Freeplay gives product teams a better way to build with LLMs. Get the power to prototype faster, test with confidence, evaluate data, and optimize features for customers — together as a team.
End-to-end platform for software excellence! 🌐🚀 How does Freeplay streamline the journey from prototyping to optimizing LLM-powered features? Eager to explore! Upvoted!
Freeplay helps companies build out critical monitoring and evaluation workflows for their LLM applications with a much higher consideration or the teams and necessary collaboration than other players in the space. We are always pushing out Gen AI Partners clients to invest in monitoring and evaluation in order to improve their overall performance and Freeplay provides a great toolkit to do it!
Hey Product Hunt!
We’re excited to announce the public beta of Freeplay. We quietly started a private beta back in May, now it’s time to open it up to a wider audience to help us shape the future product.
Over the past year, we’ve talked to >100 leaders & builders creating products with LLMs, ranging from seed stage to public companies and across engineering, product & design. They want LLMs to be:
Consistent, and not embarrass them (from hallucinations, unsafe or other bad responses)
Affordable – they can’t ruin margins on existing products
Compelling for customers, so that metrics actually move in the right direction
On top of those desires, working with AI is also changing how software gets built → developers, PMs, designers, QA & domain experts are all collaborating together in new ways. They need better tools & workflows that work for the whole team.
Freeplay is an end-to-end platform to help product teams address these challenges, and make it feel a bit more like building traditional software.
📝 Prompt management & version control that anyone on the team can use to adjust prompts & swap out models without updating code, similar to other server-side experimentation tools
🏷️ Observability & data curation in the same environment, so you can easily save & label examples from dev, staging or production
🤝 Combine the best of AI & human evaluations for quality control & optimization – the same combo that leading AI companies use
🤖 Automate testing to get the same type of confidence you have with traditional CI, and to speed up prototyping & new iterations
Early customers have been using Freeplay for a while now, and we’re ready for more companies to take advantage of it.
If you’re working on these problems, we’d love to get your feedback!
Hey all,
Over the past year, our team has been heads down crafting Freeplay, and we’re thrilled to finally share our public beta with you!
Our founding team has been building enterprise developer products together for more than a decade together. We’re bringing the things we’ve learned to help companies accelerate their adoption of LLMs in their software.
On the technical side, what stands out?Adaptable SDK Support: First-class SDKs for Python, Node, and Java. Seamlessly integrate LLMs without compromising on your tech stack.
Enterprise-Ready: Thoughtfully designed for established teams, with instrumentation and workflows that feel familiar to traditional software teams.
Commitment to Security: Built with a focus on data privacy, security, and compliance from the start. SOC2 coming soon, and we offer a self-hosting option for enhanced control and protection.
We’d love to hear what you think!
Very excited about Freeplay finally entering public beta!
Developing software in the age of generative AI is a completely different proposition, and no one has quite built a collaborative Xcode to address that opportunity.
Freeplay helps teams apply the scientific method to prompt engineering, which then allows them to ship great products with LLMs.
The future is here, it's just not evenly distributed. Until now. 😉
@chrismessina congrats 🥂 on your 🚀! I wanted to ask does this allow you to integrate LLMs into your apps, if so where is the data sourced from and how is the data ethics side and creative rights dealt with?
@imran_razak Freeplay doesn’t get involved at that level. We help manage the prompts sent to models, which can include additional data from other sources — but we let our customers populate that data in their code.
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