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Get Started Free →Optimize Fathom API usage and plan selection. Trigger with phrases like "fathom cost", "fathom pricing", "fathom plan".
.claude/skills/jeremylongshore-fathom-cost-tuning/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -2% | 0% |
Fathom pricing scales with per-seat licensing for team features, with primary cost drivers being transcript storage volume and recording hours consumed. Every meeting generates a transcript and AI summary that persist in storage. For organizations running dozens of meetings daily, unchecked transcript accumulation and redundant API polling for meeting data create unnecessary spend. Optimizing retrieval patterns and storage lifecycle directly reduces both API costs and plan overhead.
| Component | Cost Driver | Optimization | |-----------|------------|--------------| | Seat licenses | Per-user/month for Team plan | Audit active seats quarterly; remove inactive users | | Transcript storage | Accumulated meeting transcripts | Archive transcripts older than 90 days to local storage | | Recording hours | Meeting duration across all users | Disable recording for standup/informal meetings | | API polling | Repeated list/get calls for meeting data | Use webhooks for push notifications instead of polling | | CRM sync events | Per-meeting sync to Salesforce/HubSpot | Batch CRM writes; skip internal-only meetings |
typescriptclass FathomTranscriptCache { private cache = new Map<string, { transcript: string; summary: string }>(); async getTranscript(meetingId: string, apiFn: () => Promise<any>): Promise<any> { // Transcripts are immutable after generation — cache permanently if (this.cache.has(meetingId)) return this.cache.get(meetingId); const result = await apiFn(); this.cache.set(meetingId, result); return result; } async listMeetings(params: { include_summary: boolean }): Promise<any[]> { // Always use include_summary=true to avoid N+1 calls // Fetches summaries inline with the list response const response = await fetch('/api/meetings?include_summary=true'); return response.json(); } }
typescriptclass FathomUsageTracker { private apiCalls = 0; private readonly rateLimit = 60; // 60 req/min private windowStart = Date.now(); async throttledCall<T>(fn: () => Promise<T>): Promise<T> { if (Date.now() - this.windowStart > 60_000) { this.apiCalls = 0; this.windowStart = Date.now(); } if (this.apiCalls >= this.rateLimit) { const waitMs = 60_000 - (Date.now() - this.windowStart); await new Promise(r => setTimeout(r, waitMs)); this.apiCalls = 0; this.windowStart = Date.now(); } this.apiCalls++; return fn(); } getUsageReport(): { callsThisMinute: number; remainingCapacity: number } { return { callsThisMinute: this.apiCalls, remainingCapacity: this.rateLimit - this.apiCalls }; } }
include_summary=true in list requests to avoid extra calls| Issue | Cause | Fix | |-------|-------|-----| | 429 rate limit hit | Exceeding 60 req/min | Implement throttling with sliding window | | Duplicate transcript fetches | Multiple services requesting same meeting | Centralize through shared cache | | Stale meeting list | Polling on long intervals | Switch to webhook-driven updates | | CRM sync failures | Batch too large or network timeout | Chunk CRM writes into batches of 10 | | Storage costs climbing | No transcript lifecycle policy | Implement 90-day archive-to-local policy |
See fathom-performance-tuning.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 26,196 | 21,149 | -19% | 1 | 1 | 0% | 4,243 | 4,301 | +1% | 0 | 0 | — |
case-02 | fail→fail | 29,072 | 18,820 | -35% | 1 | 1 | 0% | 4,841 | 4,912 | +1% | 0 | 0 | — |
case-03 | fail→fail | 44,379 | 33,440 | -25% | 1 | 1 | 0% | 6,312 | 7,219 | +14% | 0 | 0 | — |
case-04 | pass→pass | 26,906 | 29,832 | +11% | 1 | 1 | 0% | 4,428 | 6,152 | +39% | 0 | 0 | — |
case-05 | pass→pass | 20,283 | 15,182 | -25% | 1 | 1 | 0% | 3,107 | 3,795 | +22% | 0 | 0 | — |
case-06 | pass→pass | 22,112 | 20,496 | -7% | 1 | 1 | 0% | 3,519 | 4,863 | +38% | 0 | 0 | — |
case-07 | fail→fail | 12,801 | 12,007 | -6% | 1 | 1 | 0% | 1,852 | 2,565 | +38% | 0 | 0 | — |
case-08 | fail→pass | 12,202 | 4,928 | -60% | 1 | 1 | 0% | 1,835 | 1,625 | -11% | 0 | 0 | — |
case-09 | pass→pass | 10,545 | 3,695 | -65% | 1 | 1 | 0% | 1,564 | 1,541 | -1% | 0 | 0 | — |
case-10 | pass→pass | 12,298 | 12,270 | -0% | 1 | 1 | 0% | 1,721 | 2,718 | +58% | 0 | 0 | — |
case-11 | pass→pass | 19,017 | 2,914 | -85% | 1 | 1 | 0% | 2,695 | 1,485 | -45% | 0 | 0 | — |
case-12 | pass→pass | 15,557 | 11,136 | -28% | 1 | 1 | 0% | 2,022 | 2,561 | +27% | 0 | 0 | — |
case-13 | fail→pass | 13,834 | 9,189 | -34% | 1 | 1 | 0% | 2,195 | 2,348 | +7% | 0 | 0 | — |
case-14 | pass→pass | 16,941 | 10,782 | -36% | 1 | 1 | 0% | 3,095 | 2,910 | -6% | 0 | 0 | — |
case-15 | fail→pass | 19,071 | 7,578 | -60% | 1 | 1 | 0% | 2,705 | 2,108 | -22% | 0 | 0 | — |
case-16 | pass→pass | 17,997 | 18,397 | +2% | 1 | 1 | 0% | 2,480 | 3,713 | +50% | 0 | 0 | — |
case-17 | pass→pass | 14,597 | 16,868 | +16% | 1 | 1 | 0% | 2,391 | 3,578 | +50% | 0 | 0 | — |
case-18 | fail→pass | 17,155 | 8,904 | -48% | 1 | 1 | 0% | 2,235 | 2,185 | -2% | 0 | 0 | — |
case-19 | fail→pass | 13,571 | 4,383 | -68% | 1 | 1 | 0% | 2,036 | 1,744 | -14% | 0 | 0 | — |
case-20 | fail→fail | 13,014 | 11,990 | -8% | 1 | 1 | 0% | 2,023 | 3,096 | +53% | 0 | 0 | — |
case-21 | fail→pass | 13,580 | 3,476 | -74% | 1 | 1 | 0% | 2,294 | 1,644 | -28% | 0 | 0 | — |
case-22 | fail→pass | 11,723 | 2,927 | -75% | 1 | 1 | 0% | 1,650 | 1,370 | -17% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.