How to build Statsig dashboards in Metabase

Statsig is a product experimentation platform combining feature gates, A/B testing, and product analytics on one event stream. Metabase is where you turn that delivery activity into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Statsig MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Statsig history into a database so you can build dashboards anyone can read.

How do you connect Statsig to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring delivery reporting to a warehouse-backed model.

What can you analyze from Statsig data in Metabase?

Once experiments and the related feature gates, Pulse results, gate changes data are in a database, the highest-value views are:

• Experiment velocity and outcomes — how many experiments launch, conclude, and reach a decision each month.
• Gate rollout status — which gates are mid-rollout, fully open, or stuck halfway.
• Stale gate cleanup — gates past their expected lifespan with no recent changes.
• Metric lifts by experiment — Pulse results landed as a table, comparable across experiments.
• Exposure volume by gate — how many users each gate and experiment actually touches.

Which Statsig dashboards should you build in Metabase?

How do you use the Statsig MCP Server with the Metabase CLI?

Pair the Statsig MCP Server with the Metabase CLI for fast, hands-on 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 the current experiments inventory with recent change history and rollout status.
• Export the result as CSV, keeping stable IDs, projects, states, and timestamps.
• 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.
• Audit-log change events are required for rollout velocity and change-vs-incident analysis.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "statsig": {
      "url": "https://api.statsig.com/v1/mcp"
    }
  }
}

Community npm packages with Statsig-flavored names predate the official server — use the endpoint from Statsig's own docs, not a lookalike package.

# 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 experiments export — creates a table AND a model
mb upload csv --file statsig-experiments.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file statsig-experiments.csv

Can you generate a Statsig dashboard with AI?

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

Create a polished Metabase dashboard for Statsig feature flag analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering leaders understand rollout activity, flag hygiene, experiment outcomes, and change safety from Statsig data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for statsig tables and
  models). If durable Statsig data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Statsig MCP Server:
  experiments, plus feature gates, Pulse results, gate changes.
  Prefer entity tables and rollups over raw logs. 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, projects,
branches, states, and timestamps, and whether history exists before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Statsig — it reads a database or CLI-uploaded tables.
- Never try to load raw build logs or event streams into Metabase; use runs,
  entities, state changes, and rollups.
- Only compute durations (cycle time, build duration, time to review) when the
  required timestamps exist.
- Exclude bot activity, draft work, and non-production environments from
  headline numbers, and keep the segmenting column explicit.
- 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.

Dashboard title: Statsig Feature Flag Overview

Sections:
1. Executive summary: Active flags; Changes last 30 days; Temporary flags
   over 90 days old; Experiments running; Experiments concluded last quarter.
2. Rollouts: Changes per week by environment; in-progress rollouts; time to
   full rollout.
3. Hygiene: Stale temporary flags; unevaluated flags; flags per project.
4. Experiments: Launched vs concluded; win/loss share; days to decision.
5. Change safety: Changes vs incidents timeline; changes followed by error
   spikes; off-hours changes.

Filters: Date range, Project or Repository, Branch or Environment, Team, Status.

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 Statsig data into a database or warehouse?

For dashboards that need history and reliability, land Statsig entities and change history in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the Statsig Console API for control over fields, grain, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Fivetran syncs Statsig via a webhook-based connector and Airbyte has a marketplace source. For experiment readouts, the Console API's Pulse report endpoints export lift results per experiment — land those as a results table next to your gate and experiment metadata.

Notes

• Land raw entity tables first, then build clean Metabase models on top.
• Sync state-change or audit history from day one — duration metrics can't be reconstructed later without it.
• Normalize project, environment, flag key, action, actor, and changed-at fields.

How should you model Statsig data in Metabase?

Core tables

Modeling advice

• Build a clean flag_changes model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (projects, repos, flags, members) from run-grain and event-grain tables.
• Exclude bots, drafts, and non-production environments from headline metrics; keep the flag as an explicit column.
• Use stable IDs for project, repo, and person joins; display names change.

Which Statsig metrics should you track in Metabase?

What SQL powers Statsig 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('week', changed_at) AS week,
  environment,
  COUNT(*) AS flag_changes
FROM flag_changes
WHERE changed_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY 1, 2
ORDER BY 1, 2;

SELECT
  key,
  project,
  created_at,
  EXTRACT(DAY FROM (CURRENT_TIMESTAMP - created_at)) AS age_days
FROM flags
WHERE temporary
  AND NOT archived
  AND created_at < CURRENT_DATE - INTERVAL '90 days'
ORDER BY created_at
LIMIT 50;

SELECT
  date_trunc('month', decision_made_at) AS month,
  COUNT(*) AS experiments_concluded,
  COUNT(*) FILTER (WHERE winning_variant IS NOT NULL) AS with_winner,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (decision_made_at - started_at)) / 86400
  ) AS median_days_to_decision
FROM experiments
WHERE decision_made_at IS NOT NULL
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing Statsig in Metabase?

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FAQ