Artie
@artie_labs
Power your AI with real-time data. Sub-minute replication to Snowflake, Databricks, Redshift, and more. No babysitting required.
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Standing up a production CDC pipeline used to mean designing source access, replication, backfills, destination writes, schema-change handling, monitoring, and recovery.
Now, it can done with a single prompt: “Create a pipeline from the staging Postgres database to Snowflake.”
With Artie MCP available in Claude Code, Cursor, and Codex, an AI agent can use Artie’s tools to configure and manage that workflow. So engineers can start with what they need, instead of spending months assembling and maintaining the pipeline themselves.
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We filmed three data engineers in their natural habitat.
Not shown in the doc:
> Monitoring a pipeline that was 'definitely fine' five minutes ago.
> Reading logs like they’re a crime scene.
> Doing recovery math while someone asks if the dashboard is up to date yet.
Moving data is easy. What’s difficult is keeping data correct and current, and knowing how to recover when pipelines restart, schemas change, or data falls behind.
That’s why Artie now has a Free plan: so anyone can run real-time replication without building and operating the infrastructure themselves.
Deploy a pipeline and watch your data replicate in real time.
Try it:
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We benchmarked Artie vs. AWS DMS on real-time CDC from Postgres to Snowflake, under sustained writes of ~22,400 events/sec for 10 minutes.
Midway through the test, DMS had already piled up 3.7GB of WAL backlog. By the end, it hit 6.1GB, growing faster in the second half. Meanwhile, Artie barely moved: 381MB to 385MB.
A CDC tool that falls behind doesn't just give you stale data. It can fill up disk on your production database and in some cases, take it down entirely.
Full breakdown👇
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