logs production prompts asynchronously in the background, adding zero latency to your live user requests.
Can I export my fine-tuned model weights from OpenPipe?
Yes, OpenPipe allows you to download your fine-tuned model weights (LoRA adapters) to host on your own private infrastructure or vLLM clusters.
Free Plan
Free tier allows capturing up to 10,000 production prompt logs per month and evaluating prompt datasets.
Paid Plan
Fine-tuning starts at $100 per trained model; inference hosted on dedicated endpoints at a fraction of GPT-4 costs.
Pros & Cons
Drop-in SDK compatibility with standard OpenAI API clients
Reduces production LLM inference costs by 80% to 90%
Sub-100ms inference latency compared to slow frontier models
Automated prompt logging, data deduplication, and evaluation benchmarks
Built-in fallback routing to frontier models for edge-case resilience
Requires several hundred production prompt logs for optimal fine-tuning accuracy
Tailored for specialized repetitive tasks rather than open-ended general chat
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View allOpenPipe
Developer platform to capture production prompts and fine-tune small models
OpenPipe is a developer-centric model distillation and fine-tuning platform that enables engineering teams to replace expensive, slow frontier LLMs (like GPT-4.5 and Claude 3.7) with specialized, fine-tuned smaller models that run faster and cost up to 90% less. Rather than manually gathering synthetic training datasets, OpenPipe acts as a drop-in proxy wrapper around your existing OpenAI or Anthropic SDK calls. It automatically logs real-world production prompts and completions in the background. Once sufficient production data is collected, OpenPipe allows you to trigger automated fine-tuning runs on open-weights architectures (such as Llama 3 and Mistral) with one click, delivering equivalent task accuracy at sub-100ms inference latency.
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