Skip to main content
Building AI products is fundamentally different from building traditional software. Success depends on how fast your team can iterate: identifying what’s working, experimenting with improvements, and validating changes before they reach users. The teams that execute this cycle fastest ship better products. Freeplay is built from the ground up to accelerate this iteration cycle. Where other tools can offer a collection of developer utilities and leave teams to figure out how the pieces connect, Freeplay provides an opinionated workflow that guides your team through a continuous feedback loop from production insights to tested improvements.

​
A connected workflow

AI engineering teams quickly discover they need common components to support their ops workflow — like logging, playgrounds, and evaluation runners. But these often feel disconnected from each other. Each solves a narrow problem without consideration for what comes before or after. Freeplay takes an integrated approach. Every feature is designed to feed into the next step of your iteration cycle:
  • Prompt templates and named agents separate structure from data. Freeplay distinguishes between the static parts of your prompts and the variables populated at runtime. Each instance of a trace for a specific agent or sub-agent gets named and grouped together. This structure makes it seamless to turn production logs into replayable dataset rows for testing.
  • Datasets enforce compatibility. Dataset schemas match prompt templates or specific agents, so you always know whether a dataset will in a given test scenario. No more time lost reformatting test data.
  • Evaluations reference your data structure. When writing LLM judges or code-based evaluators, you can target or compare specific input variables (not just an entire interpolated input blob). This precision leads to more meaningful quality signals.
  • Production traces become test cases. Annotated examples from production flow directly into datasets. Failures become regression tests. The system is designed to turn usage into better testing data.
This connected design provides the foundation for building a data flywheel: where each iteration strengthens not just your core prompts and agents, but also your datasets, evaluation criteria, and testing infrastructure. Over time, those elements compound into a closed loop for continuous improvement.

​
True cross-functional collaboration

AI product development works best when engineers, product managers, designers, and domain experts work together. The people closest to customer or business problems often have the clearest sense of what “good” looks like, but most AI engineering tools relegate them to spectators who can only contribute when closely supported by an engineer. Freeplay changes this dynamic, so that each team member can contribute their full expertise. Non-engineers can:
  • Create and iterate on prompts, models and tool definitions in the playground
  • Build test datasets manually or from production logs
  • Write and refine LLM judges for custom evaluation metrics
  • Run tests and evaluations to compare prompt and model changes
  • Review agent traces and annotate quality issues
All of those can be completed without touching code. Engineers stay in control of orchestration and deployment, while domain expertise flows directly into the product. Learn what you can do in the UI →

​
AI that accelerates your workflow

The future of AI engineering involves AI agents working alongside human teams. Freeplay applies AI at specific points in the iteration cycle where it adds the most value, for example:
  • Eval generation helps you write better LLM judges faster, automatically adapting to your prompt structure and data
  • Prompt optimization uses your production data — evaluation results, user feedback, and human annotations — to generate improved prompt versions, optimized for specific models
  • Review insights analyze patterns across human notes and LLM judge reasoning to surface actionable themes and root causes
These features are designed to amplify your team’s expertise, not replace it. Humans define the goals and provide nuanced feedback, and AI accelerates the path to meeting those goals.

​
Built for enterprise teams

Freeplay truly serves the needs of enterprise product development teams: organizations with strong software engineering foundations applying AI to complex business problems. We focus on teams that need:
  • Production-grade infrastructure that works in any cloud and scales with usage, providing instant search over terrabytes of logs and traces
  • Framework flexibility to work with your existing stack, whether you write your own custom code or use popular agent frameworks
  • Security and compliance controls for enterprise requirements, including support for multi-region deployments to support strict data domicile requirements
  • Premium support to help teams without prior AI engineering experience build solid evaluations and test harnesses and adopt best practices
The data asset you build in Freeplay — your logs, evaluations, and datasets — belongs to you. It gives you full flexibility to switch models, providers, or libraries as the ecosystem evolves.

​
Get started

Start in the UI

Create prompts, datasets, and evaluations without writing code

Integrate with your app

Connect Freeplay to your existing AI application