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Redshift vs BigQuery vs Synapse: Data Warehouse Comparison (2026)

Last verified: June 18, 2026  ·  Format: Comparison

Redshift vs BigQuery is the matchup most teams reach for first when they shop for a cloud data warehouse, but the real decision is usually a three-way one once Azure Synapse enters the room. All three store and analyze large datasets with SQL, yet they meter cost differently, take different stances on serverless, and lean toward different cloud ecosystems. This comparison sets Amazon Redshift, Google BigQuery, and Azure Synapse Analytics side by side using each vendor's own published figures, so you can match the warehouse to your stack rather than to a headline.

We keep it practical. You will find a quick verdict, a side-by-side table, a walk through how each warehouse works, a pricing breakdown, a look at the serverless and scaling story, and a decision section for picking one. Figures were checked on June 18, 2026 against AWS, Google Cloud, and Microsoft documentation; always confirm current rates before you commit, and note that Google states its prices in binary units such as TiB and GiB while AWS and Microsoft use TB and GB.

Redshift vs BigQuery vs Synapse at a Glance

Before the detail, here is the head-to-head. The table below sets the three warehouses side by side on the attributes that drive most decisions: the underlying model, how each one meters cost, whether a serverless option exists, the free entry point, and who each is best suited to. Read it as a map, then use the sections that follow to dig into pricing and scaling.

How Each Data Warehouse Works

The three warehouses share a job description, scanning large tables to answer analytical questions in SQL, but they are built differently underneath. A Redshift vs BigQuery comparison really turns on architecture, and adding Synapse only widens that gap, so understanding how each is built is the fastest way to predict how it will behave, and bill, for your workload.

Amazon Redshift

Amazon Web Services runs Redshift in two shapes. Provisioned Redshift uses RA3 nodes, which separate compute from managed storage, and newer Graviton-based RG nodes; you choose a node type and size and the cluster runs until you change it. Redshift Serverless removes the cluster decision entirely: capacity is measured in Redshift Processing Units, where each RPU provides 16 GB of memory, and it scales automatically with your workload. Redshift Spectrum extends either model out to data sitting in Amazon S3, charged separately by the volume scanned.

Google BigQuery

Google BigQuery is serverless from the ground up. It decouples storage from compute, with queries running on units of capacity called slots that BigQuery allocates and releases for you. There is no cluster to provision or size, which is the trait that most distinguishes it from the other two. You load data, write SQL, and the platform handles the rest, billing storage and compute separately. The Google Cloud pillar covers where it sits among the rest of the platform's data services.

Azure Synapse Analytics

Microsoft Azure ships Synapse as a unified analytics service with several engines under one roof. Dedicated SQL pools provide provisioned warehouse capacity measured in Data Warehouse Units, serverless SQL lets you query data in place without a standing pool, and Apache Spark pools handle big-data processing. One planning note matters here: Microsoft positions Microsoft Fabric as its go-forward analytics platform and provides a Synapse-to-Fabric migration path, so a Synapse decision today should weigh that direction of travel.

Pricing Models Compared

Pricing is where Redshift vs BigQuery vs Synapse stops looking like a fair fight and starts looking like three different games. Each vendor meters cost in its own unit, so the trick is to map your query pattern, steady, bursty, or occasional, onto the model that rewards it. The cards below summarize the entry points; the notes after them add the detail.

Amazon Redshift pricing

Redshift Serverless starts at $1.50 per hour and is measured in RPU-hours at $0.375 per RPU-hour, billed per second with a 60-second minimum, scaling between 4 and 1024 RPUs. Provisioned pricing instead bills by node-hour, with RA3 nodes ranging from about $0.543 per hour for ra3.large up to about $13.04 per hour for ra3.16xlarge, plus newer Graviton RG nodes. Redshift Spectrum adds $5.00 per TB scanned for queries against S3. New Serverless users receive a $300 credit valid for 90 days.

Google BigQuery pricing

BigQuery prices storage and compute apart. Compute is on-demand at $6.25 per TiB scanned, with the first 1 TiB each month free, or capacity-based under BigQuery Editions billed per slot-hour, starting around $0.04 for Standard, $0.06 for Enterprise, and $0.10 for Enterprise Plus. Storage runs from $0.01 per GiB for logical or $0.02 per GiB for physical, with the first 10 GiB free and an automatic discount of roughly half for data left unmodified for 90 days. Remember Google states these in binary units, GiB and TiB.

Azure Synapse pricing

Synapse meters by engine. Serverless SQL is about $5 per TB of data processed, with a 10 MB minimum charged per query. Dedicated SQL pools bill by Data Warehouse Units: the lowest tier, DW100c, runs about $876 per month, with compute around $883 per 100 DWUs per month unless you pause the pool to stop compute charges. Apache Spark pools are about $0.143 per vCore-hour, and storage is about $23 per TB per month. Treat the node, tier, and slot figures across all three vendors as starting and region-varying.

Serverless and Scaling

If there is one axis that separates these three cleanly, it is how serverless each one is. In a Redshift vs BigQuery comparison this is the sharpest distinction, and Synapse sits somewhere between the two. Serverless here means you do not size, provision, or babysit a cluster; capacity appears when a query runs and goes away when it finishes, and you pay for what you used. That model suits bursty and unpredictable analytics, while provisioned capacity can be cheaper for steady, high-volume workloads.

BigQuery is the most fully serverless of the three. There is no cluster to manage at all; compute scales through slots automatically, which is why it is the default pick for ad-hoc, spiky querying where you would rather not predict capacity. Redshift offers both worlds: Redshift Serverless scales RPUs automatically for bursty work, while provisioned RA3 clusters give predictable, reservable capacity for steady warehousing. Synapse offers a serverless SQL pool for querying data in place alongside provisioned dedicated SQL pools, and its dedicated pools can be paused to stop compute charges when idle.

One more scaling nuance worth holding onto: each serverless model bills against a different unit, so identical workloads cost differently. Redshift Serverless meters RPU-hours per second, BigQuery on-demand meters TiB scanned, and Synapse serverless SQL meters TB processed. A query that scans little data is cheap on BigQuery; a workload that runs near-constantly may favor reserved Redshift or a dedicated Synapse pool. If the broader idea of renting elastic capacity is new, our guide to what cloud computing is sets the foundation, and the Cloud Tools hub maps these services among the wider cloud landscape.

Which Data Warehouse to Choose

The cleanest way to choose is to start from the cloud you already run on, then refine by query pattern. All three are capable warehouses; the deciding factor is usually fit, not raw capability. Here is how the choice tends to fall out in practice.

To make it concrete: pick BigQuery for serverless, ad-hoc analytics and GCP-native stacks; pick Redshift for AWS ecosystems and either predictable, steady warehousing on provisioned clusters or bursty work on Serverless; and pick Synapse for Azure and Microsoft estates, while evaluating Microsoft Fabric as the path Microsoft is steering toward. The matchup of Redshift vs BigQuery often narrows to AWS versus GCP gravity, and Synapse joins that logic on the Azure side.

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