OpenPipe

Automatically captures and logs LLM API requests and responses from production applications with minimal code changes. Integrates via simple webhook or SDK to collect real-world model interactions for analysis and optimization.

Analyzes captured LLM logs to identify high-quality training examples and automatically prepares datasets for model fine-tuning. Filters and organizes request-response pairs into curated training sets without manual data cleaning.

Provides SDKs and webhooks that integrate with existing LLM applications with minimal code changes. Supports multiple LLM providers and frameworks.

Tracks versions of training datasets used for fine-tuning, maintains lineage from production logs to trained models, and enables rollback to previous dataset versions.

Collects user feedback on model outputs and incorporates quality signals into future fine-tuning iterations. Enables continuous improvement based on real-world usage feedback.

Executes end-to-end fine-tuning of language models on curated datasets with a single action, abstracting away ML infrastructure complexity. Handles hyperparameter selection, training, and validation automatically.

Compares fine-tuned models against baseline models and original API calls using production data. Measures quality metrics, latency, and cost differences to quantify optimization gains.

Manages deployment of fine-tuned models to production and handles switching between baseline and optimized models. Provides rollback capabilities and gradual traffic shifting for safe model updates.

Continuously tracks fine-tuned model performance in production against defined quality thresholds and cost targets. Alerts on performance degradation or unexpected behavior changes.

Automatically calculates inference costs for baseline models versus fine-tuned alternatives and projects ROI from optimization. Provides detailed cost breakdowns and savings estimates.

Enables rapid experimentation cycles where developers can collect new logs, retrain models, benchmark improvements, and deploy updates in sequence. Supports A/B testing between model versions.

Allows side-by-side evaluation of multiple fine-tuned models and baseline options across quality, cost, and latency dimensions. Facilitates data-driven model selection decisions.

Analyzes logged prompts to identify patterns and suggests optimizations. Tests prompt variations against models to find highest-performing versions before fine-tuning.