Quality
See recurring patterns, breakdowns, and risk indicators behind coding-agent usage.
Observability for coding agents
See what coding agents are doing across your org, what they cost, and where quality and risk diverge.
Get one operational view across quality, cost, governance, and usage instead of stitching together vendor dashboards.
See the product through
Designed for engineering leaders who need operational clarity without micromanaging individuals.
Works across Claude, Codex, Cursor, Copilot, and more.
Org work
Quality score
74
Monthly spend
$18.4k
Policy aligned
87%
Signal mix
Last 30 daysBreak down recurring risks and stronger operating patterns by team, project, model, or agent.
Track spend concentration, spike days, and efficiency shifts before AI coding spend becomes opaque.
See permission modes, risky artifacts, and policy posture next to the rest of the operating picture.
Quality
Break down recurring risks and stronger operating patterns by team, project, model, or agent.
Cost
Track spend concentration, spike days, and efficiency shifts before AI coding spend becomes opaque.
Governance
See permission modes, risky artifacts, and policy posture next to the rest of the operating picture.
Quality signals by team, model, or project
See where process quality and anti-pattern rates diverge across the org.
Spend trends and top cost drivers
Understand what is driving AI coding spend instead of only seeing aggregate totals.
Governance posture in the same layer
Policy risk, permissions, and risky artifacts stay connected to actual usage.
Cross-tool visibility
Diff gives one operational surface across tools instead of siloed vendor analytics.
Quality
See recurring patterns, breakdowns, and risk indicators behind coding-agent usage.
Cost
Track spend, spikes, and concentration before AI coding budgets become opaque.
Governance
Understand permissions, risky artifacts, and policy posture alongside operational usage.
Cross-tool
Compare Claude, Codex, Cursor, Copilot, and more in one operating layer.
For engineers
Diff gives individual engineers a view into their coding-agent work across sessions, tools, and configurations so they can understand what is working and what is not.
Track personal session patterns over time instead of relying on memory.
See which workflows, tags, and habits recur across your work.
Understand the configuration and setup context behind the sessions.
Personal signals
Active days
19
Pattern tags
12
Work items
6
Signal mix
Last 30 daysSee what has been increasing, stabilizing, or falling off in your own work.
Sessions are more useful when viewed with their surrounding setup and process cues.
The product should make sense for an engineer before any org-wide rollout happens.
Personal trends
See what has been increasing, stabilizing, or falling off in your own work.
Workflow context
Sessions are more useful when viewed with their surrounding setup and process cues.
Useful before procurement
The product should make sense for an engineer before any org-wide rollout happens.
Personal signal, not just admin reporting
The engineer path stays useful even without an org dashboard purchase.
Pattern visibility
Use recurring tags and trends to understand how your sessions are evolving.
For managers
Diff gives engineering leaders a cross-tool operational layer for understanding how coding agents are being used, where outcomes diverge, and where intervention is needed.
Break down quality and risk by team, project, model, or agent.
Track spend and efficiency, not just usage or seat counts.
See governance posture next to the rest of the operating picture.
Org overview
Tracked teams
12
Top 3 concentration
58%
Risk exposure
9 sessions
Signal mix
Last 30 daysBring quality, cost, governance, and usage into one shared system instead of separate conversations.
Slice by project, team, model, or agent to find where operating differences matter.
Keep the product focused on system understanding and operational improvement.
Operational breadth
Bring quality, cost, governance, and usage into one shared system instead of separate conversations.
Actionable segmentation
Slice by project, team, model, or agent to find where operating differences matter.
Management without surveillance
Keep the product focused on system understanding and operational improvement.
Break down the org view
Leaders can inspect team, project, model, and agent differences without losing the top-line picture.
Usage plus posture
Adoption metrics become more meaningful when they sit beside cost and governance data.
Also Useful To
Understand AI coding spend, spike days, and cost concentration by team, model, and workflow.
See permission modes, risky artifacts, and policy-alignment signals alongside actual usage.
Normalize cross-tool telemetry and compare setup differences across teams and providers.
Personal proof
Diff can expose public-safe aggregate activity for engineers without exposing raw transcripts or sensitive project details.
Explore the engineer pathPublic profile
Aggregate activity only. No raw transcript exposure.
Sessions
148
Streak
12d
Trust
Diff is designed to help teams understand coding-agent operations without turning engineers into scorecards.
Individual-facing value
The product should be useful to engineers themselves, not only to management.
Controlled visibility
Cross-tool observability works best when access and visibility are intentional rather than indiscriminate.
Public-safe defaults
Where public or shared surfaces exist, they should default to aggregate-safe exposure rather than raw detail.
For Engineers
See how you work with coding agents across sessions, tools, and setup.
Start freeFor Managers
Understand quality, cost, governance, and cross-tool usage in one layer.
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