AI Model Match
You innovate. We orchestrate.
4 followers
You innovate. We orchestrate.
4 followers
Focus on new AI experiences while AI Model Match chooses the best prompt pipeline for you. It automates release and experimentation for AI Applications. You define the use case, we collect feedback, and automatically promote top-performing prompts for you.
Hey Product Hunt! 👋
We’re excited to launch AI Model Match, an open-source service designed to help teams test, optimize, and gradually release AI prompt configurations automatically.
We’d love your help trying it out! Share your thoughts here and be part of making it even better.
Before you dive in, let me give you a bit of context on the problem we’re solving, the solution we built, and who it’s for.
The Problem
Releasing new prompts or model configurations is risky. Synthetic data tests or small evaluation sets just can’t predict how real users will react—everyone has different expectations, sensitivities, and context.
Without a safe way to roll out updates gradually, teams either risk breaking the user experience or slow down innovation. On top of that, AI teams are often stuck in release schedules and code cycles, making it hard to focus on what really matters: improving prompts and AI behavior quickly and safely.
The Solution
AI Model Match tackles the risks of releasing new prompts by letting teams roll out changes gradually and safely. Instead of relying on synthetic data or small tests, updates are exposed to real users in controlled phases, allowing the system to monitor performance, collect feedback, and detect issues immediately. Underperforming prompts are automatically rolled back, while the best strategies are promoted in real time. It’s like A/B testing for AI prompts, but continuous, adaptive, and fully automatic.
Who It’s For
AI Model Match is designed for AI PMs who want to test and iterate on AI behavior faster, for AI teams seeking data-driven optimization rather than only tests on synthetic data, and for companies committed to delivering consistent, reliable AI experiences while continuously improving and minimizing risks.
How It Works
AI Model Match organizes AI experimentation into structured use cases, flows, and steps, allowing teams to test and optimize prompt configurations safely and efficiently. For each use case, multiple Execution flows can be created, with each flow composed of precise steps that guide AI behavior at every stage of the interaction.
When a flow is released, the system distributes traffic across users in controlled phases:
In the warmup phase, new flows are gradually introduced until they reach their target traffic.
During the adaptive phase, traffic is automatically shifted toward higher-performing flows based on real user feedback.
If a flow underperforms or fails to meet defined thresholds, the escape mechanism triggers an automatic rollback, protecting the user experience and minimizing risk.
Feedback collected from these interactions drives the continuous optimization of AI behavior, promoting the best-performing flows while retiring ineffective strategies.
Want More?
AI Model Match is fully open-source, so you can self-host, customize, and adapt it to your needs. We’d love your feedback, contributions, and ideas to help improve the project. To make integration even easier, we’ve also provided a Python SDK so you can connect it seamlessly with your applications.
Github: https://github.com/ai-model-match
Docker Hub: https://hub.docker.com/u/aimodelmatch