Bruin is an open-source data platform that replaces separate ingestion, transformation, orchestration, and quality tools with one system. You define SQL, Python, and R pipelines as plain files in Git, pull data from 70+ sources, and run checks and column-level lineage in the same DAG. Bruin Cloud adds an AI data analyst that answers plain-English questions in Slack, Teams, and other channels, plus AI dashboards and scheduled agents. Built for data teams of any size.

• All-in-one data pipelines: Ingestion, transformation, orchestration, and quality checks run in one tool. You stop stitching together separate loaders, modeling tools, and schedulers.
• SQL, Python, and R in one DAG: All three languages are first-class and share lineage and tests. Python runs in isolated environments through uv, so dependencies stay clean.
• Built-in data ingestion: Pull data from databases, SaaS apps, files, APIs, webhooks, and event streams using the open-source ingestr loader. Sources include Postgres, Stripe, Shopify, HubSpot, Salesforce, Google Ads, and Meta Ads.
• Data quality checks: Define checks like not-null and uniqueness right next to your assets. Failing checks can block downstream steps, so bad data doesn't reach your reports.
• Column-level lineage and governance: See how each column flows through your pipelines. A built-in catalog, audit logs, and access controls help you track ownership and changes.
• AI data analyst: Ask questions in plain English from Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email, or the browser. You get the answer along with the SQL query behind it.
• AI dashboards and scheduled agents: Build dashboards and data apps from a prompt. Scheduled agents send recurring briefs, alerts, and digests, and can take actions like pinging data owners.
• Git-native, self-healing pipelines: Pipelines live as plain files in your repo, so review and rollback follow your normal workflow. The managed platform can re-run and backfill failed pipelines.

• Free, open-source core: The Bruin CLI is Apache 2.0 licensed with no time limit or feature paywall on the engine. You can self-host it, including on-prem and air-gapped setups.
• Fewer tools to manage: One platform covers ingestion, transformation, orchestration, and quality. That means fewer vendors, contracts, and integrations to maintain.
• Pay only for what runs: The Team plan has no seats and no minimum spend. Compute is billed per second, and idle time isn't charged.
• Wide warehouse support: Bruin works with 30 data platforms, including Snowflake, BigQuery, Databricks, Redshift, ClickHouse, DuckDB, and Microsoft Fabric.
• Easier migration: Bruin can read existing dbt, Airflow, LookML, Dagster, and Prefect assets. Migration tooling helps you move over without starting from scratch.
• Security credentials in place: Bruin lists ISO/IEC 27001:2022 certification and SOC 2 Type 2 attestation. It also states that your raw data is not used to train models.

• Some features require Bruin cloud: The free, open-source CLI doesn't include the AI Data Analyst, AI dashboards, data apps, scheduled agents, or managed pipeline scheduling. These features are available through Bruin Cloud, which offers a free starting allowance before usage-based charges apply.
• Enterprise controls sit on a custom tier: SSO/SAML, bring-your-own LLM credentials, BYOC, and data residency options are only on the Scale plan. Teams with strict security needs will have to contact sales for pricing.
• Code-first setup: Pipelines are defined as SQL, Python, and YAML files in Git. Business users can query data through the AI analyst, but someone with data engineering skills needs to set up and maintain the pipelines.

• Replacing a fragmented data stack: A startup running Airbyte, dbt, and Airflow side by side moves everything into Bruin. The team manages one set of pipeline files in Git and cuts down on tool overhead.
• Self-serve analytics for business teams: A marketing manager asks in Slack how last week's ad spend compares to revenue. The AI analyst replies with the number and the SQL it used, so the data team can check the logic.
• Automated daily briefings: An ops lead sets up a scheduled agent that sends a morning digest of key metrics. It also alerts data owners when a pipeline fails or numbers look off.
• Catching bad data before it spreads: A data engineer adds not-null and uniqueness checks to core tables. When a check fails, downstream models stop running, so broken data never reaches dashboards.
• Consolidating SaaS and database data: An e-commerce team pulls Shopify, Stripe, and Google Ads data into BigQuery with built-in ingestion. They join it with SQL and Python models to track customer lifetime value.
• Managing data in controlled environments: Businesses with strict infrastructure or data residency requirements can self-host Bruin's open-source CLI on their own infrastructure. This gives them control over where pipelines run and data is processed, while retaining access to pipeline orchestration, lineage, and quality checks.