AI Assistants
AI Assistants brings connected assistant usage and cost into one place under Intelligence > AI Assistants. Review spend by department and person, then open each assistant tab for its own usage and cost detail.
Connect your assistants
Cursor and GitHub Copilot use organization-wide provider connections under Integrations > Providers. Connect your provider first: see Connecting Providers. See Setup Cursor and Setup GitHub Copilot. For telemetry setup, see Setup Claude Code, Setup Claude Cowork, Setup Gemini CLI, and Setup Codex CLI.
For individual CLI onboarding and historical backfill, use the Revenium CLI. Claude Code and Gemini setup validate the Revenium key, test the connection and try to update your shell profile; Codex setup checks the key format and configures telemetry, and revenium-codex test checks the connection. For an organization-wide Claude Code rollout, an administrator configures telemetry once in managed settings, as described in Setup Claude Code.
If a developer's Claude Code usage is missing or looks too low, see Troubleshooting.
Choose a pricing mode
Fixed seats and subscriptions and API-priced traffic have different cost meaning. Configure the distinction per team so Revenium knows whether assistant traffic stays in AI Assistant analytics as API-equivalent estimates or counts as real spend across cost reports. See Configure AI Assistant Pricing Mode.
Overview
The Overview shows person-attributed AI spend, the number of departments and people, and dollars per million tokens for the period you choose. Below the headline figures are Daily AI Spend, Spend by AI Source, Spend Mix and Spend by Department & Source, then department comparisons (top spending, highest cost rate, movers and spend concentration), Shadow Spend by Department and the Departments table. Until departments are set up, the Overview shows a Set up departments notice with the per-person view below it.
By Employee
The per-tool dashboards each scope to a single assistant. The By Employee tab pivots the same data around people instead, rolling every connected assistant (Claude Code, Claude Cowork, Cursor, Gemini, Codex, Copilot) and direct API spend up to the individual. Where a per-tool view answers "what did Cursor cost," By Employee answers "what did any one person cost across everything": the view you need for chargebacks and for spotting concentrated spend. It needs no extra setup; it draws on the assistants you've already connected.
Start with the Attribution Coverage figure at the top. It tells you how much of your identifiable AI spend has been tied to a known person, and it's the trust indicator for every number below: if coverage is low, the per-employee figures describe only the attributed slice, not your whole AI spend.
From there the page surfaces:
Headline KPIs: Attributed AI Cost, Active Employees, Avg Cost / Employee, Blended $/Mtok (a single efficiency number for the whole population), and the split between assistant and direct-API spend.
Callouts for spend concentration and cost-rate outliers, when the data supports them.
Charts — daily spend trend, spend by AI source, spend mix, and how cost is distributed across employees. The daily spend trend can also be broken out by vendor.
All Employees table: per-person cost, % of total, tokens and $/Mtok, requests, channel, and last active, exportable to CSV for finance review.
The Shadow Spend — Account Types view splits spend into Corporate and Personal. That split is a heuristic based on a configurable corporate-domain allowlist, not a verified employee directory. Read it, and the page as a whole, as a cost-governance and coverage tool: it exists to answer "what are we spending, on whom, and how much of it can we see", not to monitor individuals.
Select a bar in a Shadow Spend chart, on By Employee or on the AI Assistants Overview, to open the people behind it.
Filter the view
Subscription-based assistant usage is included by default; use the page filters to exclude it when you want to, and to focus on the provider data and model tiers relevant to the analysis. Filters narrow the current result set before you group, choose columns, or export it.
Compare groupings and team benchmarks
Group the table by employee, tool, model, model tier, or model maker. The table adds team-average cost-per-million-token context so an individual result can be compared with the wider team rather than read in isolation.
Choose columns and export the result
Select the columns you need for the current analysis. Exporting to CSV uses the current result set, so apply the required filters and grouping before creating the file.
Set a budget for an employee
Select Set a budget from an employee's detail to open a budget alert with that person prefilled. Review the form and submit it before the rule is created; opening the form does not save it automatically.
For the full rule workflow, see Guardrails.
Tickets
Tickets shows what AI work costs for each engineering ticket, most of it from coding assistants, so you can use past ticket costs when planning similar work. See Tickets.
PR Health
PR Health shows which pull requests need a decision, which were abandoned, and how much coding-assistant spend sits behind them. See PR Health.
Claude Code
The Claude Code tab shows spend, merged pull requests, people, findings, and seat and plan costs. See Claude Code Dashboard for how to read those measures.
Claude Cowork
The Cowork dashboard has Overview and People tabs. On Overview, review what changed, findings, spend and sessions over time, spend by department or person, and cost by model; use People to inspect individual usage. See Claude Cowork Dashboard for how to read the results.
Cursor
The Cursor tab shows usage and costs synced from its team API. See Setup Cursor for how to connect it and verify the first sync.
Gemini
The Gemini tab shows Gemini CLI usage and costs from telemetry. See Setup Gemini CLI for configuration and connection checks.
Codex
The Codex tab shows Codex CLI usage and costs from telemetry. See Setup Codex CLI for configuration, a test metric, and backfill.
Copilot
The Copilot tab shows GitHub Copilot usage and costs, with Cost by Model for supported dates. See Setup GitHub Copilot for the provider connection and refresh timing.
Skills
Assistant cost tells you what a tool is spending. Skills tells you what it is spending it on, breaking usage down by the skills your assistants actually invoke rather than by the assistant as a whole. It is the view to reach for when a cost figure is understood but the behavior behind it is not.
Filter the view by person to scope it to one developer or a small group, which turns a team-wide distribution into a specific question about specific work. Filters are held in the page address, so the view you are looking at is the view you get back when you reload it, and the link you paste into a thread opens for a colleague exactly as it looked for you.
Spend in skill sessions puts the rest of the page in proportion. It is the spend from sessions where a skill ran, shown with the number of those sessions against the total for the workspace. A workspace where skills touch a small share of sessions reads very differently from one where they touch most of them, and this figure tells you which one you have. If a filter narrows the skills counted but not the sessions they are compared against, the page says so above the figure.
The invocation mix separates the two reasons a run has no named trigger. Inside another skill means one skill called another, which is ordinary nesting. Not matched means the run has no start record, so the telemetry did not arrive. It does not mean nothing happened.
Exports cover every skill matching your filters, in the sort order on screen, rather than only the rows already loaded into the page. The page itself explains how spend is attributed to skills. Skill cost is currently an approximation: use it to compare skills and to see where spend concentrates, not as an exact amount. Skill-labelled spend counts only the calls made in the turn that started a skill, so work that continues in later turns or is handed to a subagent is left out and the labelled figure usually undercounts a skill. Spend in skill sessions is usually the upper bound, so the real cost usually sits between the two; a skill call that arrives without a session can make the labelled figure the larger one. Open How we attribute spend to skills on the page for the full explanation.
What to Do With What You Find
Use the Departments table on Overview to see which departments drive AI spend. On By Employee, group by department to see the API-equivalent column alongside department cost when subscription-based assistant usage is included.
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