Metaplane

Data observability for modern data teams. beamstart.com

RevOps Consulting
★★★★★ 4.9
2019 Founded
11-50 Employees
140+ (as of January 2023; current total not publicly disclosed) Customers
Boston, Massachusetts, United States Headquarters

Quick Facts

✓ Free Trial
Starting Price
10
Pricing Model
usage-based
Free Trial
14 days
Company Type
acquired

About Metaplane

Metaplane is an end-to-end, self-serve data observability platform that helps data and RevOps teams trust the data powering their business. Often described as the "Datadog for data", it continuously monitors metrics, metadata, lineage, and logs across warehouses, transformation tools, and BI platforms so teams are the first to know when something breaks—not the last. Data teams at companies like Bose, ClickUp, Klaviyo, Ramp, Sigma, CarGurus and GoFundMe use Metaplane to catch data quality issues before executives see broken revenue dashboards or customers experience downstream issues. beamstart.com

Founded in 2019 in Boston, Massachusetts by MIT graduate Kevin Hu and former HubSpot engineers Peter Casinelli and Guru Mahendran, Metaplane went through Y Combinator’s Winter 2020 batch and quickly established itself as a leader in the emerging data observability category. The company raised an $8.4M seed round followed by a $13.8M Series A and a subsequent investment from Snowflake Ventures, bringing disclosed funding to just over $23M from backers including Felicis, Khosla Ventures, Flybridge and Y Combinator. In April 2025 Datadog acquired Metaplane, which now operates as “Metaplane by Datadog” while continuing to offer its standalone product. ycombinator.com

Product-wise, Metaplane automatically baselines table- and column-level behavior (such as volume, schema, freshness, null rates and statistical distributions) and uses machine learning–based anomaly detection to surface incidents with minimal manual rule-writing. It offers schema change alerts, end-to-end column-level lineage from sources through the warehouse into BI and reverse ETL tools, data CI/CD with impact and test previews in GitHub/GitLab and dbt, job monitoring for dbt and Airflow, query and warehouse spend monitoring, and granular alert routing to Slack, Microsoft Teams, email, PagerDuty, APIs and webhooks. Teams can deploy Metaplane as a SaaS application or as a Snowflake Native App that keeps monitoring logic inside their data cloud and can even be paid for using Snowflake credits. metaplane.dev

Metaplane positions itself as a powerful yet lightweight alternative to heavier enterprise observability suites, emphasizing fast time-to-value (connect a warehouse in under 10 minutes and monitor an entire stack in under 30 minutes), flexible usage-based pricing, and a free forever tier. Independent review platforms consistently rate it highly—Metaplane holds a 4.8/5 rating from 116 reviews on G2 and a 5.0/5 rating from 23 reviews on Capterra—earning particular praise for ease of setup, integration depth, and responsive support. For organizations running modern cloud warehouses and BI tools, especially those whose RevOps, finance, and product teams rely on trusted metrics, Metaplane has become a go-to choice for operationalizing data quality. docs.metaplane.dev

Key Features

ML-based anomaly detection on table and column metrics (volume, schema, freshness, nullness, uniqueness, distributions) so teams don't have to hand-write data quality rules. metaplane.dev

End-to-end column-level lineage that traces data from sources and ELT tools through the warehouse into BI dashboards and reverse ETL destinations for precise impact analysis. metaplane.dev

Data CI/CD with impact and test previews, integrating with GitHub, GitLab and dbt so you can catch breaking changes in pull requests before they reach production. metaplane.dev

Automated schema change detection and alerts when databases, schemas, tables or columns are added, removed or modified. metaplane.dev

Job monitoring for dbt and Airflow to catch failed, delayed or long-running jobs and understand their downstream impact. docs.metaplane.dev

Data Insights that show table and column usage, query patterns and cost optimization opportunities to reduce data debt and warehouse spend. metaplane.dev

Warehouse spend monitoring add-ons that track Snowflake and other warehouse credits and costs alongside data quality signals. metaplane.dev

Suggested monitors and monitor forecasts that automatically prioritize high-value tables and reduce alert noise. metaplane.dev

Flexible alert routing to Slack, Microsoft Teams, email, PagerDuty, Jira, APIs and webhooks, with model feedback loops directly from collaboration tools. docs.metaplane.dev

Snowflake Native App deployment option that keeps monitoring logic and metadata inside Snowflake and allows payment via Snowflake credits. businesswire.com

Read-only metadata access plus SOC 2-audited architecture and GDPR/HIPAA-aligned processing to minimize data risk while monitoring mission-critical pipelines. metaplane.dev

Chrome extension, CLI and integrations with notebooks/BI tools to bring observability context directly into analysts' and RevOps stakeholders' workflows. docs.metaplane.dev

Self-serve onboarding and configuration so new teams can connect warehouses and start receiving meaningful alerts in minutes rather than weeks. beamstart.com

Pros & Cons

👍 Pros

  • Very fast, low-friction setup (often under an hour) with minimal ongoing maintenance, allowing data teams to start getting value quickly. docs.metaplane.dev
  • Powerful ML-based anomaly detection that automatically learns expected ranges for metrics like row counts, freshness and distributions, reducing the need for manual data quality rules. metaplane.dev
  • End-to-end column-level lineage and impact analysis that make it easy to trace issues across warehouses, transformations, BI dashboards and reverse ETL feeds. metaplane.dev
  • Highly responsive, hands-on customer support and success teams; reviewers frequently praise Metaplane's onboarding help and openness to product feedback. capterra.com
  • Tight integrations with the modern data stack (Snowflake, BigQuery, Redshift, Databricks, dbt, Fivetran, Airbyte, Looker, Tableau, Sigma, Power BI, Slack, PagerDuty and more) that fit naturally into existing workflows. metaplane.dev

👎 Cons

  • Alerting can be noisy at first until monitors and thresholds are tuned; several reviewers mention notification fatigue during early rollout or when sensitivity is high. g2.com
  • Role-based access control and user management are relatively basic, making it harder to safely expose Metaplane broadly to non-technical business users without granting admin-level access. capterra.com
  • Some advanced features and UI components can feel immature or occasionally buggy when first released, reflecting a fast-moving product; however, users note the team generally iterates quickly on feedback. capterra.com
  • A few reviewers would like deeper customization of alerts, dashboards and lineage views compared with heavier enterprise observability suites. g2.com

User Reviews

G2
4.8
★★★★★
Capterra
5.0
★★★★★
TrustRadius
unknown
☆☆☆☆☆

Integrations

Snowflake BigQuery Amazon Redshift Databricks ClickHouse AWS S3 PostgreSQL MySQL Microsoft SQL Server Apache Iceberg Fivetran Airbyte Segment dbt Cloud dbt Core Airflow Looker Tableau Mode Metabase Sigma Hex Power BI Slack Microsoft Teams PagerDuty Jira Google Chat

Best For

Company Size

smb mid-market enterprise

Industries

SaaS Technology E-commerce Financial Services Healthcare Consumer Goods

Use Cases

Proactive monitoring of revenue dashboards, funnel reports and pipeline metrics so RevOps teams catch data issues before leadership does. Monitoring ETL/ELT pipelines and data warehouse tables for anomalies in volume, freshness, schema changes and null rates. End-to-end lineage and impact analysis to understand which downstream dashboards, models and business systems are affected by upstream changes. Data CI/CD and regression testing for analytics code changes in dbt and version control systems such as GitHub and GitLab. Warehouse spend and query performance optimization based on usage analytics and cost insights. Establishing and enforcing data quality SLAs for analytics, self-serve BI and AI/ML workloads.
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