Data Observability Platform and Tools That Catch Broken Data First
Data observability software that monitors freshness, volume, schema, and anomalies across your warehouse, maps lineage end to end, and alerts you in Slack the moment data breaks. Set up in 15 minutes.
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
Live console · pick a break, watch it get caught
Connects to your stack
Why teams switch
The self-serve, transparent data observability platform
Built for the lane the enterprise tools abandoned: connect in minutes, know your price, and catch breaks before they reach a stakeholder.
Know your price, start today
No six-figure contract and no contact-sales gate. Sign up, connect a warehouse, and see your monitors and your price the same afternoon.
$99 to start
Live across all 5 pillars in 15 minutes
We read your dbt manifest and auto-generate monitors for freshness, volume, schema, and anomalies. Lineage comes free from the same connection.
15 min to first alert
Alerts you will actually act on
ML-tuned thresholds and alert grouping kill the noise, and metadata-first checks keep your warehouse compute cost tiny.
1 incident, not 10 pings
The 5 pillars
Five pillars of data observability, monitored on every table
Freshness
Know the moment a table stops updating on schedule. SLA-based freshness monitors catch stalled syncs and paused pipelines before a stale dashboard does.
fresh 4m ago
Volume
Row counts that spike or collapse are the first sign of a broken load. Volume monitors learn the normal range and flag drops and surges automatically.
+0.4% vs forecast
Schema
A renamed column or a dropped field breaks everything downstream silently. Schema-change detection alerts you the instant a table structure shifts.
0 changes 24h
Anomalies
Distribution drift, null spikes, and out-of-range values caught by ML-tuned thresholds that learn each table and keep false alarms low.
nulls 0.1%
Lineage
End-to-end column-level lineage maps every table to the models, dashboards, and exports it feeds, so you see the blast radius of any incident at a glance.
247 tables mapped
How it works
From connected to catching breaks in four steps
Connect your warehouse
Read-only metadata access to Snowflake, BigQuery, Databricks, or Redshift. About 5 minutes.
Auto-generate monitors
We read your dbt manifest and create monitors across all 5 pillars on every table.
Get alerted
When freshness, volume, schema, or distribution breaks, you get a grouped alert in Slack or PagerDuty.
Track to resolution
Each break opens an incident with root-cause hints and downstream lineage impact.
See it catch a break
Watch a stale table get caught before finance sees it
This is the whole product story in one screen: a freshness SLA breaks, the trace spikes, the alert fires, and lineage shows the dashboards and exports it would have quietly corrupted.
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
“We caught a broken revenue table before finance ever opened the dashboard. The freshness alert hit Slack 40 minutes before the morning report. That paid for the year.”
Transparent pricing
Pricing you can read on the page
Every plan is paid and self-serve. Start with a 14-day free trial, no credit card. Transparency is the whole point.
Starter
Small data teams getting started
Team
Growing analytics and data-eng teams
Scale
Data platforms running at scale
Enterprise
Large orgs and custom deployments
FAQ
Questions data teams ask first
Data observability is the practice of continuously monitoring the health of your data and pipelines across five pillars: freshness, volume, schema, distribution (anomalies), and lineage. A data observability platform automatically detects when data arrives late, when row counts swing unexpectedly, when a schema changes, or when values drift, then alerts the data team and maps the downstream impact so problems get caught before stakeholders see broken numbers.
Dataobservability is transparently priced and self-serve: Starter is 99 dollars per month, Team is 299 dollars per month, and Scale is 799 dollars per month, billed yearly, with Enterprise priced on request. There is no six-figure contract and no contact-sales gate just to see a price. You can start with a 14-day free trial and no credit card.
Most teams are live in about 15 minutes. You connect your warehouse with read-only metadata access, and if you use dbt we auto-generate monitors from your manifest. All five pillars are active on day one, and lineage comes free from the same connection.
No. Dataobservability is metadata-first: most monitors read warehouse metadata and information-schema statistics rather than scanning full tables, so the compute footprint stays tiny. Heavier checks are sampled and scheduled, and you control the cadence per table.
Alerts are tuned to be acted on, not ignored. ML-based thresholds learn each tables normal behavior, related alerts are grouped into a single incident, and you set severities and routing per monitor. The result is fewer, higher-signal alerts in Slack or PagerDuty.
We connect with read-only access and are metadata-first, meaning we read metadata and statistics rather than copying your rows. Enterprise plans offer an in-VPC deployment so data never leaves your environment, plus SOC 2 controls, SSO, and an audit log.
Catch broken data before your stakeholders do
Connect your warehouse, auto-generate monitors across all five pillars, and get alerted the moment data breaks. 15-minute setup, transparent pricing, no credit card.
15 min setup · metadata-first · no credit card