Databases · head to head
Amazon Redshift vs BigQuery

Amazon Redshift
Databases
Fast, scalable cloud data warehouse from AWS
- From
- Free
- Rated
- -

BigQuery
Databases
Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Amazon Redshift on-demand pricing runs up to 75% higher than competitors like Snowflake and BigQuery; BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
- They diverge on capability: Amazon Redshift covers Columnar Storage, BigQuery covers Serverless compute.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Amazon Redshift and BigQuery actually diverge.
| Attribute | Amazon Redshift | BigQuery |
|---|---|---|
| Platforms | Web | Web, Cloud API |
| Founded | 2012 | 2008 |
Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), category (Databases).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Amazon Redshift
- Columnar Storage
- Massively Parallel
- Machine Learning
- AQUA Acceleration
- Data Sharing
- Federated Query
- Concurrency Scaling
- S3
Only in BigQuery
- Serverless compute
- Separation of storage and compute
- Two pricing models
- Partitioning and clustering
- Materialised views
- BigQuery ML
- Storage Write API
- BI Engine
What people use each for
The jobs each tool is most often brought in to do.
Amazon Redshift
- Business intelligencenot BigQuery
- Data warehousingnot BigQuery
- Real-time analyticsnot BigQuery
- Reportingnot BigQuery
- Machine learningnot BigQuery
BigQuery
- A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Amazon Redshift
- Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Amazon Redshift
- Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Amazon Redshift
- Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Amazon Redshift
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Amazon Redshift
- On-demand pricing runs up to 75% higher than competitors like Snowflake and BigQuery
- Requires significant manual tuning including managing concurrency scaling costs and configuring Workload Management queues
- Performance degrades without proper design of distribution keys and sort keys
- Limited elastic resize options - can only halve or double current cluster size
- AWS lock-in makes it unsuitable for multi-cloud architectures
BigQuery
- On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
- There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
- It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
- Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
- The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.
Pricing, plan by plan
Amazon Redshift
Free- Free TrialFree
- 750 DC2.Large hours
- 2 months free
- Full features
- On-Demand$0.25/hour
- Pay per node hour
- All features
- Standard support
BigQuery
Free- Free TierFree
- 1TB queries/month
- 10GB storage/month
- Standard support
- On-demand$6.25/TB
- Pay per query
- Pay per storage
- All features
Which should you pick?
Choose Amazon Redshift if
- You need columnar storage.
- You want to start without paying.
- You also want massively parallel.
Choose BigQuery if
- You need serverless compute.
- You want to start without paying.
- You work on Web, Cloud API.
- You also want separation of storage and compute.
Questions people ask
- Is Amazon Redshift or BigQuery better?
- Neither clearly leads. Amazon Redshift starts at Free and BigQuery at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Amazon Redshift or BigQuery?
- Amazon Redshift starts at Free and BigQuery at Free.
- Does Amazon Redshift or BigQuery run on more platforms?
- Amazon Redshift runs on Web. BigQuery runs on Web, Cloud API.
- Can I use Amazon Redshift for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Amazon Redshift best used for?
- Amazon Redshift is most often used for business intelligence, data warehousing, real-time analytics, reporting. Of those, business intelligence and data warehousing are not what BigQuery is typically brought in for.
- What can Amazon Redshift do that BigQuery cannot?
- Amazon Redshift covers Columnar Storage, Massively Parallel, Machine Learning, AQUA Acceleration. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.
Answered from the vendors’ own pages
Amazon Redshift: What deployment options does Amazon Redshift offer?
Redshift offers Provisioned Cluster (with RA3 or DC2 nodes) and Serverless options to match varying workloads. The new Redshift RG instance family, powered by Graviton, delivers 2.4x faster performance than RA3 at 30% lower cost per vCPU.
SourceBigQuery: How is BigQuery actually billed?
Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.
Amazon Redshift: What does Amazon Redshift cost?
Provisioned cluster pricing: RA3 on-demand starts at $1.086/hour for ra3.xlplus. Serverless costs approximately $0.375 per RPU-hour with 4-RPU minimum (roughly $1.50/hour active workload). Managed storage costs $0.024/GB-month.
SourceBigQuery: How do I control query cost?
Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.
Amazon Redshift: Does Redshift work with data lakes?
Yes, Redshift's integrated data lake query engine processes workloads on Apache Iceberg tables and other supported formats in Amazon S3, allowing you to run SQL analytics across your data warehouse and data lake from the same engine.
SourceBigQuery: Can I use it without being on Google Cloud?
The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.
Amazon Redshift: Is there a free tier for Amazon Redshift?
AWS offers a free trial with $300 USD in Serverless credits valid for 90 days, but Redshift is not part of the permanent AWS Free Tier.
SourceBigQuery: Is it suitable for serving application queries?
No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.
BigQuery: When should I move from on-demand to capacity pricing?
When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.
Related pages
More on Amazon Redshift
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