Anomalo AI
Anomalo / anomalo.com
AI data quality monitoring that automatically detects anomalies in data warehouse tables, providing proactive alerting before bad data reaches dashboards and business decisions.
Pricing
Free
Free plan
No
Category
Developer Tools
Platforms
2
Free plan
No
API access
No
Open source
No
Platforms
2
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What is Anomalo AI?
Anomalo uses ML to monitor data warehouse tables for quality issues automatically — learning normal patterns per table and alerting when row counts, null rates, value distributions, or key metrics deviate unexpectedly. Unlike Monte Carlo which also provides comprehensive lineage, Anomalo focuses specifically on the data quality monitoring layer with deep dbt and warehouse integration. Anomalo's check templates allow configuring table-specific quality rules on top of automatic anomaly detection, combining AI detection with custom validation. It integrates natively with dbt exposures so affected downstream dashboards are visible when quality issues are detected.
How Anomalo AI works
Anomalo AI runs as ml platform software built around data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on web and api.
Watch Anomalo AI in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating Anomalo AI.
Automatic ML detection
Learns table patterns and detects deviations in row counts, distributions, and null rates without manual threshold configuration.
dbt exposure integration
Shows which dbt models and downstream dashboards are affected by detected quality issues — providing immediate impact context for incident prioritisation.
Custom check templates
No-code templates for adding specific quality rules alongside AI detection for table-specific validation requirements.
Best use cases
Who should use it
Pros
- Warehouse-native approach with deep dbt integration fits modern data stack workflows naturally
- AI automatic detection without manual rule definition reduces initial setup time
- No-code rule templates enable data engineers to add custom checks alongside AI detection
- dbt exposure integration shows affected dashboards when quality issues are detected
Cons
- Enterprise-only pricing limits accessibility for smaller data teams
- Less comprehensive lineage than Monte Carlo — primarily detection rather than full observability
- Newer company with smaller community than established monitoring tools
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Is it worth the price?
Enterprise licensing. No public pricing. Contact for pricing. Free trial available.
Model
Enterprise
Starting price
Free
Free trial
Yes
Tools like Anomalo AI
Monte Carlo (rank 507) provides broader data observability with lineage. Great Expectations provides open source data quality testing. Bigeye is a competing data observability tool. dbt tests provide transformation-layer quality checks.
Anomalo AI vs Monte Carlo AI
A side-by-side look at the closest alternative in this category.
Anomalo AI
Anomalo
Monte Carlo AI
Monte Carlo
Enterprise licensing. No public pricing. Contact for pricing. Free trial available.
Enterprise licensing. No public pricing. Contact for pricing. 14-day trial.
SOC 2 Type II. GDPR compliant. Enterprise data handling agreements.
SOC 2 Type II. ISO 27001. GDPR compliant. Enterprise data handling agreements.
Warehouse metadata and statistics accessed for monitoring. Row-level data is not stored — anomaly detection uses statistics and samples. Review Anomalo's data handling policy.
Monte Carlo accesses data warehouse metadata and statistics (not row-level data) for monitoring. Review Monte Carlo's data handling policy for metadata access patterns.
- Warehouse-native approach with deep dbt integration fits modern data stack workflows naturally
- AI automatic detection without manual rule definition reduces initial setup time
- No-code rule templates enable data engineers to add custom checks alongside AI detection
- dbt exposure integration shows affected dashboards when quality issues are detected
- Pioneered the data observability category — largest mindshare and ecosystem in the space
- ML auto-learned baselines eliminate the manual rule-writing that makes traditional data quality testing brittle
- Table and field-level lineage accelerates root cause analysis from hours to minutes for data incidents
- Fox, JetBlue, and PagerDuty references validate across diverse enterprise verticals
- Enterprise-only pricing limits accessibility for smaller data teams
- Less comprehensive lineage than Monte Carlo — primarily detection rather than full observability
- Newer company with smaller community than established monitoring tools
- Enterprise-only pricing requires data quality programme investment
- Monitoring without lineage is less valuable — initial setup requires comprehensive lineage connection
- Competing with dbt tests, Great Expectations, and Anomalo for data quality monitoring
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
Anomalo
Platforms
Web, API
Deployment
SaaS
Integrations
Snowflake, BigQuery, Databricks, Redshift, dbt, Slack, PagerDuty, API
Team Collaboration
No
Launch Year
2023
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
SOC 2 Type II. GDPR compliant. Enterprise data handling agreements.
Warehouse metadata and statistics accessed for monitoring. Row-level data is not stored — anomaly detection uses statistics and samples. Review Anomalo's data handling policy.
What users are saying
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Common questions about Anomalo AI
Editorial Verdict
Should you use Anomalo AI?
Anomalo is an excellent AI data quality monitoring platform for modern data stack teams using dbt who want automatic anomaly detection with easy custom rule layering and dashboard impact visibility.
Last verified July 24, 2026.