Freeplay
Also known as: Freeplay AI
LLM product testing, evaluation, and observability platform that unifies prompt management, evals, experiments, and production monitoring for cross functional teams.
Freeplay is an end to end platform for testing, evaluating, and monitoring LLM powered products. Its central idea is that building with language models is a team sport, so it brings prompt management, evaluations, experiments, and production observability into one surface where product managers, engineers, QA, and domain experts review the same traces instead of trading spreadsheets. The company was founded in 2022 by Ian Cairns and Eric Ryan, who previously led Twitter's developer platform and first worked together at Gnip, and it is aimed at the cross functional teams taking generative AI features from prototype to production.
Freeplay can act as the source of truth for prompt templates, which an application fetches at runtime or build time, so non engineers can iterate on prompts and swap models without a code change. A prompt editor lets teams test combinations of prompts, models, and parameters against real data, then launch batch tests from the browser. Because Freeplay treats every prompt and model configuration as an experiment, teams can compare versions side by side and, once satisfied, deploy a change straight from the results page much like a feature flag.
On the evaluation side, Freeplay runs offline and online evals using model graded judges, code based scorers, and auto categorization, and its signature workflow aligns those automated judges with human labels so the scores reflect a team's actual judgment. Production observability lets teams search and filter across millions of completions down to individual traces, inspecting prompt versions, input variables, RAG context, cost, and latency. Human in the loop review and labeling queues turn production issues into curated datasets for testing and fine tuning, closing the loop between what ships and what gets improved.
Freeplay is designed to stay out of the runtime hot path: it separates prompt management, LLM calls, and observability, and ships SDKs for Python, Node and TypeScript, and the JVM so developers keep full control of their code and model choice. It supports multiple providers including OpenAI, Anthropic, Google Vertex, AWS Bedrock, and Groq. The product is generally available with a free tier to start, and self hosting and enterprise options on request. Freeplay has raised about $8.9M, most recently a round led by Renegade Partners in 2025, and counts teams like Postscript and Help Scout among its users.
Vendor details
Canonical URL
https://freeplay.ai
Category
Agent infrastructure
Subcategory
Evaluation and observability
Funding status
Founded in 2022 by Ian Cairns and Eric Ryan, former leaders of Twitter's developer platform who first worked together at Gnip. Has raised about $8.9M: a $3.25M seed in late 2023 co led by Conviction and Matchstick Ventures, and a $5.6M round in June 2025 led by Renegade Partners. Independent.
Company status
independent
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Multi vendor model support across OpenAI, Anthropic, Google Vertex, AWS Bedrock, and Groq, with SDKs for Python, Node and TypeScript, and the JVM that keep Freeplay out of the runtime hot path. Partnerships such as MongoDB Atlas support RAG workflows, and prompts can be fetched at runtime or build time.
In practice
Your PMs track LLM test cases in a spreadsheet and copy paste into a playground while engineers coordinate prompt changes with deploys. Freeplay gives everyone one place to edit prompts, run batch tests, and ship from the browser.
You shipped an AI feature and have no idea what's happening in production. Freeplay logs every call, runs evals on live traffic, and routes flagged results to review queues so problems surface before customers complain.
Your LLM as judge scores do not match what your domain experts consider good. Freeplay's alignment workflow calibrates the automated judges against human labels so the eval scores you ship against reflect your team's standards.
Sources & related URLs
Related / legacy domains
Capability coverage
5.5 / 14 capabilities · 39%
| Integrations & Tool CallingEmbeds into an application via SDKs and supports multiple model providers (OpenAI, Anthropic, Google Vertex, AWS Bedrock, Groq), with partnerships such as MongoDB Atlas, but it is an evaluation layer rather than a tool calling hub. | Partial |
|---|---|
| Workflow OrchestrationOrchestrates evaluation and test workflows such as batch tests and experiments, but does not orchestrate agent execution, sequencing, or branching at runtime. | Unable to verify |
| Knowledge Grounding & RAGInspects and evaluates RAG context and supports RAG specific evals, but does not itself provide retrieval or knowledge grounding to agents. | Unable to verify |
| Human Oversight & GuardrailsStrong human in the loop review and labeling queues with a workflow to align automated judges to human labels, providing oversight of output quality, but not runtime approval gates or guardrails on agent actions. | Partial |
| Security, Identity & GovernanceMarketed as enterprise ready with a self hosting option for data ownership, but specific certifications and identity and governance controls are not publicly detailed. | Partial |
| Observability & AuditabilityCore product. Production observability with search and filtering across millions of completions down to individual traces, exposing prompt versions, input variables, RAG context, cost, and latency. | Full |
| Memory & State PersistencePersists logs, traces, and curated datasets for evaluation, but does not provide an agent memory or state persistence layer. | Unable to verify |
| Deployment & Data ResidencyAvailable as managed SaaS with self hosting offered on request for data residency, but self hosting is enterprise arranged rather than an openly available build. | Partial |
| Prebuilt Agents, Templates & PacksProvides evaluator types and scorer configurations and manages a team's own prompt templates, but ships no prebuilt agents or starter application templates. | Unable to verify |
| Triggers & Channel CoverageSupports monitors and alerts on production quality metrics and SDK integration tests that can run in CI, but has no conversational channels or agent invocation. | Partial |
| Model Flexibility & RoutingBroad multi vendor model support with easy model swapping and side by side comparison in prompt management and experiments, but it sits out of the runtime hot path and is not a routing gateway. | Partial |
| APIs, SDKs & MCP ExtensibilityRobust REST API and SDKs for Python, Node and TypeScript, and the JVM, plus LLM optimized documentation for coding agents, though no MCP server is documented. | Partial |
| Testing, Debugging & OptimizationCore product. Offline and online evaluations with model graded and code based scorers, experiments comparing prompt and model versions, SDK integration tests, and a workflow to align judges with human labels. | Full |
| Browser & Computer UseNot applicable. Freeplay is an evaluation and observability platform with no browser automation or computer use. | Unable to verify |
Pricing
Free tier · paid plans contact sales
Not publicly disclosed; typically seat and logged volume based for evaluation and observability platforms
Included quota
Free tier to get started. Paid tier limits and quotas are not publicly listed.
What is public
Freeplay does not publish a price card. It offers a free tier to sign up and start, and directs teams to contact the company for paid plans, self hosting, and enterprise terms.
Billing mechanics
Not publicly disclosed. Evaluation and observability platforms of this type typically bill on a combination of seats and logged volume such as traces or completions, but Freeplay does not list specifics.
Cost watchouts
Logged volume, data retention, and seat counts are common cost drivers for evaluation and observability tools but are not disclosed here.
Variable cost rationale
Pricing is not public, so this is inferred. Evaluation and observability platforms commonly bill on a mix of seats and logged volume such as traces or completions, which would scale with usage, but Freeplay discloses no specifics.
Additional watchouts
Because pricing is not listed, teams must scope cost directly with the vendor, and the eventual bill likely depends on user seats and the volume of logged completions or traces.
Overage / add-ons
Not publicly disclosed.
Sales call required
Yes — required for paid access
Free / trial
Free tier available; sign up without a card
Lowest paid plan
Not publicly listed; contact sales
Commercial notes
Sold to cross functional AI product teams and enterprises, with a free entry point for evaluation and a sales assisted motion for paid, self hosted, and enterprise deployments. Generally available with named customers including Postscript and Help Scout.
Key ambiguities
Whether paid pricing is seat based, volume based, or a hybrid, and where the free tier limits sit, are not public.
Cancellation / refund
Not publicly disclosed; arranged with the vendor.
Support SLA / resale
Not publicly disclosed; enterprise support and SLA terms are arranged with the vendor.
Missing data
All paid pricing, tier limits, seat costs, and enterprise terms are not public and require contacting Freeplay.
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