Pricing the Unknown: Ref builds credit-based pricing for the first standalone paid MCP server
Ref faced the novel challenge of pricing a first-of-its-kind standalone paid MCP server in an ecosystem where nearly everything is free, while covering high web-crawling and indexing costs and supporting usage volumes ranging from individual developers making ten searches a day to agents making thousands.
Three months after launch, Ref has thousands of weekly users and hundreds of subscribers, with the monthly minimum covering fixed indexing costs and additional credit purchases scaling with usage.
Frequently asked questions
What did this team achieve with this AI workflow?
Three months after launch, Ref has thousands of weekly users and hundreds of subscribers, with the monthly minimum covering fixed indexing costs and additional credit purchases scaling with usage.
What tools did this team use?
Turbopuffer, ref_search_documentation, ref_read_url, Cursor, Claude Code.
What results were reported?
Weekly users: thousands of weekly users; Paid subscribers: hundreds of subscribers (source-reported, not independently verified).
How is this workflow AI workflow structured?
AI agent calls MCP tool → LLM retrieval processing → Index search via Turbopuffer → Precise documentation result returned → Web crawler keeps docs current.