For data teams on Snowflake, BigQuery, Databricks and Redshift
Data observability that catches broken data before your stakeholders do
Monitor freshness, volume, schema, and anomalies across your warehouse, map lineage end to end, and get a Slack alert the moment data breaks. Read-only setup, no agent to install.
Plans from $59/mo billed yearly, half the monthly price. 14-day trial, no credit card.
- Warehouse access
- Read-only
- Typical setup
- 5 min
- Pillars on every table
- 5
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
Pick a break and 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.
From $59/mo
Live across all 5 pillars from one read-only connection
We read your dbt manifest and auto-generate monitors for freshness, volume, schema, and anomalies. Lineage comes free from the same connection.
No agent to deploy
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.
One incident, not ten 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
Lineage maps every table to the models, dashboards, and exports it feeds, so you see the blast radius of any incident at a glance. Column-level detail comes on Scale.
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.
Incidents
One incident with the root cause, not ten pings
Every break becomes a tracked incident with an owner, the upstream cause, and the dashboards and exports it would have quietly corrupted.
orders freshness SLA breached
47 min late against a 5-min SLA, alerted #data-eng in Slack 0.8s after the breach
- Owner
- @you
- Root cause
- Upstream Fivetran sync paused
- Downstream impact
- revenue_daily Looker: Revenue finance_export
- Status
- Sync resumed, backfill running
Transparent pricing
Pricing you can read on the page
Every plan is paid and self-serve. Your 14-day trial needs no credit card. Transparency is the whole point.
Pricing another data observability vendor?
We read each vendor's pricing page and marketplace listing at source, so you can hold a quote against a real number. The full side by side is in our data observability pricing comparison.
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 59 dollars per month, Team is 179 dollars per month, Scale is 479 dollars per month and Enterprise is 999 dollars per month, billed yearly (half the monthly price of 119, 359, 959 and 1,998 dollars). There is no six-figure contract and no contact-sales gate just to see a price. Every plan starts with a 14-day trial, no credit card required, and you can cancel anytime.
Most teams are live the same day. 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 table's 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 SSO and an audit log.
Vendor pricing we read at source
Catch broken data before your stakeholders do
Connect your warehouse, auto-generate monitors across all five pillars, and get alerted the moment data breaks. Read-only setup, transparent pricing, no credit card.
Read-only setup · metadata-first · no credit card