Data observability · Anomalo

Anomalo

Data quality monitoring platform using unsupervised ML to detect anomalies without manually written rules, deployable in-VPC.

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Anomalo applies unsupervised machine learning to profile tables and automatically detect unusual patterns in freshness, volume, schema and value distributions, reducing the need to hand-author rules for every column. It provides no-code workflows for teams that do want to define specific checks, plus lineage-aware root cause analysis to help pinpoint which upstream job or table caused an issue. Notifications route through Slack, email or PagerDuty, and it includes checks aimed at validating data feeding LLM and AI pipelines, not just BI dashboards. A key differentiator is deployment: Anomalo can run inside a customer's own VPC or cloud account so data never has to leave the customer's environment, which appeals to security- and compliance-sensitive organizations that would otherwise avoid a fully hosted SaaS observability tool.

At a glance

Vendor Anomalo
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Security-conscious enterprises wanting deep anomaly detection without data leaving their own environment.

Pricing

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Features

  • Unsupervised ML anomaly detection
  • No-code rule authoring
  • Lineage-aware root cause analysis
  • Data quality dashboards
  • Slack/email/PagerDuty notifications
  • In-VPC or private cloud deployment
  • Data validation for AI/LLM pipelines
  • Scheduled quality checks

Integrations

Profile last reviewed September 21, 2026

Alternatives

Monte Carlo Data observability platform using ML-based anomaly detection to catch broken pipelines before they reach dashboards. Bigeye Data observability platform that auto-generates quality metrics and anomaly alerts across warehouses and lakes. Sifflet End-to-end data observability platform combining anomaly detection, lineage and governance controls for data and AI assets.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Data validation Checking that data meets explicit, predefined rules for type, range, format, or consistency before it is used or trusted. Diagnostic analytics Analytics focused on explaining why something happened by drilling into patterns, correlations, and root causes behind results. Root cause analysis (RCA) A structured process for tracing a problem back through its contributing factors to the underlying cause, not just its symptoms.

Related guides

How to choose a data observability tool Pick a data observability tool by how it generates coverage, where it needs to run relative to your data, and whether you're already living inside dbt.