Amazon Redshift vs BigQuery: which is better for analytics at scale?

At large or unpredictable scale, BigQuery's serverless pricing usually costs less to run than a Redshift cluster sized for peak load, because BigQuery charges per query instead of per hour of reserved compute. On-demand BigQuery pricing is $6.25 per terabyte scanned, with the first 1 TB scanned each month free; Redshift's RA3 nodes bill continuously whether a query runs or not, though Redshift Serverless narrows that gap by billing per RPU-hour instead.

How do Redshift and BigQuery differ on pricing and scaling?

When does Redshift work better at scale?

Redshift tends to cost less for steady, predictable workloads, since a provisioned cluster running near capacity all day is cheaper per query than paying per terabyte scanned on every request. Teams that already store data in S3 and use other AWS services get Redshift Spectrum and IAM-based access control in the same account.

When does BigQuery work better at scale?

BigQuery tends to work out better for bursty or unpredictable query patterns, since there is no cluster to size or resize before a spike in usage. Because storage and compute are fully separate, a team can scan a much larger dataset during a demand spike without provisioning new nodes in advance, and idle time costs nothing beyond storage.

Which platform fits which kind of scale?

Teams with steady, forecastable analytical load and an existing AWS footprint tend to get more predictable costs from Redshift, especially with RA3's separate storage billing. Teams facing variable or spiky query volume, or teams already standardized on Google Cloud, tend to get more headroom from BigQuery's per-query pricing and automatic scaling.