Why are “all-in-one” analytics tools so expensive?

Most “all-in-one” analytics tools look expensive for a reason that has nothing to do with servers.
Infrastructure is cheap. Interpretation is not.

This new Dashboard issue walks through a modern data stack end to end and puts real numbers on each layer. The punchline is simple: the $2,000 you pay each month often maps to roughly $20 in infra and the rest in packaging, opinion, and handholding.

Inside the breakdown:

• The four layers that move your data: collection, storage, modeling, and visualization

• Realistic costs by layer at a 1M events per month scale

• Why is storage cheap, but compute is where the bill lives

• The boxed tradeoff you accept with Hyros or Triple Whale

• When a bundled tool is rational, when a modular stack wins

• A starter map: Airbyte or Meltano for extraction, BigQuery or ClickHouse for storage, dbt for modeling, Lightdash or Metabase for readouts

• The part most teams skip: model definitions that match how your business creates value

You’ll learn:

• What “$20 of infra, $1,980 of interpretation” really means

• How to spot vendor opinion embedded in your metrics

• A practical path to encode your own thinking into your measurement

👉 Read the full piece here.

Have a great week!
The VidTao Team

PS — The Dashboard is where Brat shares weekly notes on building Bratrax, buying media, and making sense of performance data - without the fluff.

PPS — Send this link to a friend who needs it: blog.bratrax.com