Frugal Tokens – explore costs and usage across coding agents

I wanted to share a project I’ve been working on called Frugal Tokens. I originally built it because I was curious to see how much all of my sessions cost and how much cache misses affected that spend. I’d noticed people had widely different spend profiles and wanted to better understand what might contribute to that.As I’ve worked on this, the tool has grown to show more usage patterns across all of your sessions. It shows overall usage, estimated working time and overlapping sessions, and where your spend is coming from across models and cache misses. I also have a few session level metrics with percentile breakdowns, along with a list of your sessions and high level info.Clicking into a session opens an explorer where you can see individual model calls and tool inputs and outputs. You can also jump directly to where a cache miss happened. There’s also a rough cost comparison that shows what the recorded session would have cost with another model’s pricing, or for Anthropic, with 5m vs 1h caching.In the future, I’d love to collect more information to see which patterns might make people’s workflows more expensive, e.g. long sessions, high context usage, many turns, etc.The tool requires deno, but is just one command to run once that is installed. The demo provided has some of the data scrubbed, but helps to show what it looks like before running it.Would appreciate any thoughts or ideashttps://github.com/dpclark4/frugal-tokens

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62

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Verdict

VALIDATE

medium confidence

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A cost tracking and debugging tool for Claude API users that visualizes token usage, cache efficiency, and spending patterns across sessions.

The pain

People using Claude API extensively don't know why their bills vary wildly month-to-month or session-to-session. They can't identify whether cache misses, long contexts, or specific workflows are driving costs. This creates anxiety about spending and prevents optimization.

The gap

Anthropic provides raw API logs but no visualization or analysis layer. Users must manually parse logs or build their own dashboards. There's no tool that connects spending to specific behaviors (session length, context size, cache strategy) to help users understand their cost drivers.

Build angle

Start by validating that power users of Claude API (agencies, startups, researchers) actually want this and would pay for it. The current free tool is a proof-of-concept. The real business is either: (1) a SaaS dashboard with team features and historical trending, or (2) a premium version with predictive cost modeling and optimization recommendations. Test willingness-to-pay before building either.

Strengths

  • Solves a real problem for a growing segment (Claude API power users spending $100-10k+/month)
  • Low competition, Anthropic hasn't built this, and no third-party tool dominates the space
  • Technical founder with working prototype and clear understanding of the problem space
  • Extensible foundation, can add team dashboards, alerts, cost forecasting, and optimization suggestions
  • Natural upsell path from free tool to paid analytics platform

Risks

  • Market size unclear, how many Claude API users actually care about cost optimization vs. just paying the bill?
  • Anthropic could build this natively into their dashboard, eliminating the business overnight
  • Requires API access to user logs, creating data privacy and security concerns that could limit adoption
  • Deno dependency and local-only setup limits accessibility; most users expect cloud SaaS
  • Monetization path undefined, unclear if users will pay for what's currently free, or if the TAM is large enough to justify building a company around it
  • Feature creep risk, the roadmap (pattern detection, workflow optimization) requires significant ML/data science work with uncertain ROI

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