Pricing
Data observability tools, transparently priced
Every plan is paid and self-serve. No six-figure contract, no contact-sales gate to see a price. Start with a 14-day free trial, no credit card.
Starter
Small data teams getting started
- 1 warehouse connection
- Up to 50 tables monitored
- All 5 pillars: freshness, volume, schema, anomalies, lineage
- Slack alerts
- 7-day metric history
Team
Growing analytics and data-eng teams
- 1 warehouse connection
- Up to 250 tables monitored
- dbt-native auto-monitors
- End-to-end lineage and ML anomaly detection
- Slack and PagerDuty alerts, 90-day history
Scale
Data platforms running at scale
- Multi-warehouse connections
- Up to 1,500 tables monitored
- Column-level lineage
- SSO and audit log
- Priority support
Enterprise
Large orgs and custom deployments
priced on request
- Unlimited tables monitored
- In-VPC deployment option
- SOC 2 and custom SLAs
- Custom integrations
- Dedicated support engineer
14-day free trial · no credit card · cancel anytime
Far below a six-figure contract
Enterprise data observability platforms are typically sold through sales-led, contract-based motions that start in the tens of thousands per year. Dataobservability gives you the same five pillars and end-to-end lineage, self-serve, starting at 99 dollars per month.
Pricing FAQ
What you get and what it costs
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.
Dataobservability connects to Snowflake, BigQuery, Databricks, and Redshift, is dbt-native, and reads orchestration signals from tools like Airflow and Fivetran. Alerts route to Slack and PagerDuty, and lineage spans your warehouse through to BI tools like Looker.