Redshift vs BigQuery: AWS vs Google Cloud Warehouses (2026)

If your team is on AWS, pick Redshift. If you're on Google Cloud, pick BigQuery. That covers 80% of decisions. The remaining 20% comes down to how much infrastructure you want to manage, how you'd rather pay, and which ecosystem your tools already depend on. Redshift gives you cluster-level control and deep integration with S3, Glue, and SageMaker. BigQuery gives you a serverless engine where you never provision a node. Both handle petabytes. The difference is operational philosophy, not capability.

How do the architectures compare?

This is the decision that shapes everything else — pricing, scaling, and how your team spends its time.

          REDSHIFT                               BIGQUERY
 
  ┌─────────────────────────┐          ┌─────────────────────────┐
  │    Leader Node           │          │    Dremel Engine          │
  │  (query planning, coord) │          │ (serverless, auto-scaled) │
  ├─────────────────────────┤          ├───────────────────────────┤
  │  Compute Node (dc2.large)│          │                           │
  │  Compute Node (ra3.xlp)  │          │   No clusters. No nodes.  │
  │  Compute Node (ra3.4xl)  │          │   Slots allocated per     │
  │                          │          │   query or reserved.      │
  │  You pick the node type. │          │                           │
  │  You set the cluster     │          │   You pay per TB scanned  │
  │  size. You scale it.     │          │   or per slot-hour.       │
  ├─────────────────────────┤          ├───────────────────────────┤
  │  Redshift Managed Storage│          │  Capacitor Columnar Store │
  │  (automatic tiering to   │          │  (auto-optimized, no      │
  │   S3, no manual mgmt)    │          │   manual tuning needed)   │
  └─────────────────────────┘          └───────────────────────────┘
   AWS only                              GCP only

Redshift is cluster-based. You choose a node type (dc2 for SSD-backed compute, ra3 for managed storage that separates compute from storage), set the number of nodes, and that cluster processes your queries. A leader node coordinates query planning. Compute nodes do the work. You scale by resizing the cluster — adding nodes or switching to larger instances. Redshift Serverless exists as an alternative (more on that below), but the classic provisioned model is what most production deployments run.

BigQuery has no clusters at all. Google's Dremel engine allocates compute per query from a shared pool and reclaims it when the query finishes. You don't pick instance types. You don't resize anything. The engine scales automatically based on query complexity. Storage is in Google's Capacitor columnar format and optimizes itself — no distribution keys, no sort keys, no vacuum operations.

The practical difference: Redshift teams spend time sizing clusters and tuning distribution. BigQuery teams spend time writing efficient queries and partitioning tables. Both approaches work. The question is which kind of work your team is better at.

How does pricing actually work?

The billing models are different enough that direct comparison requires assumptions. Here's a realistic mid-size scenario.

  COST ESTIMATE: 5 TB stored, 30 TB scanned/month
 
  Redshift Provisioned (3x ra3.xlplus):
  ┌──────────────────────────────────────────────────┐
  │  Compute: 3 nodes x $1.086/hr x 730 hrs = $2,378│
  │  Storage: 5 TB x RMS (included w/ ra3)   =    $0│
  │                                                  │
  │  Estimated total:                    ~$2,378/mo  │
  └──────────────────────────────────────────────────┘
 
  Redshift Serverless (light-to-moderate load):
  ┌──────────────────────────────────────────────────┐
  │  Compute: ~800 RPU-hrs x $0.375/RPU-hr = $300/mo│
  │  Storage: 5 TB x $0.024/GB             = $120/mo│
  │                                                  │
  │  Estimated total:                      ~$420/mo  │
  └──────────────────────────────────────────────────┘
 
  BigQuery On-Demand:
  ┌──────────────────────────────────────────────────┐
  │  Queries:  30 TB x $6.25/TB scanned    = $187/mo│
  │  Storage:  5 TB x $0.02/GB (active)    = $100/mo│
  │                                                  │
  │  Estimated total:                      ~$287/mo  │
  └──────────────────────────────────────────────────┘
 
  BigQuery Capacity (100 slots):
  ┌──────────────────────────────────────────────────┐
  │  Compute: 100 slots x $0.04/slot-hr x 730 hrs   │
  │           = $2,920/mo                            │
  │  Storage: 5 TB x $0.02/GB (active)    = $100/mo │
  │                                                  │
  │  Estimated total:                    ~$3,020/mo  │
  └──────────────────────────────────────────────────┘
 
  * Provisioned Redshift = predictable but always-on cost
  * Redshift Serverless = pay-per-use, good for bursty workloads
  * BigQuery on-demand = cheapest at low scan volumes
  * BigQuery capacity = predictable, better for heavy sustained loads

Redshift Provisioned charges by the hour per node. A dc2.large cluster runs $0.25/hr per node. The ra3 family — where most new deployments land — runs $1.086/hr for ra3.xlplus up to $13.04/hr for ra3.16xlarge. Reserved Instances cut this 30-60% with 1- or 3-year commitments. Storage on ra3 nodes uses Redshift Managed Storage, which tiers cold data to S3 automatically. You pay for the cluster whether it's running queries or sitting idle, though you can pause clusters to stop compute charges.

Redshift Serverless charges $0.375 per RPU-hour (Redshift Processing Unit). You don't pick nodes — you set a base RPU capacity and Redshift scales within it. Good for variable workloads. Can be cheaper than provisioned if your usage is bursty. Can be more expensive if it's sustained.

BigQuery on-demand charges $6.25 per TB of data scanned. You pay only for what your queries touch. A well-partitioned table where queries hit a single day's partition costs almost nothing. A SELECT * from an unpartitioned 50 TB table costs $312.50 every time someone runs it. Storage is $0.02/GB/month for active data, $0.01/GB/month for long-term (90+ days untouched).

BigQuery capacity reserves slots at $0.04/slot-hour with autoscaling editions. One hundred slots running 24/7 costs about $2,920/month. This makes sense when your team runs enough queries that on-demand billing becomes unpredictable or expensive.

The cheapest option depends entirely on usage patterns. BigQuery on-demand wins for teams running moderate, well-optimized queries. Redshift Provisioned wins for teams with sustained, predictable workloads who commit to Reserved Instances. Serverless options on both sides work well for intermittent usage.

How does scaling work?

Redshift Provisioned scales by resizing the cluster — either elastic resize (adds/removes nodes in minutes) or classic resize (changes node types, takes longer). You can also enable concurrency scaling, which temporarily adds extra cluster capacity during peak demand. The process isn't automatic. Someone decides when to resize and what configuration to target.

Redshift Serverless scales within the RPU range you configure. It adjusts compute automatically based on workload, though you set the floor and ceiling. Closer to the BigQuery model, but still within guardrails you define.

BigQuery scales without any user action. The Dremel engine allocates slots per query from a pool. A simple query might use 50 slots. A complex multi-join aggregation might use 2,000. On-demand users draw from a shared pool with per-project quotas. Capacity users draw from their reserved slots plus optional autoscale headroom. You never resize anything.

If your workload is predictable and your team is comfortable managing clusters, Redshift's manual control lets you optimize cost. If your workload is spiky or your team doesn't want to think about infrastructure, BigQuery's automatic scaling removes the question entirely.

Side-by-side comparison

What about SQL compatibility?

Both speak ANSI SQL. The differences are in extensions and ecosystem hooks.

Redshift is PostgreSQL-compatible. If your team already knows Postgres, the transition is smooth — most queries work without changes. You get Postgres-style CREATE TABLE ... DISTKEY ... SORTKEY syntax for distribution and sort optimization. Stored procedures use PL/pgSQL. External tables via Redshift Spectrum query data directly in S3 without loading it, which means your data lake and warehouse share a query engine.

BigQuery uses GoogleSQL, which is ANSI SQL with Google's extensions. The syntax is close but not identical to any existing database. BigQuery ML lets you CREATE MODEL to train machine learning models directly in SQL — logistic regression, boosted trees, time-series forecasting, even imported TensorFlow models. Geospatial functions (ST_DISTANCE, ST_AREA, ST_INTERSECTS) are built in and mature. Nested and repeated fields (STRUCT, ARRAY) handle denormalized data natively, which matters for event data and semi-structured logs.

If you're migrating from Postgres, Redshift requires fewer query rewrites. If you want ML and geospatial analysis inside the warehouse without external tools, BigQuery is further along.

How do they handle semi-structured data?

Redshift added the SUPER data type for JSON. You can ingest JSON documents, query them with dot notation and array indexing, and use PartiQL syntax for nested access. It works, but it was added later — the ergonomics are functional, not native. Deep nesting gets verbose.

BigQuery treats nested and repeated data as first-class citizens. STRUCT and ARRAY types let you model nested objects directly in the schema. A single row can contain an array of structs, each with their own nested arrays. This maps naturally to event data (a session containing events containing parameters) without flattening into dozens of join tables.

If your data is mostly flat and relational, both handle it equally well. If you're working with deeply nested event streams, analytics payloads, or JSON-heavy data, BigQuery's native nested types are meaningfully easier to work with.

Which ecosystem fits your stack?

Redshift plugs into the broader AWS ecosystem. S3 is the default data lake and staging area. Glue handles cataloging and ETL. SageMaker provides ML capabilities that Redshift ML wraps. QuickSight is AWS's BI tool (though many teams opt for Tableau, Looker, or other third-party tools instead). Lambda functions extend Redshift with UDFs. IAM handles authentication and authorization end to end. If you're already running production infrastructure on AWS, Redshift shares the same security perimeter, VPC configuration, and billing account.

BigQuery plugs into Google Cloud. GA4 exports event data directly into BigQuery — no ETL required. Google Ads conversion and spend data flows in natively. Google Sheets can query BigQuery tables. Looker and Looker Studio connect without configuration. Vertex AI extends BigQuery ML into full model training and deployment. If your marketing team runs on Google Analytics and your company uses Google Workspace, BigQuery becomes the natural analytical layer.

The pattern: AWS shops pick Redshift because their data, networking, and security already live there. Google Cloud shops pick BigQuery because their event data and marketing analytics are already there. Mixing clouds is possible but adds friction.

How to decide

Pick Redshift if:

• Your infrastructure already runs on AWS and you want one security perimeter, one billing account
• You want direct control over cluster sizing and can optimize dist/sort keys for your workload
• You need Spectrum to query S3 data lakes without loading data into the warehouse
• Your team knows PostgreSQL and you want minimal SQL migration effort
• Reserved Instance pricing makes sense for your sustained compute needs

Pick BigQuery if:

• You're on Google Cloud and want native integration with GA4, Google Ads, Looker, and Sheets
• You don't want to manage clusters, resize nodes, or think about infrastructure at all
• Your workload is variable enough that pay-per-query pricing saves money over always-on clusters
• You want ML and geospatial queries inside the warehouse without external services
• Your team is small and you'd rather spend time on queries than on cluster operations

Pick either if:

• You need a petabyte-scale columnar warehouse, a SQL-fluent team, and standard BI on top. Both handle this. The tiebreaker is almost always which cloud you're already invested in.

Where Fastero fits

Fastero connects to both Redshift and BigQuery — along with 30+ other data sources. You plug in your warehouse credentials, and you get AI-powered dashboards and alerts on top of your data. No QuickSight subscription. No Looker seat fees. If you're running both warehouses — one team on AWS, another on GCP, which happens more than anyone plans for — Fastero lets you query across them from one place.

We don't replace your warehouse. We sit on top of it and make it useful to people who shouldn't need to open a SQL console.

FAQ

Is BigQuery cheaper than Redshift? At low-to-moderate query volumes with well-partitioned tables, BigQuery on-demand is often cheaper. But the comparison shifts at scale. Redshift with Reserved Instances can undercut BigQuery capacity pricing for sustained workloads. The real variable is optimization: a sloppy SELECT * on BigQuery costs $6.25 per TB every run, while an idle Redshift cluster costs nothing extra for its hundredth query. Neither is categorically cheaper. The lowest bill goes to the team that understands their billing model.

Can I use Redshift without managing clusters? Yes. Redshift Serverless launched in 2022 and removes the cluster management entirely. You get a namespace and workgroup, set an RPU base capacity, and Redshift handles the rest. The tradeoff is less control over performance tuning and slightly different pricing ($0.375/RPU-hour). For teams that want Redshift's SQL compatibility without the operational overhead, it's a good middle ground.

How hard is it to migrate from Redshift to BigQuery? SQL migration is moderate — Redshift's PostgreSQL dialect and BigQuery's GoogleSQL differ in functions, data types, and DDL syntax, but the core queries translate. The harder parts are rebuilding ETL pipelines, moving data (export to S3/GCS, then load), updating IAM/permissions, and reconnecting BI tools. Budget 3-6 weeks for a medium-complexity migration. DuckDB can help as a local testing layer during the transition.

Does BigQuery work with AWS data? BigQuery can query data in S3 via BigQuery Omni, which runs compute in AWS regions through BigQuery's connection framework. Metadata and management stay in GCP. It's useful for federated queries but isn't the same as Redshift running natively on AWS. If most of your data lives in S3, Redshift Spectrum is a more natural fit. If you need to unify queries across clouds, Fastero can query both warehouses without moving the data.

Which has better data sharing? Redshift supports cross-account data sharing within AWS — you can share databases or specific schemas between Redshift clusters in different AWS accounts with read access. BigQuery's Analytics Hub provides a similar exchange model, with both public and private listings. Snowflake's data sharing ecosystem is more mature than either, if data sharing is your primary concern.

Related posts:

• Snowflake vs BigQuery: Cloud Warehouses Compared
• DuckDB vs Postgres for Analytics Workloads
• Best Tools for Data Engineering Teams (2026)
• How to Run SQL Across Multiple Databases Without a Warehouse

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