# Analyze AI Assistant Spend
AI coding assistants like Claude Code, Claude Cowork, Gemini CLI, and Cursor have become a significant and often poorly tracked line item for engineering teams. Seats get purchased, adoption is assumed, and the actual picture - who's using what, how intensively, and whether the subscription is paying for itself - stays invisible until renewal time.
Revenium connects directly to all four and gives you that picture continuously: adoption by team and individual, cost against API-equivalent value, cache efficiency, and inactive seat identification. Find it under Intelligence > AI Assistants in your sidebar.
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### :route: Setup Paths (Enterprise vs Per-User)
If your question is "How do I set up Revenium to show AI assistant metrics?", use this split:
| Setup model | Use this when | Start here |
| ----------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Enterprise/workspace-wide setup** | You want organization-level visibility across teams and projects, with standardized attribution and governance. | [Connect Your Agent](/get-started/quickstart/connect-your-agent.md) -> [SDK Setup](/integrations/sdk-setup.md) and [Workspace Connections](/get-started/quickstart/workspace-connections.md) |
| **Per-user CLI onboarding** | You want an individual developer flow to onboard Claude/Cursor/Gemini quickly. | [Revvy CLI setup instructions](https://github.com/revenium/revenium-cli-node) |
The enterprise path is the recommended default for customer rollouts. The CLI path is still useful for fast individual adoption and testing.
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### :link: Connecting Your Assistants
A single package covers Claude Code, Cursor, and Gemini CLI. Get started with:\
npm install -g @revenium/cli
Each tool has its own interactive setup wizard that handles API key validation, connectivity testing, and shell profile configuration automatically. Full setup instructions for all three tools, including configuration options and backfill commands for importing historical data, are in the repository.
[**View Setup Instructions on GitHub →**](https://github.com/revenium/revenium-cli-node)
> 💡 Tip: During setup you'll be asked for your subscription tier. Revenium uses this to translate your flat subscription fee into a per-usage cost figure, which is what makes the API-Equivalent Value metric meaningful. It's worth getting right.
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### :dollar-sign: The Overview: What Are You Actually Getting for Your Money?
Once your tools are connected, the Overview pulls them together side by side — Active Users and Total Cost per tool for the period selected. The combination of those two numbers drives the conversation: a tool with high cost and low active users is a procurement issue. A tool with growing active users and strong session depth is one worth expanding.
The aggregate metrics across all connected tools sharpen that picture further:
* **Active Users** - total unique developers using any AI coding tool across your organization. The headline adoption number, distinct from the per-tool counts above.
* **Sessions per User** - average daily engagement per developer, a proxy for genuine adoption rather than installed-but-ignored.
* **Usage Trend** - period-over-period change in engagement, so you can see whether adoption is growing or quietly declining after the initial rollout.
* **API-Equivalent Value** - the estimated cost if the same usage had been delivered through direct API access. If this significantly exceeds your subscription cost, the tool is delivering real leverage. If it doesn't, that's worth understanding before renewal.
The **Usage by Organization/Department** table breaks all of this down by internal team — users, sessions, cost, and trend — for teams running internal chargebacks or simply wanting to know which departments are driving AI tooling investment.
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### :hammer: Per-Tool Dashboards
Each connected tool has its own detailed view, with the structure varying based on whether the tool is a fixed-seat subscription or a usage-based service.
#### Subscription-Based Tools (Claude Code, Cursor, Gemini CLI)
The per-user breakdown shows exactly who is using each tool, how many sessions they're running, how many tokens they're consuming, and what that's costing. This surfaces both ends of the distribution — the power users getting the most from the tool, and the inactive seat holders who haven't engaged since onboarding.
**Model Usage Distribution** shows which underlying models are handling requests across the team. For tools that route across multiple models, this tells you whether expensive reasoning models are being used where they add value, or whether usage patterns suggest a configuration adjustment could reduce costs without affecting output quality.
**Cache Utilization** is one of the clearest efficiency signals available. A high cache hit rate means the tool is reusing prior context efficiently rather than reprocessing the same tokens repeatedly. The **Estimated Savings** figure translates that into dollars — useful both for validating current efficiency and for projecting what a broader rollout would cost.
The subscription view surfaces the insight that vendor dashboards rarely volunteer: how many of your paid seats are actually active, and what the inactive ones are costing per month. Revenium flags these directly with the recoverable cost attached, so the decision to reclaim them ahead of renewal is straightforward rather than something that requires a manual audit.
#### Usage-Based Tools (Claude Cowork)
The Cowork dashboard focuses on per-user usage attribution rather than seat utilisation. The headline metrics — API-Equivalent Value, Total Requests, Average Duration, and Total Tokens — give you the size and shape of the workload, with period-over-period comparisons so you can see how usage is trending.
The Usage by Claude Cowork User table breaks the same data down per developer: how many requests each person made, what those requests cost, and how many tokens they consumed. Toggle between Totals (cumulative for the period) and Over Time (trend per user) to switch between "who's been the heaviest user" and "who's been ramping up or backing off." Value by User translates the per-user totals into API-equivalent figures, the same metric that appears on the Overview but scoped to individual subscribers rather than the tool as a whole.
Because Cowork is metered rather than seat-based, the Subscription Overview view that exists for the other three tools doesn't appear here — there's no inactive seat to reclaim, just usage to attribute and trend.
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### :forward: What to Do With What You Find
Use the **Adoption Trend** to time renewal conversations with evidence rather than recency bias. Use **Usage by Department** to make the case for expanding access to teams that aren't yet engaged, or to recover budget from teams where adoption hasn't materialised. Use **API-Equivalent Value** to build the ROI case for finance — showing not just what you spent, but what that spend delivered in terms of developer leverage.
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