Anomalo
ML anomaly detection for Snowflake, BigQuery and Databricks
Overview
Anomalo scans warehouse tables in Snowflake, BigQuery and Databricks for anomalies, schema drift and broken pipelines using machine learning instead of hand-written rules, plus newer unstructured-text and LLM-response checks. Customers include Notion, Discover, Block and BuzzFeed.
Use cases
What you can produce with Anomalo
- No-code ML anomaly and schema-drift detection
- Root-cause analysis for data quality alerts
- Unstructured-text and LLM-output quality checks
- Airflow provider for pipeline integration
- Terraform provider for monitor-as-code setup
- Gemini CLI / MCP extension for coding agents
- Used by Notion, Discover, Block, BuzzFeed
ASEAN Perspective
Anomalo in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
The pitch is real: point it at a warehouse table and Anomalo's ML models learn what normal looks like, then flag the anomaly instead of waiting for someone to write a dbt test for every edge case. Root-cause tooling and the newer unstructured-text checks (built for reviewing free-text fields and LLM outputs) extend that further than most warehouse-only competitors go.
Reviewers on G2 rate it 4.4/5 across 42 reviews and consistently cite fast setup and low false-positive rates once tuned — the "once tuned" part matters, since a few flag alert noise before that happens. Pricing is quote-only and scales with table coverage, so expect a real sales conversation rather than a self-serve signup. Snowflake Ventures and Databricks Ventures have both invested, which tracks with who actually buys this: mid-to-large teams already running a modern warehouse stack, not startups patching together their first pipeline.
What people say
A $33M Series B landed in 2026, led by SignalFire with Databricks Ventures and existing backers Norwest Venture Partners, Two Sigma Ventures and Foundation Capital joining — pushing total funding to $72M. Snowflake Ventures invested directly too, which lines up with how the product is actually used: most deployments sit on top of Snowflake, BigQuery or Databricks. Customers publicly named include Discover Financial Services, Notion, Block (Square), BuzzFeed, Substack and Marsh McLennan.
The core product replaces hand-written data tests with ML models that learn a table's normal behavior and flag drift, volume anomalies, schema changes and freshness problems without engineers writing rules for every column. Anomalo has extended into unstructured data quality too, scanning free-text fields and LLM outputs for the same kind of drift, plus a Gemini CLI extension that connects the platform to coding agents over MCP. Integrations include an official Airflow provider and a Terraform provider maintained partly by Square, one of its customers. Discover's own case study describes the motivation plainly: a data platform team supporting hundreds of banking and payments applications producing petabytes of data needed a way to scale quality monitoring past what manual testing could cover, and turned to ML for that instead of adding headcount.
On G2, Anomalo holds a 4.4/5 rating across 42 reviews — 69% five-star, 28% four-star, essentially no one-star reviews. Reviewers repeatedly cite quick time-to-value and confidence in dashboards that previously needed manual spot-checks; a Notion engineer specifically credited the integrations, response speed and interface for winning the account. The recurring complaint isn't detection quality, it's alert volume before monitors get tuned, plus a pricing structure G2 reviewers describe as non-transparent — there's no published rate card, only quote-based deals that scale with the number of tables covered.
Customization for edge-case rules trails purpose-built testing frameworks like Great Expectations, and there's no small-team tier. This is built and priced for organizations already running a real warehouse at scale.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Anomalo's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Anomalo unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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