Compare · Lunary

MCPSpend vs. Lunary

Lunary is a solid open-source LLM monitoring + prompt management platform — strong on prompt versioning, user feedback loops, and offline evals. MCPSpend is something different: zero-code installation purpose-built for the MCP tool layer, with opinionated cost dashboards out of the box. Lunary requires SDK instrumentation in your agent code; MCPSpend works without touching it.

DimensionMCPSpendLunary
Product categoryAI cost observability (MCP-native)Open-source LLM monitoring, prompt management, evals, user feedback
Primary unit trackedMCP tool call (server + tool + cost + latency)LLM completion (prompt → response, with optional user feedback)
Install pathOne CLI command — auto-detects Cursor, Claude Desktop, Windsurf, VS Code, Claude CodeInstall lunary SDK, wrap each LLM call with Lunary handlers
MCP tool call visibilityNative — every call decoded automaticallyManual — you instrument each tool invocation as a Lunary event
Works in closed IDEs (Cursor, Claude Desktop)Yes — config-only wrapNo — requires SDK instrumentation inside the agent code
Prompt versioning & template managementNot in scopeCore feature
User feedback loops & evalsNot in scopeCore feature
Dollar budget + alerts$ budget at 50/80/100% via email + Slack out of the boxCost tracking present; alerting depends on integration setup
Per-MCP-server cost breakdownBuilt-in dashboardNot modelled — build with metadata
Self-hosted coreProxy + MCP server MIT on npmFully open-source, Docker-deployable
Free tier (managed)25,000 tool calls / month forever1,000 LLM events / month free (cloud)
Paid entry point$29 / month (Pro)Self-host free; managed cloud from ~$20 / month

Choose MCPSpend if…

  • ✓ Your agents live in IDEs you don't control the source of
  • ✓ You want first-class MCP cost dashboards without writing SDK code
  • ✓ $ budget + Slack alerts are required day one
  • ✓ EU-hosted + GDPR Art. 15/17/20 self-serve matters

Choose Lunary if…

  • ✓ Prompt versioning + template management is a top use case
  • ✓ You want user-feedback loops tied to specific LLM completions
  • ✓ Offline evaluations / regression tests on prompts matter
  • ✓ You prefer a fully open-source, self-hostable stack

They're complementary.

Lunary handles prompts, evals, and user feedback. MCPSpend handles MCP tool cost attribution. Different layers, both useful.