Cloud data warehouses · Amazon Web Services, Inc.

Amazon Redshift

AWS's managed cloud data warehouse, available as provisioned clusters or an auto-scaling serverless option.

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Amazon Redshift is AWS's petabyte-scale, columnar cloud data warehouse. It runs either as provisioned clusters, sized in RA3 or the newer RA2/RG nodes with managed storage, or as Redshift Serverless, which measures capacity in Redshift Processing Units (RPUs) and scales automatically without cluster management. Redshift Spectrum lets queries reach directly into data in Amazon S3 without loading it, and zero-ETL integrations pull data in from Aurora, RDS, and DynamoDB. It shares much of its optimizer and SQL surface with PostgreSQL but is a proprietary, massively parallel processing (MPP) engine under the hood. Redshift is AWS-only and delivered as a managed service; there is no self-hosted or other-cloud deployment option.

At a glance

Vendor Amazon Web Services, Inc.
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for AWS-centric teams wanting a managed warehouse tightly integrated with the rest of the AWS data stack.

Pricing

Provisioned clusters are billed hourly per node type; Redshift Serverless bills per RPU-hour with a per-second minimum; new Serverless users get trial credits and provisioned clusters offer a limited free-tier trial.

Plan Price Notes
RA3 provisioned (example) $3.26/hr for 4x ra3.4xlarge On-demand, US East (N. Virginia)
Redshift Serverless $0.375 per RPU-hour Per-second billing, 60-second minimum per session
Managed storage $0.024/GB-month US East (N. Virginia)
New Serverless users $300 trial credit 90-day expiration; provisioned clusters offer a 2-month, 750-hours/month free trial where Serverless isn't available

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Provisioned (RA3) or auto-scaling Serverless (RPU-based) deployment
  • Redshift Spectrum for querying S3 data lakes directly
  • Zero-ETL integrations from Aurora, RDS, and DynamoDB
  • Concurrency scaling for bursty query workloads
  • Materialized views and automatic query result caching
  • Machine learning via Redshift ML (SageMaker integration)
  • Federated querying across other AWS databases

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Snowflake Cloud-native data warehouse that separates storage and compute and bills usage in per-second credits. Google BigQuery Serverless cloud data warehouse billed by data scanned or by slot capacity, with no infrastructure to provision. Azure Synapse Analytics Microsoft's unified analytics service combining a dedicated SQL data warehouse, serverless SQL, and Apache Spark pools.

Amazon Redshift in the index now

Terms to know

Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. Reverse ETL The practice of syncing modeled data from a warehouse back out into operational tools like a CRM or ad platform. OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis. Massively parallel processing An architecture that splits a query across many independent compute nodes, each processing its own data slice.

Related guides

How to choose a cloud data warehouse Start from your cloud, your pricing tolerance and your workload shape — general BI, high-concurrency apps, or enterprise mixed workloads — not the feature list. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down.