Google BigQuery vs Amazon Redshift
Compare BigQuery and Redshift by query or capacity billing, serverless and provisioned scope, data location and recurring free access versus trial credit.
Which buying model fits the workload?
Compare BigQuery and Redshift using the same datasets, query mix, freshness and concurrency requirement. BigQuery offers on-demand query processing and capacity-based editions. Redshift offers serverless and provisioned warehouse models. Existing cloud data location can change the integration work, but it does not establish a measured cost or speed winner. Include ingestion, storage, transfer and operational controls in the evaluation.
Seven differences that change the shortlist
| Buying question | Google BigQuery | Amazon Redshift |
|---|---|---|
| Deployment scope | Managed analytical platform | Serverless or provisioned analytical warehouse |
| Primary compute choices | On-demand bytes processed or capacity-based editions | Serverless RPU usage or provisioned capacity |
| Storage budget | Stored data with applicable storage model | Warehouse storage and applicable supporting services |
| Free access | Recurring usage limits and a restricted no-card sandbox | Eligible first-time Serverless credit trial |
| Sandbox distinction | Lifetime sandbox storage limit and automatic object expiration | Trial credit is independent of general AWS Free Tier |
| Data dependencies | Assess Google Cloud and external source paths | Assess AWS and external source paths |
| Acceptance unit | Same query mix, freshness and concurrency target | Same query mix, freshness and concurrency target |
Four worked buying briefs
These are hypothetical scenarios with explicit assumptions. They do not establish equal capabilities, measured usability or a negotiated price.
| Requirement | Proof to collect | Acceptance condition |
|---|---|---|
| Query workload | Versioned query set and dataset shape | Both evaluations answer the same business questions |
| Freshness | A load schedule and completion evidence to collect | Required data is available before the reporting deadline |
| Cost controls | A plan for query limits, capacity and usage ownership | Unexpected consumption has an investigation path |
| Retention | Documented data expiration and recovery requirements | Evaluation data is not mistaken for protected production storage |
Intermittent analyst queries
What does a quiet month and a burst month cost?
Google BigQuery: Estimate bytes processed under on-demand billing and compare capacity if relevant.
Amazon Redshift: Estimate the selected serverless or provisioned workload configuration.
Include repeated scans, loading and retained storage rather than comparing one successful query.
Daily executive reports
Can ingestion finish and reports refresh within the required window?
Google BigQuery: Map load jobs, transformations and analytical consumption.
Amazon Redshift: Map the corresponding warehouse load and query workflow.
Use reconciled business totals and an explicit freshness deadline.
Data already in one cloud
Does moving data change the economics or permissions?
Google BigQuery: Trace source access, transfer and BigQuery dataset location.
Amazon Redshift: Trace source access, transfer and Redshift deployment location.
Estimate initial migration, daily change volume and recovery reload separately.
No-card learning project
Will a free environment keep the data needed for the exercise?
Google BigQuery: Account for sandbox lifetime storage and 60-day object expiration.
Amazon Redshift: Check eligibility and the finite Serverless trial credit.
A learning route should not become an undocumented production retention policy.
Compare the same business questions and data shape
Row count alone is an inadequate warehouse brief. Record column widths, partitions, update patterns, query frequency and simultaneous users. Include a representative refresh and an unexpected backfill so the team can see which assumptions affect capacity or query consumption.
Keep any proposed performance exercise reproducible: version inputs and queries, state whether caches are involved, and retain the configuration. This page provides buying criteria rather than claiming that a hypothetical workload has already been measured.
Read sandbox limits separately from recurring allowances
BigQuery’s billed-account free usage and its no-card sandbox are different boundaries. The current sandbox documentation specifies a lifetime storage quota and expiring objects, alongside a monthly query allowance. It is useful for exploration, but the owner must explicitly plan retention and any move to a billed project.
Redshift’s eligible Serverless trial uses a finite credit balance and is independent of the AWS-wide Free Tier. Prepare the post-trial estimate before adoption. On either platform, a free initial run says little about monthly ingestion, repeated query use or retained data.
Questions before you choose
Is BigQuery always billed per query scan?
No. On-demand processing is one model; capacity-based editions are also available. Compare the model chosen for the actual workload.
Does Redshift Serverless mean no capacity cost?
No. Redshift Serverless uses billable compute resources measured through RPUs, with other applicable costs such as storage.
Is BigQuery sandbox storage a fresh 10 GiB every month?
No. The current sandbox documentation states a lifetime 10 GiB storage limit that is not refunded on deletion. The billed-account free tier has separate recurring storage terms.
Is Redshift trial credit the same as the AWS Free Tier?
No. AWS states that Redshift Serverless trial eligibility is assessed independently of the general AWS Free Tier programme.
Turn the comparison into your own evaluation
Use the same criteria and priorities for both products, keep unknowns unassessed and record the plan and evidence behind your ratings.
Build a Google BigQuery vs Amazon Redshift scorecard →Keep the assumptions with the figures
Use the date, billing cycle, units and limits with each example. Check current account terms before purchasing. Unverified prices and model costs stay unpriced.
Download the buying examples as JSON ↓Share the comparison
Use these examples when discussing the shortlist with your team.