MLflow Prompt Optimization vs Promptic

MLflow Prompt Optimization is an open-source, automated service for improving prompts through evaluation and iteration. It evaluates prompts against supplied data, identifies failure patterns, and generates revised variants. The `mlflow.genai.optimize_prompts` API provides a shared interface for optimization methods including GEPA and Metaprompting. You can provide training data, scorers, custom scorers, and aggregation functions, then save resulting prompts as new Prompt Registry versions. Runs, metrics, and traces support comparison and rollback. The workflow integrates with LangChain, LangGraph, OpenAI Agent, Pydantic AI, CrewAI, AutoGen, and custom frameworks, and works with any LLM provider. It supports API, self-hosted, and web access, with prompt variables, testing, and multiple-model support. GEPA is recommended for clear metrics and quality-critical work; documentation suggests 50–100 labeled examples, or at least 100 records for GEPA. The free Apache 2.0 option can be self-hosted or managed through cloud providers.

Promptic has no write-up on Specifiction yet; its figures are above.