jeremylongshore Cursor Usage Analytics
Track and analyze Cursor usage metrics for Business and Enterprise plans. Covers dashboard metrics, cost optimization, adoption tracking, and ROI measurement.
Admin Dashboard Overview
Access: cursor.com/settings > Team > Usage (Business/Enterprise only)
┌─ Dashboard ────────────────────────────────────────────┐
│ │
│ Total Requests This Month: 12,847 │
│ Fast Requests Remaining: 2,153 / 15,000 │
│ Active Users: 28 / 30 seats │
│ Most Used Model: Claude Sonnet (62%) │
│ │
│ ┌─ Usage Trend ─────────────────────────────────┐ │
│ │ ▆▆▇▇██▇▆▇█▇▇▆▅ │ │
│ │ Mon Tue Wed Thu Fri Sat Sun │ │
│ └───────────────────────────────────────────────┘ │
│ │
│ ┌─ Top Users ───────────────────────────────────┐ │
│ │ alice@co.com 847 requests (Sonnet) │ │
│ │ bob@co.com 623 requests (GPT-4o) │ │
│ │ carol@co.com 591 requests (Auto) │ │
│ └───────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────┘
Key Metrics
Request Metrics
| Metric | What It Measures | Target |
|---|---|---|
| Total requests | All AI interactions (Chat, Composer, Inline Edit) | Growing month-over-month |
| Fast requests | Premium model uses (count against quota) | Stay under monthly limit |
| Slow requests | Queued requests after quota exceeded | Minimize (upgrade if high) |
| Tab acceptances | How often Tab suggestions are accepted | 30-50% acceptance rate is healthy |
User Adoption Metrics
| Metric | Healthy | Needs Attention |
|---|---|---|
| Weekly active users | 80%+ of seats | Below 50% of seats |
| Requests per user/day | 5-20 | Below 3 (underutilization) |
| Users with 0 requests (30d) | 0-10% of seats | Above 20% (wasted seats) |
| Model diversity | 2-3 models used | Single model only |
Cost Metrics
| Metric | Calculation |
|---|---|
| Cost per seat | Plan price / active users |
| Cost per request | Total spend / total requests |
| BYOK costs | Sum of API provider invoices |
| Total AI spend | Cursor subscription + BYOK costs |
Quota Management
Fast Request Quota
Each team member gets ~500 fast requests per month (varies by plan). Fast requests are consumed when using premium models (Claude Sonnet/Opus, GPT-4o, o1, etc.).
When quota is exceeded:
- Requests are queued as "slow" (may take 30-60 seconds instead of 5-10)
- Tab completion is unaffected
- cursor-small model remains fast
Strategies to Stay Under Quota
1. Default to Auto mode
- Cursor routes simple queries to cheaper models
- Only uses premium models when complexity warrants it
2. Educate team on model selection
- Simple questions → cursor-small or GPT-4o-mini
- Standard coding → GPT-4o or Claude Sonnet
- Hard problems only → Claude Opus, o1 (these burn quota fast)
3. Reduce round-trips
- Write detailed prompts (fewer back-and-forth turns)
- Use @Files instead of @Codebase (less context = faster)
- Start new chats instead of continuing stale ones
4. BYOK for power users
- Heavy users can use their own API keys
- Their requests don't count against team quota
Reporting for Stakeholders
Monthly Report Template
# Cursor Usage Report - [Month Year]
## Summary
- Active users: X / Y seats (X% utilization)
- Total AI requests: X,XXX
- Fast request quota usage: XX%
- Monthly cost: $X,XXX
## Adoption Trends
- New users onboarded: X
- Users showing increased usage: X
- Inactive users (0 requests): X
## Model Usage Distribution
- Claude Sonnet: XX%
- GPT-4o: XX%
- Auto: XX%
- Other: XX%
## Recommendations
- [Scale / optimize / train based on data]
ROI Calculation
Time saved per developer per day: ~1 hour (conservative estimate)
Working days per month: 22
Developer hourly cost (fully loaded): $75
Monthly time savings per developer: 22 hours × $75 = $1,650
Cursor cost per developer: $40/month (Business)
ROI per developer: $1,650 - $40 = $1,610/month
ROI multiple: 41x
Break-even: developer saves >32 minutes/month
Note: Actual time savings vary. Track team velocity (story points, PRs merged, cycle time) before and after Cursor adoption for data-driven ROI.
Usage Optimization Playbook
For Underutilized Teams (< 5 requests/user/day)
1. Run team training session (30 min demo of Chat + Composer)
2. Share the cursor-hello-world skill for hands-on practice
3. Create project rules (.cursor/rules/) so AI gives better results
4. Assign "AI Champion" per team to share tips and answer questions
5. Set a 30-day adoption goal and review progress
For Overutilized Teams (quota consistently exceeded)
1. Review model usage -- are users defaulting to expensive models?
2. Enable Auto mode as team default
3. Train on efficient prompting (fewer turns = fewer requests)
4. Consider BYOK for top 5 users (offloads their usage from team quota)
5. Evaluate upgrading to more seats or Enterprise plan
For Inconsistent Usage
1. Check if project rules are configured (AI is less useful without them)
2. Verify indexing works (poor @Codebase = poor experience)
3. Look for extension conflicts (GitHub Copilot still enabled?)
4. Survey team for friction points and address them
Enterprise Considerations
- Advanced analytics: Enterprise plans include detailed per-user, per-model, per-project breakdowns
- API access: Programmatic access to usage data for integration with internal dashboards (Enterprise)
- Compliance reporting: Usage logs can support audit requirements (who used AI, when, which model)
- Cost allocation: Tag usage by team/project for internal chargeback accounting