Our Story

Why we built Metaustral — and why it didn't exist before.

The problem we kept running into

Working with data teams of different sizes, we noticed the same problem repeating itself: teams were drowning in data they couldn't find, understand, or trust. Analysts spent hours tracking down the definition of a single KPI. Engineers documented tables in private Notion pages that no one else could find. New hires needed weeks to understand what data existed — and by then, the documentation was already outdated.

The tools that existed didn't fit

We looked at the available data catalog tools. The enterprise leaders — Collibra, Alation, IBM Watson Knowledge Catalog — were designed for 500-person data organizations with dedicated implementation teams and six-figure budgets. The open-source options (Apache Atlas, DataHub, Amundsen) required Hadoop clusters, Kafka infrastructure, or Elasticsearch stacks that a 5-person data team simply couldn't operate. The modern SaaS alternatives (Secoda, Atlan) were moving in the right direction but still priced and featured for mid-to-large organizations.

There was a clear gap: a simple, affordable, cloud-native data catalog for teams with 1–50 people working with data. That gap is what Metaustral was built to fill.

Built to be used on day one

From the first version, our design principle was: a data analyst should be able to sign up, connect their database, and have a working catalog in under one hour — without reading a manual or waiting for IT. Every feature decision has been made with that constraint in mind. No agents to deploy (unless you want on-premise connectivity). No YAML configuration files. No infrastructure to manage. Just sign up, connect, document.