Freeplay

Freeplay is a comprehensive, all-in-one platform engineered to streamline the entire lifecycle of AI application development. It empowers AI teams, including both engineers and domain experts, to collaborate effectively and ship high-quality AI products faster. By creating a tight 'data flywheel,' Freeplay facilitates continuous improvement, transforming the often-chaotic process of AI development into a disciplined, testable, and data-driven workflow.

The platform is built around three core pillars: Build, Test, and Learn. It provides essential tools to manage prompts and models, define custom evaluations, run rigorous experiments, monitor performance in production, and analyze user interaction data. This integrated approach eliminates the need for patchwork solutions, allowing teams to move from ideation to deployment and iteration with confidence and efficiency.

How to use Freeplay

Using Freeplay involves integrating its tools into your existing development workflow to create a cycle of continuous improvement:

1. Integration: Start by integrating Freeplay's lightweight SDKs and APIs into your codebase. This is a non-intrusive process with zero latency impact on your production environment.
2. Build & Prompt Engineering: Use the Customizable Playground to craft, test, and compare prompts across various LLM providers like OpenAI, Anthropic, and Google Vertex. Manage and version your prompts and model configurations like feature flags.
3. Testing & Evaluation: Define custom evaluations (evals) that measure quality metrics specific to your product's needs. Run batch tests and experiments to quantify the impact of any change to your prompts or agent pipelines. Freeplay can automatically run your entire test suite.
4. Deployment & Monitoring: Ship your AI features with confidence. Use the LLM Observability tools to get instant search and review capabilities for any LLM interaction, from development to production. Set up alerts to catch issues proactively.
5. Learn & Iterate: Leverage the production monitoring data and customer feedback to gain actionable insights. Use the multi-player data review and labeling workflows to analyze interactions, identify patterns, and create high-quality datasets for fine-tuning or further experimentation.

Core Features of Freeplay

• Prompt & Model Management: Version control and deploy prompt and model changes like feature flags for rigorous A/B testing and experimentation.
• Custom Evaluations (Evals): Create and fine-tune custom evaluation metrics to measure the quality and performance specific to your product's unique requirements.
• LLM Observability: Gain deep visibility into your AI application's performance with instant search and review of all LLM interactions in both development and production.
• Customizable Playground: A central hub to craft prompts for any LLM provider and quickly compare results side-by-side.
• Batch Tests & Experiments: Launch automated tests from the app or your code to systematically measure the impact of every change.
• Production Monitoring & Alerts: Use evals and customer feedback to detect issues in real-time and receive actionable alerts.
• Data Review & Labeling: Collaborative workflows for teams to analyze, label, and derive insights from production data.
• Dataset Management: Effortlessly turn production logs into structured test cases, golden sets, and fine-tuning datasets.

Use Cases for Freeplay

Freeplay is trusted by companies ranging from fast-growing startups to Fortune 100 enterprises. Use cases include:

• Developing Agentic AI: Building, evaluating, and monitoring complex AI agents with end-to-end observability.
• E-commerce AI: Creating and refining AI features for e-commerce platforms, ensuring changes positively impact customer experience.
• SaaS Product Enhancement: Integrating reliable and high-performing AI features into existing SaaS products, as demonstrated by Help Scout and Postscript.
• Multimodal AI Workflows: Testing and optimizing applications that involve voice, vision, and other modalities.
• Enterprise AI Deployment: Ensuring security, control, and reliability for AI applications in regulated industries with features like private hosting and SOC 2 compliance.

Advantages of Freeplay

The primary advantage of Freeplay is its unified, end-to-end approach. It replaces a fragmented toolchain with a single, cohesive platform, leading to:

• Accelerated Iteration: Teams report up to a 10x increase in the pace of iteration and prompt improvements.
• Increased Confidence: Move from 'vibe-prompting' to a disciplined, testable workflow, shipping features with certainty about their impact.
• Enhanced Collaboration: A common platform for engineers, product managers, and domain experts to participate in the AI development process.
• Enterprise-Grade Security: SOC 2 Type II and GDPR compliant, with options for private cloud hosting, SSO, and granular role-based access control (RBAC).
• Full Developer Control: Lightweight SDKs and APIs integrate into any stack without requiring new frameworks or introducing production latency.

Pricing and Plans

Freeplay offers a flexible pricing structure to suit teams of all sizes:

• Free Plan: $0/month. Includes access to all features, unlimited users, unlimited auto-evals, 10,000 completions/month, 1 project, and 10 test runs/month.
• Growth Plan: Starts at $500/month. Includes everything in the Free plan, with limits increased to 100,000 completions/month, 5 projects, and 50 test runs/month. Also includes a 14-day free trial.
• Enterprise Plan: Custom pricing. Designed for high-volume needs, it includes everything from Growth plus features like self-hosting, SSO/SAML, SLAs, a dedicated AI Engineer for support, and bespoke training sessions.

Startup discounts of up to 50% off Growth and Enterprise plans are also available.