How to build Vantage cost dashboards in Metabase

Vantage is a FinOps platform that normalizes costs from AWS, Azure, Google Cloud, Kubernetes, and dozens of SaaS providers into unified cost reports, with virtual tagging and savings recommendations. Metabase is where you turn that billing data into shared, trustworthy cost dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live cost data with the Vantage MCP and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Vantage cost data in a database so you can build dashboards anyone can read.

How do you connect Vantage to Metabase?

Most teams combine both routes: quick answers through MCP and CLI uploads first, then recurring cost reporting on a warehouse-backed model.

What can you analyze from Vantage data in Metabase?

• Multi-cloud spend overview — built from cost report rows and the related cost reports, providers and integrations, virtual tags data your sync exposes.
• Cost by team from virtual tags — built from cost report rows and the related cost reports, providers and integrations, virtual tags data your sync exposes.
• Provider cost comparison — built from cost report rows and the related cost reports, providers and integrations, virtual tags data your sync exposes.
• Savings recommendations and realized savings — built from cost report rows and the related cost reports, providers and integrations, virtual tags data your sync exposes.
• Forecast vs. actual spend — built from cost report rows and the related cost reports, providers and integrations, virtual tags data your sync exposes.

Which Vantage cost dashboards should you build in Metabase?

How do you use the Vantage MCP with the Metabase CLI?

Pair the Vantage MCP with the Metabase CLI for fast, hands-on cost analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for cost report rows for the trailing 90 days grouped by provider and team.
• Export the result as CSV, keeping stable IDs, accounts, services, tags, and dates.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Daily cost rows across several months are required for growth trends and forecast-vs-actual views.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Vantage MCP and the Metabase CLI?

{
  "mcpServers": {
    "vantage": {
      "url": "https://mcp.vantage.sh/mcp"
    }
  }
}

80+ tools, including the general-purpose query-costs tool that accepts VQL for filtered, grouped cost queries. A self-hosted variant exists for API-key auth: npx -y vantage-mcp-server with a VANTAGE_TOKEN.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a cost-report-rows export — creates a table AND a model
mb upload csv --file vantage-cost-report-rows.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file vantage-cost-report-rows.csv

Can you generate a Vantage dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Vantage MCP and the Metabase CLI. It works end to end: if Vantage cost tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for Vantage multi-cloud cost analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering, platform, and finance leaders understand normalized multi-cloud spend, team allocation, savings recommendations, and forecast accuracy from Vantage data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for vantage tables and
  models). If durable Vantage cost data is already present — a billing export,
  warehouse sync, or earlier upload — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Vantage MCP:
  cost report rows, plus cost reports, providers and integrations, virtual tags.
  Prefer daily rollups by service, account, team, or namespace over raw line
  items. Write each result to a CSV, then load it with the Metabase CLI — run
  "mb upload csv --file <export>.csv" so each upload creates a table and a
  ready-to-query model. Use "mb upload replace <table-id> --file <export>.csv"
  to refresh an existing table instead of creating duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, accounts,
services, tags or labels, and date ranges before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Vantage — it reads a database or CLI-uploaded tables.
- State whether costs are amortized, unblended, or net of credits — and never
  mix conventions in one card.
- A single CSV is a point-in-time snapshot: only build trend cards if there is
  a usable date column or multiple periods have been uploaded.
- Exclude or clearly separate credits, refunds, and taxes from usage cost.
- Only compute unit economics (cost per customer) when a customer-level
  dimension or tag actually exists in the data.

Dashboard title: Vantage Multi-Cloud Cost Overview

Sections:
1. Executive summary: Normalized spend this month; MoM growth; Effective
   savings rate; Unallocated spend; Forecast variance.
2. Multi-cloud: Spend by provider by month; provider share; top services.
3. Teams: Cost by team; teams over budget; untagged spend.
4. Savings: Recommendations by category; potential vs. realized savings.
5. Forecast: Forecast vs. actual; budget burn; biggest movers.

Filters: Date range, Provider or Account, Service, Team, Environment, Tag.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync Vantage data into a database or warehouse?

For dashboards that need history and reliability, land Vantage cost data in a database first, then connect Metabase to that database.

Connector options

• Billing export or managed ETL — use the vendor's native export or a connector when one covers the objects you need.
• Custom pipeline — use the Vantage API (VQL) for control over rollup grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync cost reports, providers, and recommendations with the Airbyte Vantage source (full-refresh — schedule accordingly), or script the Vantage API with VQL queries for exactly the cost slices your models need.

Notes

• Decide the rollup grain first (daily per service, account, and team) — it drives warehouse cost and every trend card.
• Land raw billing tables first, then build clean Metabase models on top.
• Normalize provider, account, service, team, accrual-date, and normalized-cost fields.

How should you model Vantage data in Metabase?

Core tables

Modeling advice

• Build a clean cost_daily_rollups model with common columns across clouds and tools, so multi-source dashboards don't fork definitions.
• Keep amortized, unblended, and net-of-credits cost as separate, labeled columns — never let one card silently mix them.
• Treat tags and labels as slowly changing: they apply from when they were added, and historical spend stays untagged.
• Separate usage cost from credits, refunds, taxes, and support fees in the model layer, not in each card.

Which Vantage cost metrics should you track in Metabase?

What SQL powers Vantage cost dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  date_trunc('month', accrued_at) AS month,
  provider,
  ROUND(SUM(amount_usd), 2) AS cost
FROM vantage_costs
GROUP BY 1, 2
ORDER BY 1, cost DESC;

SELECT
  category,
  COUNT(*) AS open_recommendations,
  ROUND(SUM(potential_savings_usd), 2) AS potential_monthly_savings
FROM vantage_recommendations
WHERE status = 'open'
GROUP BY category
ORDER BY potential_monthly_savings DESC;

WITH monthly AS (
  SELECT
    date_trunc('month', accrued_at) AS month,
    provider,
    service,
    SUM(amount_usd) AS cost
  FROM vantage_costs
  GROUP BY 1, 2, 3
)
SELECT
  month,
  provider,
  service,
  cost,
  ROUND(
    100.0 * (cost - LAG(cost) OVER (
      PARTITION BY provider, service ORDER BY month
    ))
    / NULLIF(LAG(cost) OVER (
      PARTITION BY provider, service ORDER BY month
    ), 0), 1
  ) AS mom_growth_pct
FROM monthly
ORDER BY month DESC, cost DESC;

What are common mistakes when analyzing Vantage in Metabase?

Related

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FAQ