cursor-cost-optimization

Cursor Cost Optimization

You have access to Cursor Enterprise usage data through the cursor-usage MCP server. This skill teaches you how to identify cost-saving opportunities and recommend actionable changes.

Cost Optimization Framework

Step 1: Understand the Spend Profile

Call get_team_overview to get the baseline, then:

1. Identify the spend distribution — Is spend concentrated in a few users or spread evenly?

   • If top 10% of users account for >50% of spend → focus on those users
   • If spend is evenly distributed → focus on model selection policies

2. Identify the cost driver — Is it model choice, volume, or both?

   • Call get_model_usage to see which models dominate
   • Premium models (Opus, GPT-5) at 10-50x the cost of standard models (Sonnet, GPT-4o)
   • A team of 50 where 5 people use Opus can spend more than the other 45 combined

Step 2: Model Selection Optimization

The single highest-impact cost lever is model selection.

Task Type	Recommended Model Tier	Why
Code completion / tabs	Budget (Flash)	High volume, low complexity, latency-sensitive
Inline edits (Cmd+K)	Standard (Sonnet, GPT-4o)	Good balance of quality and cost
Chat conversations	Standard	Most questions don't need frontier models
Agent mode (complex tasks)	Premium (Opus) only when needed	Reserve for genuinely complex multi-step work
Code review	Standard	Pattern matching, not creative generation

Key insight: Most developers default to the "best" model out of habit, not necessity. 80%+ of requests can be handled by standard-tier models with no noticeable quality difference.

Step 3: Spend Limits

Use set_spend_limit to set guardrails:

• Soft approach: Set limits at 2-3x the team median spend. This catches runaway usage without blocking normal work.
• Hard approach: Set limits at a fixed dollar amount per cycle. Good for budget-constrained teams.
• Per-group approach: Use billing groups to set different limits for different teams based on their needs.

Warning: Setting limits too low causes developer frustration and workarounds. Start generous and tighten based on data.

Step 4: Usage Pattern Optimization

1. Agent mode loops: Check get_usage_events for users with many consecutive agent requests. Long agent loops are the #1 cause of unexpected spend spikes.
2. Headless requests: Filter events where isHeadless: true. These are background processes (Bugbot, indexing) that may be running unnecessarily.
3. Low acceptance rates: Call get_agent_edits and get_tabs. If acceptance rates are below 30%, the team may need better prompting practices, not more AI requests.

Cost Benchmarks

These are rough benchmarks based on typical enterprise teams:

Team Size	Monthly Spend (healthy)	Monthly Spend (high)	Monthly Spend (alarm)
10 devs	$200-500	$500-1,500	>$2,000
50 devs	$1,000-3,000	$3,000-8,000	>$10,000
100 devs	$2,000-6,000	$6,000-15,000	>$20,000
500 devs	$10,000-30,000	$30,000-75,000	>$100,000

These assume a mix of standard and premium model usage. Teams exclusively using premium models will be 3-5x higher.

Presenting Recommendations

When presenting cost optimization findings:

1. Lead with the dollar impact — "Switching 3 users from Opus to Sonnet would save ~$X/month"
2. Show the data — Reference specific users, models, and spend figures
3. Acknowledge tradeoffs — Premium models ARE better for complex tasks; the goal is right-sizing, not downgrading
4. Suggest incremental changes — Don't recommend sweeping policy changes; suggest a pilot with willing users first

For Deeper Analysis

This skill covers quick, data-driven cost optimization. For ongoing monitoring with:

• Automated anomaly detection (statistical outlier detection)
• Slack/email alerts when spend spikes
• Historical trend analysis over months
• Incident lifecycle tracking (MTTD/MTTI/MTTR)

See cursor-usage-tracker — the open-source dashboard built for exactly this.