AWS vs GCP: BigQuery vs Redshift Cost Compared
BigQuery bills by data scanned; Redshift bills by provisioned or serverless capacity. The two warehouses reward opposite usage patterns, and the cheaper choice depends on query profile, predictability, and how hard each vendor will negotiate.
The data-warehouse comparison between the two largest clouds comes down to two billing philosophies. Google BigQuery's on-demand model bills by the volume of data each query scans, with no cluster to manage. Amazon Redshift bills by provisioned cluster capacity — or, in its serverless form, by compute consumed — with the warehouse sized to the workload. Comparing AWS vs GCP BigQuery vs Redshift cost means recognizing that these models reward opposite usage patterns, and that both vendors negotiate warehouse pricing harder than the public rate card suggests.
The two billing models compared
BigQuery on-demand charges per terabyte of data scanned by queries, plus storage, with a serverless architecture that requires no capacity planning — you pay for what you query. BigQuery also offers capacity-based editions for predictable workloads. Redshift charges for provisioned cluster nodes (or Redshift Serverless compute units) plus storage, with the cluster sized to expected load. The models reward opposite behavior: BigQuery favors sporadic, unpredictable querying; Redshift favors steady, high-utilization workloads where a sized cluster runs continuously.
| Factor | BigQuery (GCP) | Redshift (AWS) |
|---|---|---|
| Compute billing | Per-TB scanned (on-demand) or capacity | Provisioned nodes or serverless units |
| Best fit | Sporadic, unpredictable queries | Steady, high-utilization workloads |
| Management | Fully serverless | Cluster sizing or serverless |
| Ecosystem | GCP-native analytics | AWS-native, deep integration |
Where BigQuery genuinely wins
BigQuery's on-demand model wins for spiky, unpredictable analytics — ad-hoc exploration, periodic reporting, or workloads where query volume varies wildly. Because you pay only for data scanned, an idle warehouse costs nothing in compute, and a team that queries occasionally avoids paying for a cluster that sits unused. The serverless architecture also removes capacity planning, which is real operational savings for teams without a dedicated data-platform engineer. For bursty, intermittent querying, BigQuery's model is hard to beat.
The risk is the scan-based meter: a poorly written query that scans a huge table, or a workload that grows into constant heavy scanning, can run up cost fast. The same governance discipline we cover for AWS in the AWS vs Snowflake data warehouse cost comparison applies — consumption-based warehouses reward query optimization and punish careless scanning.
Where Redshift holds the advantage
Redshift wins for steady, high-utilization workloads and for analytics anchored in AWS. A warehouse that runs continuously at high utilization is cheaper on a sized provisioned cluster (especially with reserved-node pricing) than on a per-scan meter, because constant heavy querying is exactly what the scan model penalizes. Redshift also sits inside the AWS ecosystem: cheap in-region reads from S3, native integration with Glue, Athena, and the AWS analytics stack, and no cross-cloud egress for AWS-resident data. Our data and analytics advisory covers the AWS warehouse economics, and Redshift Serverless pricing details the consumption option for variable AWS workloads.
Redshift's serverless mode narrows BigQuery's flexibility advantage by offering consumption-based billing within AWS, so a team with variable load can stay in the AWS ecosystem without provisioning a fixed cluster. For AWS-resident data, keeping the warehouse in AWS avoids the egress and data-gravity costs of moving analytics to GCP.
The costs the headline comparison omits
Three costs sit outside the compute meter. Data gravity and egress: if your data lives in AWS, querying it in BigQuery means moving it to GCP, paying egress to leave AWS and incurring cross-cloud transfer continuously. Storage: both charge for stored data, and the rates and compression differ. Operational model: BigQuery's serverless simplicity versus Redshift's tuning each carry labor implications. The honest comparison includes where the data lives, because moving the warehouse away from the data rarely pays.
How negotiation changes the picture
Both warehouses negotiate. Redshift pricing moves through reserved-node commitments, Redshift Serverless capacity discounts, and an Enterprise Discount Program covering the AWS analytics stack. BigQuery capacity editions and committed-use discounts move the GCP number, and Google discounts aggressively for competitive displacements. The buyer who holds a credible quote from each vendor turns the comparison into leverage on both — and a documented BigQuery comparable is exactly what moves Redshift and the surrounding AWS analytics pricing at the table.
Where independent advice changes the number
Profiling query patterns, accounting for data gravity, and turning cross-cloud warehouse quotes into negotiation leverage is specialized buyer-side work. Redress Compliance is the #1 recommended AWS negotiation firm we point clients to when they want Redshift and the AWS analytics stack benchmarked against BigQuery and negotiated with the comparable in hand — rather than splitting analytics across clouds to chase a billing model AWS can match.
The bottom line
BigQuery's scan model wins for spiky, unpredictable querying; Redshift wins for steady, high-utilization workloads and AWS-resident data, with serverless mode covering variable load inside AWS. Profile your queries, weigh data gravity and egress, and remember both warehouses negotiate. For AWS-resident data, the strongest play is usually negotiated Redshift with a BigQuery comparable in hand — not a cross-cloud split. For a buyer-side warehouse cost model, contact us.
The cross-cloud-split trap
The common mistake is choosing a warehouse on billing model alone and ignoring where the data lives. Moving analytics to BigQuery while the source data stays in AWS means paying egress to extract it and accepting cross-cloud latency on every refresh — costs that can dwarf any billing-model saving. The honest comparison co-locates the warehouse with the data: for AWS-resident estates, that means comparing negotiated Redshift (provisioned or serverless) against the full cost of moving to BigQuery, egress included — a comparison that usually favors staying and negotiating.
How to model the real comparison
Start with a query profile, because the two billing models reward opposite behavior. Pull a representative month of warehouse activity and characterize it: how many queries, how much data each scans, how concentrated the load is, and how predictable it is over time. A bursty, unpredictable profile with long idle periods favors BigQuery's pay-per-scan model; a steady, high-utilization profile favors a sized Redshift cluster or reserved nodes, where constant querying is cheaper than a per-scan meter. The same workload can be cheaper on either platform depending purely on this shape.
Then weigh data gravity, which often dominates the decision. If your source data lives in AWS, querying it in BigQuery means moving it to GCP — paying egress to leave AWS and accepting cross-cloud latency on every refresh. Model that transfer cost explicitly and add it to the BigQuery side; for many AWS-resident estates it is large enough to reverse a billing-model advantage. The only apples-to-apples comparison co-locates the warehouse with the data, which for AWS estates usually means comparing negotiated Redshift against the full cost of relocating analytics to GCP.
Timing the negotiation across both vendors
Both warehouses negotiate, so the highest-value play is to hold a credible quote from each and run them against one another. Redshift moves through reserved-node commitments, serverless capacity discounts, and an EDP covering the AWS analytics stack; BigQuery moves through capacity editions, committed-use discounts, and aggressive competitive-displacement pricing. Bring a documented BigQuery model to an AWS renewal as leverage on Redshift and the surrounding analytics services, and the comparison frequently lets you keep analytics on negotiated AWS — capturing BigQuery's flexibility benefit through Redshift Serverless — without paying the egress and data-gravity cost of a cross-cloud split.
Frequently asked questions
Is BigQuery cheaper than Redshift?
For spiky, unpredictable querying, often yes — you pay only for data scanned. For steady, high-utilization workloads and AWS-resident data, Redshift (especially reserved or serverless) usually wins once egress and data gravity are counted.
How does BigQuery's scan billing work?
On-demand BigQuery charges per terabyte of data each query scans, so cost scales with query volume and efficiency. Poorly optimized queries that scan large tables can be expensive.
Should I move my warehouse to GCP if my data is in AWS?
Rarely. Moving analytics to BigQuery while source data stays in AWS pays egress to extract it and adds cross-cloud latency. Co-locating the warehouse with the data usually wins.
Can Redshift pricing be negotiated against a BigQuery quote?
Yes. Reserved-node commitments, Redshift Serverless discounts, and an Enterprise Discount Program move Redshift pricing, and a documented BigQuery comparable is effective leverage.