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Machina Sports vs Referee.chat

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Machina Sports compared with Referee.chat
DetailMachina Sports logo Machina SportsReferee.chat logo Referee.chat
What it doesPlatform to build and run sports AI agents powered by your data An AI panel debates your goal; a referee rules on re-checkable evidence
StageNot given Not given
Founded2023 Not given
Based inUS Not given
Pricing modelFreemium Freemium
PricingFree trial: 7 days, 100K tokens, no card required. Builder: $200 per month including 2M tokens, 1 project, 3 seats. Enterprise: custom pricing scoped with the team for customer-facing production. Free: $1.00 starting credit once. Credit plans from $10 top-up with no expiration. Your own key: 5% infrastructure fee after the first $5.00 of usage.
Key features
  • Factory: Turn brief into sports app or agent preview
  • Studio: Manage projects, agents, workflows, activity, and usage
  • Arena: Evaluate agents, workflows, and model configurations
  • AI Router: Route model requests through configured providers and profiles
  • Workflow governance with approval modes
  • Connect multiple data sources including live sports feeds and CRM data
  • SDK and published APIs for integration
  • Output record and audit trail
  • Sports-native agent with entity model
  • No betting advice or wagering guidance
  • Panel of up to six AI models from different vendors
  • Referee rules on re-checkable evidence only
  • Searches academic databases: arXiv, OpenAlex, Crossref, Europe PMC
  • Executes Python code to verify claims
  • Customizable acceptance standards
  • Matches pause and resume without losing work
  • Shared ledger prevents re-derivation
  • Spending limit per match
What makes it different
  • Platform purpose-built for sports workflows, not generic agent framework
  • Built-in approval and governance for compliance and brand rules
  • Structured evaluation and comparison before deployment
  • Live sports data grounding with current match state
  • Measurable output with telemetry and audit trail
  • Referee decides on evidence, not argument quality
  • Panel from different vendors fails in different directions
  • Stuck is a targeted question, not model thrashing
  • Cost grows with ledger, not round count
  • Published matches become research record
Community votes00
DR (Domain Rating by Ahrefs)28 no change since last check2 no change since last check
Trust Flow (Majestic)90
Citation Flow (Majestic)3113
Referring domains (Majestic)16231

5 details filled in for both companies.

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