cursor-usage-analytics

Track and analyze Cursor usage metrics via admin dashboard: requests, model usage, team productivity, and cost optimization. Triggers on "cursor analytics", "cursor usage", "cursor metrics", "cursor reporting", "cursor dashboard", "cursor ROI".

Quality

67%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

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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

Resources

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3e83543

Last updated: 3 days ago
Created: 3 days ago

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