Dashboard sections

The AI Spend Agent dashboard is organized into sections you navigate from the left rail. Each section answers a different question: how much are you spending, where is it going, what’s wrong, what to do about it, and what’s been recovered.

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Overview

Overview is the headline view: how much your team is spending on AI and how that’s trending.

• 30-day spend — Total cost across Anthropic and OpenAI over the last 30 days.
• Tokens (30d) — Total tokens consumed, split between input and output.
• Cache-read rate — Share of input tokens served from a prompt cache, plus the raw read vs. written token counts. High cache-read rates mean you’re paying less for repeated context.
• Models tracked — How many distinct models the agent has seen usage from.
• Price / 1M tokens — Blended cost across input and output tokens in the window, expressed per million tokens. Useful as a headline unit price you can compare across periods or after switching models.
• Cost / request — Average cost per provider-reported request. The average is taken only over rows the provider reports a request count for, so the cost basis is the request-reporting spend rather than total spend. Anthropic’s usage API is token-only, so if all your usage is Anthropic this shows — with provider does not report request counts.
• Daily spend chart — Per-day spend bars with a 7-day moving average line to smooth out day-to-day noise.

Above the KPIs, a provider tab strip (“All providers” plus one tab per connected provider that has usage) scopes every metric on the page to that provider. Only providers with usage get a tab — a connected provider that hasn’t produced any spend yet won’t appear. Overview is the fastest way to spot a spend spike or a sudden drop in cache efficiency.

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Usage

Usage decomposes the same 30 days by dimension. Each section shows a spend-mix donut alongside a ranked table.

• Spend by model — Every model that produced cost in the window, with input, output, cache-write and cache-read tokens per model plus each model’s share of total spend.
• Spend by provider — The same breakdown grouped by Anthropic vs. OpenAI.
• Spend by API key — Which keys are driving cost, useful for isolating a runaway integration.
• Spend by team member — Cost attributed to each team member, ranked. Click a row to open that member’s drill-down page — see Team member drill-down.

Use Usage when Overview surfaces a change and you need to attribute it: which model got more expensive, which provider is driving the shift, and whether cache usage moved along with it.

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Cascading filters

The filter bar above the breakdowns is cascading: each dropdown only shows values that are still possible given the ones above it. Pick a provider and the Model dropdown narrows to that provider’s models; pick a model and API Key narrows to keys that used it; and so on down through Team Member. If an upstream change makes a downstream selection impossible (for example, switching to Anthropic while an OpenAI model is picked), the downstream dropdown resets to All on its own.

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Team member drill-down

From the Spend by team member breakdown, click a row to open a per-member view for the last 90 days. It shows:

• Spend, Tokens, Cache-read rate, Requests — The member’s headline numbers, with team-median comparisons on cache-read rate and team-p90 comparisons on tokens per request so you can see whether they’re an outlier or in line with the rest of the team.
• Model mix — A donut of which models the member’s spend went to.
• Recommendations — Per-member optimization suggestions with an estimated annual saving where the agent can quantify one.
• Findings for this member — Every open finding attributed to that member’s email, so you can see anomalies and violations tied to their usage in one place.

The member view is a drill-down of the same data, not a separate audit — findings match what appears elsewhere in the dashboard.

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Anomalies

Anomalies collect behavioral and efficiency findings the agent produced from your usage. Each row is one finding, tied to a specific rule (e.g. spend_spike_anomaly, cache_efficiency_outlier, model_overkill, stale_key_active). Categories covered here:

• Usage patterns — Spend spikes, off-hours usage.
• Governance — Stale keys, untagged keys, workspace concentration, subscription-cheaper opportunities.
• Cache efficiency — Cache-write overspend, cache-read opportunities, cache-efficiency outliers.
• Model efficiency — Over-powered model choices, low-output request bursts, tokens-per-request outliers.

You can filter findings by severity (high / medium / low), date range, and category. Click any row to expand it — you’ll see the human-readable explanation, the attribution (model, key, workspace, or user where available), the pricing basis the agent used, and a Confidence score (0–100%) for rules that produce one. Confidence is separate from severity: a low-confidence high-severity finding is worth double-checking before acting; a high-confidence low-severity finding is usually safe to treat as real.

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Violations

Violations are the disputable findings: billing and pricing discrepancies where the provider’s charge doesn’t match what pricing says it should have been. These findings can back a refund claim in Recovery. Categories covered here:

• Billing — Billing discrepancies (point-in-time and windowed), rate & discount summaries.
• Pricing data — Stale pricing entries, unknown-model observations.

Violations use the same filters and expand-for-detail interaction as Anomalies. Each finding is categorized into either Anomalies or Violations — a single finding never appears in both.

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Action Plan

The Action Plan is your prioritized to-do list. It takes every finding — anomalies and violations — and ranks them by estimated dollar impact, biggest first. At the top of the page you’ll see the total identified opportunity: the sum of estimated impact across every open finding. Each item shows the finding title, its severity, the human explanation, and its individual impact. Work top-down to pick up the highest-value fixes first.

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Recovery

Recovery is the home for refund claims filed against billing violations. See Recovery and refund claims for the full walkthrough.