DocsRunData Dashboard

Data Dashboard

Total executions, success rate, token usage, and cost trends—all your account-level run data on one page.

The console's Run Analytics page rolls all runs in a time window into one dashboard: four KPI cards, two line charts for run and duration trends, a donut chart of run status distribution, and a top-workflows table sorted by run count. A morning glance answers "how many ran yesterday, did they succeed, and where did the tokens go".

Opening the Dashboard and Time Ranges

Find it in the console sidebar → Run Analytics. The top-right corner switches between three time ranges: 24 hours (aggregated hourly), 7 days, and 30 days (both aggregated daily). Counting rules: a run is included if its start time falls inside the window, and only runs the current account is allowed to see are counted.

Dashboard data has a short browser-side cache of about 30 seconds; switching time ranges renders instantly on a cache hit, and the refresh button in the top-right corner bypasses the cache to pull fresh data.

The Four KPI Cards

Indicator	Definition
Total Executions	Number of runs in the window — running, failed, and canceled all count
Success Rate	Runs with status COMPLETED ÷ total runs
Average Time Taken	Average duration of finished runs that have a recorded duration
Total token usage	Sum of tokens recorded in step events across all runs in the window (input + output); K is thousands, M is millions

Trend Charts and Status Distribution

Execution trend — a line of execution counts per time bucket (hour or day), for reading business cadence and sudden spikes
Average duration trend — the mean execution time (seconds) per time bucket; a slowly rising curve usually means an upstream API got slower or steps were added to the workflow
Execution status distribution — a donut chart of succeeded / failed / cancelled, for overall health

Top Workflow

The table at the bottom of the dashboard lists workflows by execution count, high to low. Each row shows execution count, success rate (with a colored progress bar), average duration, last run time, and token usage. To find where tokens are going or which workflow's success rate has dropped, start here.

How Costs Are Calculated

Each execution's cost is an estimate, computed from the token counts actually recorded in execution events:

text

成本 ≈ 输入 token × 输入单价 + 输出 token × 输出单价
（单价 = 美元 / 100 万 token，按模型查内置单价表）

Unit prices come from the platform's built-in price table, in USD per 1 million tokens, with input and output priced separately
The price table covers common models from Anthropic, OpenAI, Google, DeepSeek, Qwen, Mistral, and others, matching model names fuzzily
Cost can be broken down step by step: each step's input / output tokens and its estimated amount are listed separately

bash

# 单次执行的成本估算（含逐步骤拆分）
curl "https://braidrun.com/api/executions/<execution-id>/cost" \
  -H "Authorization: Bearer <token>"

json

{
  "executionId": "<execution-id>",
  "totalCostUsd": 0.0837,
  "modelName": "anthropic/claude-3.5-sonnet",
  "inputTokens": 12400,
  "outputTokens": 3100,
  "totalTokens": 15500,
  "stepCosts": [
    {"stepName": "fetch_data", "costUsd": 0.0, "inputTokens": 0, "outputTokens": 0},
    {"stepName": "daily_summary", "costUsd": 0.0837, "inputTokens": 12400, "outputTokens": 3100}
  ]
}

Treat Estimates As Estimates

The price table is built into the platform and may lag behind provider price changes; models not in the table are counted as 0
Under BYOK, actual charges land in your own provider account, and your provider's bill is the source of truth for reconciliation
The platform does not quote costs before a run — cost is only computed after execution, from the actual token records

Pull the Same Data via the API

The aggregated data behind the dashboard can also be requested directly, so you can wire it into your own reports or alerts: