Cursor Usage Analytics
Analytics Overview
Available Metrics (Business/Enterprise)
User Metrics:
- Active users (daily/weekly/monthly)
- User engagement levels
- Feature adoption rates
- Usage patterns by team
AI Metrics:
- Completions generated
- Completions accepted/rejected
- Chat messages sent
- Composer operations
- Model usage breakdown
Cost Metrics:
- API usage by model
- Cost per user
- Cost trends
- Budget utilization
Accessing Analytics
Admin Dashboard > Analytics
Views:
- Overview dashboard
- User details
- Team breakdown
- Time-based trends
- Export options
Key Metrics
Completion Metrics
Completion Acceptance Rate:
= Accepted completions / Total completions × 100
Target: 40-60% is healthy
Low (<20%): May indicate poor context/rules
High (>80%): May indicate over-reliance
Completions per User:
Track productivity gains
Compare across teams
Chat Metrics
Chat Sessions per Day:
- Average messages per session
- Time spent in chat
- Resolution rate
Useful queries:
- How many questions answered
- Code generated from chat
- Follow-up question rate
Composer Metrics
Composer Usage:
- Operations per week
- Files modified per operation
- Apply rate (accepted vs rejected)
- Multi-file vs single-file edits
Dashboard Views
Executive Summary
┌─────────────────────────────────────────────────────────┐
│ CURSOR USAGE - EXECUTIVE SUMMARY │
├─────────────────────────────────────────────────────────┤
│ Active Users: 127/150 (85%) │ Cost: $3,200/mo │
│ Completions: 45,000 this month │ Per User: $25.20 │
│ Acceptance Rate: 52% │ Trend: ↓ 5% │
├─────────────────────────────────────────────────────────┤
│ Top Features: │ Growth: │
│ 1. Tab Completion (78%) │ Users: +12% MoM │
│ 2. Chat (65%) │ Usage: +23% MoM │
│ 3. Composer (34%) │ Efficiency: +18% │
└─────────────────────────────────────────────────────────┘
Team Breakdown
Team Analytics View:
Engineering:
- Users: 45
- Avg completions/day: 120
- Primary model: GPT-4 Turbo
- Top use case: Code generation
Design:
- Users: 12
- Avg completions/day: 40
- Primary model: GPT-4
- Top use case: CSS/styling
Data Science:
- Users: 8
- Avg completions/day: 85
- Primary model: Claude
- Top use case: Python/analysis
Individual User Metrics
User Detail View:
Username: developer@company.com
Role: Member
Last Active: 2 hours ago
30-Day Stats:
- Completions: 2,450
- Acceptance Rate: 58%
- Chat Sessions: 145
- Composer Uses: 23
Model Preferences:
- GPT-4 Turbo: 65%
- GPT-3.5: 30%
- Claude: 5%
Active Hours: 9am-6pm EST
Custom Reports
Creating Reports
Admin > Analytics > Custom Reports
Report Types:
- Usage summary (daily/weekly/monthly)
- User activity
- Cost breakdown
- Feature adoption
- Team comparison
Scheduled Reports
Automate reporting:
1. Create report template
2. Set schedule (daily/weekly/monthly)
3. Add recipients
4. Choose format (PDF/CSV/Email)
5. Enable delivery
Example schedules:
- Weekly usage summary to managers
- Monthly cost report to finance
- Daily activity to team leads
Export Options
Export formats:
- CSV (raw data)
- PDF (formatted report)
- JSON (API integration)
Export via:
- Dashboard download
- Scheduled export
- API endpoint
Analytics API
API Access
# Get usage summary
curl -X GET "https://api.cursor.com/v1/analytics/summary" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"start_date": "2024-01-01",
"end_date": "2024-01-31"
}'
Available Endpoints
GET /analytics/summary
- Overall usage metrics
GET /analytics/users
- Per-user breakdown
GET /analytics/teams
- Team-level metrics
GET /analytics/features
- Feature adoption data
GET /analytics/costs
- Cost breakdown
Integration Examples
# Python integration
import requests
def get_cursor_analytics(start_date, end_date):
response = requests.get(
"https://api.cursor.com/v1/analytics/summary",
headers={"Authorization": f"Bearer {API_KEY}"},
params={
"start_date": start_date,
"end_date": end_date
}
)
return response.json()
# Use in dashboards, reports, etc.
Benchmarking
Industry Benchmarks
Typical ranges:
Completion Acceptance Rate:
- Low: < 30%
- Average: 40-55%
- High: > 60%
Daily Active Users:
- Low: < 50% of licenses
- Average: 60-75%
- High: > 80%
Features Used:
- Basic: Tab completion only
- Intermediate: + Chat
- Advanced: + Composer + Rules
Internal Benchmarking
Compare teams:
- By department
- By experience level
- By project type
- Over time
Identify:
- Top performers (share practices)
- Training opportunities
- Underutilized features
Optimization Insights
Low Acceptance Rate
Causes & Solutions:
Poor context:
→ Improve .cursorrules
→ Better indexing setup
Wrong model:
→ Switch to more appropriate model
→ Adjust per-task settings
Lack of training:
→ Conduct workshops
→ Share best practices
Underutilization
Signs:
- Low daily active users
- Only basic features used
- High cost per completion
Solutions:
- Training sessions
- Share success stories
- Optimize onboarding
- Gamify adoption
High Costs
Investigation:
- Which models are expensive?
- Who are top users?
- What's the ROI?
Optimization:
- Default to cheaper models
- Set usage guidelines
- Implement budgets
- Track productivity gains
Privacy & Compliance
Data Collected
Analytics tracks:
- Usage patterns (aggregated)
- Feature adoption
- Performance metrics
- Cost data
Does NOT track:
- Code content
- Chat conversations
- Personal data beyond email
Compliance Features
GDPR:
- Data export capability
- Deletion support
- Consent management
SOC 2:
- Audit logs
- Access controls
- Data retention policies
Best Practices
Regular Review
Weekly:
- Check active user trends
- Monitor unusual patterns
- Review top users
Monthly:
- Full analytics review
- Cost analysis
- Feature adoption check
- Generate reports
Quarterly:
- Deep dive analysis
- ROI calculation
- Strategy adjustment
- Benchmark comparison
Acting on Insights
Data → Action:
Low adoption → Training program
High costs → Model optimization
Feature gaps → Targeted workshops
Power users → Champions program