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How a Global Compliance Services Company Got Usage and Spend Visibility into Claude, Gemini, and Cursor

"For the first time, we could see exactly which teams were using Claude, Cursor, and had Gemini access they had never touched. 34% of licenses had no activity in 90 days. We lacked this visibility. That single view changed every conversation we had about our AI program, internally and with our vendors."

~ Chief Information Officer, Compliance Services Company

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$2.4M
AI spend under direct visibility
34%
of AI licenses found inactive
$1.2M
saved in the first quarter

$2.4M

AI spend under direct visibility

34%

of AI licenses found inactive

$1.2M

saved in the first quarter
Problems
Challenge
  • Claude, Cursor, and Gemini each billed on a different model,  tokens, credits, and usage tiers, against budgets built for fixed-seat software.
  • Usage data for each tool lived in a separate vendor console with no view of which teams were actively using what.
  • Teams held access to AI tools they had stopped using, but nothing surfaced until someone manually checked each vendor console.

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Solutions
Solution
  • CloudEagle pulled live usage data from Claude, Cursor, and Gemini, mapping every token, API call, and credit against contracts in one place.
  • Usage was tied to teams, departments, and cost centers, giving clear ownership of every dollar spent.
  • Inactive licenses surfaced automatically, so unused access was identified without manual audits of three separate vendor consoles.

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Profit
Result
  • $2.4M in annual AI spend moved from quarterly reconciliation to weekly review, with every invoice line traceable to a usage event.
  • 34% of AI licenses identified as having no active usage in the prior 90 days, helping IT teams optimize them.
  • $1.2M saved in the first quarter by removing access for teams that had stopped using the tools and consolidating overlapping subscriptions.

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Challenge

This Global Compliance Services organization rolled out Claude, Cursor, and Gemini across engineering, product, and go-to-market teams for coding, research, and content workflows. Each followed a different consumption model, tokens, API usage, and credits, leading to fluctuating monthly invoices and no clear mapping of spend to teams or use cases.

With annual commitment, leadership couldn’t see who was driving usage, which use cases delivered value, or whether they were on track against commitments. Usage and cost data were fragmented across vendor consoles, making it difficult to measure ROI, compare tools, or plan renewals with confidence.

The risk: AI spend scaling quickly without visibility into efficiency, ownership, or return. The CIO was accountable for AI ROI but had no way to answer the question the board was asking: was the $2.4M actually being used, and by whom.

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Solution
  • Direct API integration with Claude, Cursor, and Gemini pulled token, API call, and credit consumption live, mapping every unit against contract terms in one view.
  • Spend Intelligence attributed every token consumed and API call made to the team, department, or cost center responsible, tied to HRIS and finance data.
  • Usage was broken down by team and department across all three tools, so active and inactive access was visible without logging into three separate vendor consoles.
  • Inactive licenses surfaced automatically across all three tools the moment usage dropped below a set threshold, so unused access was easily optimized.
  • Everything managed inside the same platform where the rest of the SaaS stack already lived, so AI spend was not a separate tool or a separate process.

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Why CloudEagle.ai?
  • Usage depth beyond logins. Most tools track who has access. CloudEagle tracks features, activity, time period, which is what surfaces idle licenses that a login count never would.
  • Token-level attribution to teams and cost centers, so consumption-based spend reconciled against the org chart in real time rather than at quarter-close.
  • AI usage and spend visibility live inside the same platform where the rest of the SaaS stack is governed, so AI is not a separate tool or a separate workflow.
  • Seeing idle licenses is not the outcome, removing them is. License Management & Harvesting is built in, so unused access is reclaimed through an automated workflow.
  • Full AI lifecycle, not just the invoice. CloudEagle breaks down spend and usage by team, by tool, in one view.

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Impact

Full AI Spend and Usage Visibility

  • $2.4M in annual AI spend consolidated into one view across Claude, Cursor, and Gemini, mapped to contract terms and team attribution.
  • Every token consumed and API call made traceable to a team, cost center, or project, with usage segmented by vendor and time window.
  • Finance reconciliation cycle for AI spend compressed from quarterly to weekly, with invoice variance explained against logged usage events.

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Waste Identified Across All Three Tools

  • 34% of AI licenses identified as having no active usage in the prior 90 days, across Claude, Cursor, and Gemini.
  • Unused licenses were harvested automatically and reassigned to users in need; customers got more out of the AI investment it had already made without buying a single additional seat. 
  • Inactive and overlapping access visible in one view without logging into each vendor console separately.

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$1.2M Saved in the First Quarter

  • $1.2M saved in the first quarter by removing access for teams with no active usage and consolidating overlapping subscriptions.
  • Standardization decisions on which tool each team should use made from side-by-side usage data rather than assumption.
  • AI spend now planned against real usage baselines rather than prior-year estimates and head count approximations.

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The Transformation

Before CloudEagle
AI usage data spread across three separate vendor admin consoles with no mapping to teams or cost centers.
AI invoices reconciled against seat-based budget lines every quarter, with monthly variance explained after the fact.
Spend spikes surfaced when the invoice landed, by which point the overage had already been committed.
AI vendor conversations sized against prior-year commitments and team head count estimates.
Questions about which AI tool delivered more value per dollar answered with anecdote and preference data.
After CloudEagle
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Claude, Cursor, and Gemini usage in one view, broken down by team and tied to what was agreed in the contract.
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AI invoice variance explained weekly against logged usage events tied to specific teams and projects.
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When any team's usage started trending high, the team was alerted before the invoice landed, not after.
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Vendor conversations went in with real usage data by team, not last year's estimates.
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Decisions about which AI tool each team should standardize on were made from actual usage numbers, not preference.

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