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.
• Daily spend chart — Per-day spend bars with a 7-day moving average line to smooth out day-to-day noise.

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.

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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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), and the pricing basis the agent used.

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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.