Metric Hive review
A low-cost route to modeled marketing data, with daily refresh and paid export destinations.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Metric Hive connects marketing, analytics, commerce, payment, CRM, and operational sources, then maps imported fields into governed datasets for reporting. It is suited to teams that want a shared model for cross-source data and exports to dashboards, spreadsheets, BI tools, or warehouses. Its web-based, cloud deployment and stated fit across small, mid-market, and enterprise teams give it a broad organizational scope, while the core proposition centers on marketing data workflows.
The pipeline includes more than ingestion: Metric Hive supports derived metrics, validations, export-ready tables, historical backfills, and freshness checks. Monitoring covers source freshness, failures, schema drift, and export status, while source and account context is preserved for provenance. A semantic layer defines shared entities, metrics, grains, and field rules. These capabilities suit teams that need modeled outputs and visibility into data quality; the listed transformation depth is basic, so teams with complex transformation requirements may prefer a more specialized tool.
Pricing is usage-based and billed monthly. Each data source costs $1.25 per source per month, including storage, daily refresh, and historical data. Destinations are priced separately: Google Sheets is $30 per month for up to 10 active exports and 2 scheduled runs per day; Looker Studio is $50 per month with unlimited report views. PostgreSQL and BigQuery exports each cost $99 per month, Snowflake costs $249, and Redshift costs $299; each warehouse plan includes all standard tables for enabled products. With daily refresh, destination charges, and email support, Metric Hive fits teams prioritizing economical modeled exports over frequent updates or highly customized transformation.
Metric Hive pros and cons
- Where it wins
- Normalizes provider fields into shared dimensions, metrics, dates, and identifiers
- Supports historical backfills and monitors freshness, failures, and schema drift
- Exports modeled datasets to warehouses, spreadsheets, BI tools, and dashboards
- Where it doesn't
- Daily refresh may not suit teams that need more frequent updates
- Export destinations add monthly costs beyond per-source charges
- Google Sheets export is limited to 10 active exports and 2 scheduled runs daily
Metric Hive fact sheet, pricing and score →
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