The analysis in the blog is based on two key assumptions:
- Multi-zone deployment on AWS
- Tiered storage is not enabled
If you’re looking to estimate costs with tiered storage, you can ignore the differences in storage costs mentioned in the post.
One important point not covered in the blog is that Ursa compacts data directly into a Lakehouse (This is also the major differentiator from WarpStream). This means you maintain only a single copy of data, shared between both streaming reads and table queries. This significantly reduces costs related to:
- Managing and maintaining connectors
- Duplicated data across streaming and Lakehouse systems
> Redpanda recently introduced leader pinning, but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.
Redpanda has leadership pinning (producers) and follower fetching (consumers). I suspect a significant amount of cost is improper shaping of traffic.
With follower fetching you shouldn't have cross-AZ charges on read, only on replication. In 15 seconds of looking at this piece I cut out $360/hour...no offense but this reeks of bad faith benchmarketing...
https://streamnative.io/blog/how-we-run-a-5-gb-s-kafka-workl...
And the test result was verified by Databricks: https://www.linkedin.com/posts/kramasamy_incredible-streamna...
The analysis in the blog is based on two key assumptions:
- Multi-zone deployment on AWS - Tiered storage is not enabled
If you’re looking to estimate costs with tiered storage, you can ignore the differences in storage costs mentioned in the post.
One important point not covered in the blog is that Ursa compacts data directly into a Lakehouse (This is also the major differentiator from WarpStream). This means you maintain only a single copy of data, shared between both streaming reads and table queries. This significantly reduces costs related to:
- Managing and maintaining connectors - Duplicated data across streaming and Lakehouse systems