Claude · AI Impact & Cost

Monitor Claude costs. Understand engineering impact.

Track Claude usage and AI costs in Oobeya. Review spending alongside adoption, review time, rework, quality, and delivery. Add code-origin context separately with Blamely attribution.

Schedule a demo Explore AI Cost Monitoring

Claude impact in Oobeya

Contribution, AI costs, and delivery outcomes

Claude workflow evaluation

AI Impact Overview

How AI-assisted development relates to engineering outcomes

Coding Efficiency67.7%↑ 7.3%

Higher AI adoption coincided with improved coding efficiency.

Coding EfficiencyExplore the trend

Security: A
Maintainability: A
Reliability: C
Technical debt: 86d

AI Cost & Credits

Credit consumption, forecasts, and financial summary

Data sources

Gross usage value$18,804.04↓ 44.5%

Usage value decreased, while credit consumption exceeded the included pool.

Credit pool usage

July 2026

100.2%↓ 44.3%

Included credits consumed0% remaining

Included credit pool exceeded by 3,204.3 credits.

AI credit pool value: $18,772.00
Total AI credits used: 1,880,404.3
Avg. daily burn rate: 58,762.6
Total users: 988

Remaining credits: −3,204.3
Forecasted month-end usage: 1,880,404.3
Forecasted overage: 3,204.3
Reporting period: July 2026

Team Performance Overview

Engineering performance at a glance

Team Member Insights

New Work
Refactor
Help Others
Churn

Member impact ratio, coding efficiency, AI code share, and work distribution as percentages of New Work, Refactor, Help Others, and Churn.
Member	Impact Ratio	Coding Efficiency	AI Code Share	New Work	Refactor	Help Others	Churn
Maya PatelSenior	2.4x	94%	36%	52%	25%	17%	6%
Alex MorganMid	1.6x	92%	48%	38%	34%	20%	8%
Sam TaylorMid	0.8x	80%	76%	47%	18%	15%	20%
Jordan LeeJunior	0.6x	72%	84%	20%	30%	22%	28%

Coding Efficiency95.01%↑ 1.76% more than avg

Coding efficiency recovered to 95.01%, above the company average.

Coding EfficiencyExplore the trend

Active symptoms: 10
Critical symptoms: 2 need attention
Symptom monitoring: 10 active
Most affected area: Code Review (3)

Performance Overview

AI attribution alongside delivery and usage

AI share of new code61%↑ +3.7%

75,403 of 123,666 added lines are AI-attributed.

AI impact on deliveryExplore the trend

Delivery velocity: 158commits/week
↓ −2%prev. 161.3
Time per commitderived: 36.2min
↓ −53.3%prev. 77.6 min
Reclaimed capacityderived: 1,401hours
2,031 commits × 41.4 min saved
Developers using AIderived: 17developers
↑ +54.5%65.4% of 26 developers

Coding time split: 219.4h AI40.8h Human
Avg. lines / AI commit: 114.4 AI83.5 Human
Token spend: 905.5M
Cache efficiency: 99.3%

Explore Claude impact and cost monitoring. The Code Attribution view uses separate Blamely data; available financial measures depend on the connected source.

From signals to decisions

Connect Claude spending, usage, and engineering outcomes

Review Claude costs alongside adoption and delivery. Use attribution when you need to inspect code origin.

Track Claude AI costs

Monitor Claude spending in AI Cost. Compare costs with usage, coding efficiency, review time, and quality to evaluate your AI investment.

AI Usage & Cost Monitoring

Keep attribution in Git

Blamely records the origin of supported assistant edits and attaches attribution to commits. Inspect the contribution behind Claude-assisted changes.

Explore Blamely attribution

Read efficiency with rework

Review AI code share, coding efficiency, and churn together. Use the work distribution to focus the discussion on changes that need investigation.

Team Performance

Follow delivery and quality

Compare lead time and review movement with defects and quality findings. Check whether changes in the workflow are followed by better outcomes.

AI Impact

What to look at

Follow Claude from connection to cost and impact

Use AI Impact and its reporting views for assistant usage and costs. Blamely adds a separate source of code-origin context.

Connected source and capture coverage
Consistent team, repository, and time period
Review flow, rework, and quality trends
Delivery outcomes and follow-up actions

Connect Claude to Oobeya: Configure the Claude integration and select the reporting scope for your rollout. Review the source coverage and available usage and financial measures.
Review usage, costs, and outcomes: Use AI Impact and its Metrics and Cost & Credits views to review assistant activity and spending alongside delivery, review time, and quality.
Add code provenance with Blamely: When code origin matters, configure Blamely for supported Claude coding workflows. Attribution is captured locally and retained with Git commits, feeding the separate AI Usage analysis.

Explore measurement by coding assistant

Common questions

Claude measurement questions

Need to discuss your team or technical setup? Contact us.

Can we monitor Claude costs in Oobeya?

Yes. AI Cost supports Claude, Codex, and GitHub Copilot. Review Claude spending in AI Impact → Cost & Credits alongside usage and engineering outcomes. Blamely attribution is a separate data source. Available financial measures depend on the connected source and reporting scope.

What does Oobeya help evaluate for Claude?

Oobeya supports Claude usage and cost monitoring alongside efficiency, review flow, churn, quality, and delivery. Blamely supplies code-attribution context separately for supported Claude coding workflows.

How is Claude-written code attributed?

Blamely records supported assistant edits locally and matches changed lines to their source at commit time. Attribution is stored in Git notes and can then provide code-origin context for engineering analysis.

Can Blamely work in an air-gapped environment?

Blamely attribution runs locally, including in air-gapped environments, without uploading source code to a cloud service. Connecting attribution to Oobeya is a separate integration step.

Can we investigate quality and rework?

Review AI code share alongside churn, coding efficiency, technical debt, and code quality findings. Inspect the work behind a trend before interpreting it as an assistant outcome.

Does this measure every Claude interaction?

This page concerns Claude coding workflows and the signals captured from them. Review the configured tools and source coverage to understand what is included in your analysis.

How should we interpret a delivery improvement?

Compare consistent scopes and periods, then account for task mix, team changes, and process improvements. A relationship between Claude contribution and delivery alone does not establish causation.

See Oobeya in practice

Review Claude usage, costs, and impact together

Walk through Claude cost monitoring, adoption, review flow, and quality with your rollout in mind.

Schedule a demo Contact us