# Anomalo

> Machine-learned monitoring that can run inside your own cloud

Anomalo is a commercial data quality platform that applies machine learning to detect anomalies without hand-written rules, and can be deployed either as SaaS or entirely inside the customer's own VPC so that data never leaves their environment.

Website: https://www.anomalo.com

## Use it when

- Coverage must arrive before anyone writes rules: unsupervised models learn each table's shape and report availability, freshness, schema, and distribution deviations.
- Data cannot leave your environment: full in-VPC deployment, optionally with your own models instead of Anomalo's, is what puts it on regulated shortlists.
- Non-engineers author checks: validation rules are written in natural language rather than SQL.
- Bad batches must be stoppable: shipped Airflow operators can gate a workflow on checks passing.

## Think twice when

- You need lineage from the quality tool itself; Anomalo integrates with catalogs rather than computing its own.
- Budget planning needs a public price; nothing is published.
- The team prefers declared, versioned checks in code; Great Expectations and Soda start from that end.

## How it runs

SaaS, or entirely inside your own VPC. It connects to seventeen warehouses and lakes (Snowflake, Databricks, BigQuery, and Iceberg among them), feeds eight catalogs including Alation, Atlan, and Purview, and alerts through Slack, Teams, PagerDuty, and webhooks, with findings written as explanations of what changed rather than bare threshold breaches.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Commercial |
| Deployment | Self-hosted, Managed |
| Workload | Batch |
| Operational complexity | Medium |
| Pricing | Subscription |
| Documentation | https://docs.anomalo.com |

### Data Quality

| Attribute | Value |
| --- | --- |
| Authoring model | Mostly nothing: point it at a table and unsupervised monitoring begins without a rule being written. Above that, validation rules are authored in natural language rather than SQL, and metrics are declared for the business numbers a team already watches |
| Detection model | Unsupervised learning over the data itself rather than only its metadata, it learns the shape of each table and reports deviations in availability, freshness, schema and distribution, with declared validation rules and metric monitoring layered on where a team knows exactly what to assert. Enterprises can substitute their own models for Anomalo's |
| Built-in monitors | Table observability across freshness, volume, schema and distribution, applied automatically to every table; data quality checks learned per column; validation rules written in natural language; and metrics watching business KPIs for unusual movement |
| Connectors | Seventeen warehouses and lakes (Snowflake, Databricks, BigQuery, Azure, Redshift, Athena, Presto, Oracle, PostgreSQL, SQL Server, AlloyDB, Teradata, MySQL, SAP HANA, SingleStore, DB2 and Iceberg) beside eight catalogs including Alation, Atlan, DataHub, Purview, Unity Catalog and Collibra, four BI tools, and Airflow, dbt, Azure Data Factory and Databricks Workflows on the pipeline side |
| What you run | SaaS, or fully inside the customer's own VPC, the deployment that puts it on regulated shortlists, since data never has to leave the environment, and enterprises can bring their own models rather than run Anomalo's |
| Prevention or detection | Detection first with a real gate available, two Airflow operators ship, one running the checks for a table and one taking a list of checks that must pass and stopping the workflow when they do not, so bad records can be quarantined before downstream consumers reach them |
| Lineage | None of its own: it integrates with eight catalogs including Alation, Atlan, DataHub and Purview rather than computing lineage, and the agent that would trace downstream impact is announced rather than shipped |
| Alerts and delivery | Slack, Microsoft Teams, PagerDuty, Opsgenie, email and webhooks, carrying findings written as an explanation of what changed and why rather than a bare threshold breach |
| Incidents and ownership | Jira and ServiceNow for ticketing, with findings delivered as explanations rather than alerts; the first-responder agent that would assess criticality and follow a runbook is announced and not yet shipped |
| Billing unit | Nothing published: there is no pricing page at all, so neither a price nor a unit can be recorded. The same answer Monte Carlo gives one row over |

## Capabilities

- [Data Quality](https://matca.io/capabilities/data-quality)

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Source: https://matca.io/anomalo
Last updated: 2026-08-31T11:40:47.716Z