Last updated: 28 September 2026. Pricing mechanics verified against AWS and Google Cloud documentation on that date. This article replaces a 2019 comparison at the same address.
Quick answer:
Amazon Redshift and Google BigQuery are both mature cloud data warehouses, and in 2026 the choice between them is mostly about where the rest of your stack lives and which pricing meter you’d rather manage. Redshift bills for provisioned capacity (node-hours, or RPU-hours on Serverless) and rewards steady, predictable workloads on AWS. BigQuery bills for what you scan (or for reserved slots) and rewards spiky, event-heavy analytics on Google Cloud. Neither is “faster” in a way that survives contact with a real workload.
The short version
| Amazon Redshift | Google BigQuery | |
|---|---|---|
| Model | Provisioned clusters (RA3) or Serverless | Serverless, always |
| Compute pricing | Node-hours, or RPU-hours on Serverless | Per TB scanned (on-demand) or per slot-hour (reservations) |
| Storage pricing | Managed storage per GB-month on RA3 | Per GB-month, logical or physical billing per dataset |
| Scaling | Resize or add nodes; Serverless scales inside a limit you set | Automatic; slots scale with the query |
| Open table formats | Iceberg via Redshift Spectrum and Lake Formation | Iceberg via BigLake and BigQuery tables for Apache Iceberg |
| Streaming ingest | Streaming ingestion from Kinesis and MSK | Storage Write API, Pub/Sub subscriptions |
| ML in the warehouse | Redshift ML (SageMaker under the hood) | BigQuery ML, Vertex AI integration, native vector search |
| Best home | AWS-first companies with steady throughput | Google Cloud shops, GA4 users, event analytics |
Architecture: clusters versus a query service
Redshift started life in 2013 as a cluster you sized and paid for. RA3 nodes later separated storage from compute, so your data sits in managed storage on S3 and the nodes are pure compute. Redshift Serverless (2022) removed the sizing step: you set a base capacity in RPUs and a spend cap, and the service scales inside that box.
BigQuery never had a cluster. It’s a query service on top of Google’s Dremel and Colossus infrastructure: you submit SQL, Google allocates slots, and you pay for the bytes read or the slots you reserved. There is no machine to size, resize, pause or forget to turn off. That last point is why BigQuery bills are rarely surprising for the reason Snowflake or Redshift bills are; they’re surprising for a different reason, covered below.
Pricing: the two meters, honestly
Redshift
Provisioned RA3 clusters bill per node-hour, with 1-year and 3-year reserved pricing that cuts the rate substantially. Managed storage bills separately per GB-month. Serverless bills per RPU-hour, metered per second with a 60-second minimum per query burst. Concurrency Scaling adds transient clusters under load and bills them per second beyond a free daily allowance.
The trap: a provisioned cluster costs the same at 3 a.m. as at peak. Reserved instances make sense only when the workload is genuinely steady. The other trap is under-sizing, which shows up as queue time rather than a bigger bill.
BigQuery
On-demand bills per terabyte of data scanned by each query, with the first terabyte per month free. Reservations (Standard, Enterprise and Enterprise Plus editions) bill per slot-hour, with autoscaling and optional commitments. Storage bills per GB-month, and each dataset can be switched from logical to physical billing, which is often cheaper for compressed data but comes with a 14-day lock once changed.
The trap: SELECT * on a wide table you didn’t filter by partition. Bytes scanned is a meter that punishes lazy queries, and a BI tool refreshing a dashboard 200 times a day against a 10 TB table is a four-figure line item nobody planned.
What each costs by team size
All-in monthly ranges for the warehouse alone, from real invoices we’ve seen. Ingestion and BI come on top.
| Stage | Redshift | BigQuery |
|---|---|---|
| Solo, 1 to 2 people | $200 to $500 | $0 to $50 |
| Small team, 3 to 10 | $500 to $2,000 | $200 to $1,000 |
| Mid, 10 to 50 | $2,000 to $10,000 | $1,000 to $6,000 |
| Large, 50 to 200 | $10,000 to $40,000 | $5,000 to $25,000 |
BigQuery wins on entry cost because there’s nothing to keep running. Redshift closes the gap at steady, high-volume workloads where reserved nodes beat per-query metering. The full mechanics, including negotiation, are in the 2026 warehouse pricing playbook.
Performance: stop reading benchmarks
Every vendor benchmark shows its author winning. In practice both engines handle terabyte-scale analytics comfortably, and the differences that matter are operational: Redshift needs sort keys, distribution keys and occasional VACUUM attention to stay fast on provisioned clusters (Serverless and auto-tuning have softened this); BigQuery needs partitioning and clustering to stay cheap. A well-modeled schema on either beats a badly modeled one on the other by a wider margin than any engine difference.
Ecosystem and lock-in
Redshift is the natural pick inside AWS: Glue, Kinesis, SageMaker and Lake Formation all plug in, and IAM covers access. BigQuery is the natural pick inside Google Cloud, and it has one unfair advantage: GA4 exports land in it natively, which for marketing-heavy companies decides the question on its own.
Both now read Apache Iceberg tables in your own object storage, which is the real change since the 2019 version of this article. If you keep the storage layer open, switching engines later is a repointing exercise rather than a migration. See data lakehouse for why that matters.
Which one should you pick?
- Pick BigQuery if you’re on Google Cloud, you live in GA4, your workload is spiky, or you’re small and want the bill to start near zero.
- Pick Redshift if you’re deep in AWS, your query volume is steady enough that reserved capacity pays off, or your security team already governs everything through IAM.
- Consider neither if the real question is “Snowflake or Databricks”: for teams without a strong cloud allegiance, those two are usually the shortlist now. Our reviews of BigQuery, Snowflake and Databricks cover the trade-offs.
Common questions
Is BigQuery cheaper than Redshift?
At low and spiky usage, yes, often by a lot, because there’s no idle capacity to pay for. At steady high volume, reserved Redshift nodes can come out ahead. Model your actual query volume against both meters before believing either vendor’s calculator.
Can I migrate from Redshift to BigQuery?
Yes. Google’s BigQuery Migration Service translates Redshift SQL and moves data; the hard part is rewriting stored procedures, sort and distribution logic, and anything that assumed a cluster. Budget weeks, not days, for a real estate.
Which is better for machine learning?
BigQuery, narrowly: BigQuery ML and native vector search live in the warehouse, and Vertex AI is a short hop away. Redshift ML works but routes through SageMaker. If ML is the whole point, look at Databricks instead of either.
Do both support Apache Iceberg?
Yes, as of 2025 both read and write Iceberg tables held in your own object storage: Redshift through Spectrum and Lake Formation, BigQuery through BigLake and its native Iceberg tables.
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