Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine
AWS eliminated Redshift Spectrum's $5/TB data lake scanning fees while launching Graviton-based RG instances that run 2.2x faster than RA3 at 30% lower cost per vCPU.
Deep dive
- AWS launched Redshift RG instances powered by Graviton processors in 24 regions, replacing the previous generation RA3 instances with significant performance and cost improvements
- Performance gains: up to 2.2x faster for data warehouse workloads, 2.4x faster for Apache Iceberg queries, 1.5x faster for Apache Parquet queries compared to RA3
- Cost reduction: 30% lower price per vCPU compared to RA3, plus elimination of the $5/TB Redshift Spectrum scanning fees that previously added to total costs
- The integrated data lake query engine now runs on cluster nodes within your VPC boundary using existing IAM roles, removing the need for Redshift Spectrum
- Migration paths: elastic resize (10-15 min downtime) for compatible configurations, or snapshot/restore for those wanting to make configuration changes during migration
- No application changes required - external tables, schemas, and query syntax remain unchanged, including existing Spectrum queries
- Instance comparison: rg.xlarge replaces ra3.xlplus (4 vCPU, 32GB), rg.4xlarge replaces ra3.4xlarge (16 vCPU vs 12, 128GB vs 96GB)
- AWS explicitly designed this generation to handle high query volumes from AI agents, which can query data warehouses at scale that dwarfs typical human usage
- The March 2026 performance update already improved BI dashboard and ETL workload query speeds by up to 7x for low-latency SQL queries
- Pricing options: On-Demand Instances with hourly billing or Reserved Instances for additional cost savings
Decoder
- AWS Graviton: Amazon's custom ARM-based processor designed for cloud workloads, offering better price-performance than x86 alternatives
- Apache Iceberg: Open table format for huge analytic datasets in data lakes, providing ACID transactions and schema evolution
- Apache Parquet: Columnar storage file format optimized for analytics, commonly used in data lakes
- Redshift Spectrum: AWS's previous external data query service that charged $5/TB for scanning data in S3, now replaced by the integrated query engine in RG instances
Original article
Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine
Since 2013, Amazon Redshift has given the full power of a data warehouse in the cloud, at a fraction of the on-premises cost. Every architectural generation—from dense compute to Amazon RA3 instances, from provisioned to Amazon Redshift Serverless—has made each query cheaper, faster, and more efficient than the last.
For over a decade, as data volumes have grown and analytics requirements have evolved, organizations increasingly leverage both data warehouse tables for structured, frequently-accessed data and data lakes for cost-effective storage of diverse datasets. Add AI agents to the mix and they query your data warehouse at a scale that dwarfs typical human usage, leading to spiraling operational costs.
Amazon Redshift has doubled down on its core strengths to meet the demands of any workload — whether driven by humans or AI agents. For example, in March 2026, Amazon Redshift improved the performance of business intelligence (BI) dashboards and ETL workloads by speeding up new queries by up to 7 times. This significantly improves the response times of low-latency SQL queries, such as those used in near-real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.
Today, we're announcing Amazon Redshift RG instances, a new instance family powered by AWS Graviton. RG instances deliver better performance, running data warehouse workloads up to 2.2x as fast as RA3 instances at 30% lower price per vCPU. Their integrated data lake query engine lets you run SQL analytics across your data warehouse and data lake from a single engine with performance up to 2.4x as fast as RA3 for Apache Iceberg and up to 1.5x as fast as RA3 for Apache Parquet. This blend of speed, cost efficiency, and an integrated data lake query engine makes Redshift RG instances well-suited to handle the high query volumes and low-latency requirements of today's analytics and agentic AI workloads.
You can compare new RG instances and current RA3 instances:
| Current RA3 Instance | Recommended RG instance | vCPU | Memory (GB) | Primary Use Case |
ra3.xlplus |
rg.xlarge |
4 | 32 | Small cluster departmental analytics |
ra3.4xlarge |
rg.4xlarge |
12 → 16 (1.33:1) | 96 GB → 128 GB (1.33:1) | Standard production workloads, medium data volumes |
This approach reduces total analytics costs for customers running combined data warehouse and data lake workloads, while simplifying operations through a single system for querying both warehouse tables and Amazon Simple Storage Service (Amazon S3) data lakes. We recommend using the AWS Pricing Calculator with your specific workload patterns to estimate savings.
Getting started with Amazon Redshift RG instances
You can launch new clusters or migrate existing clusters through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS API. The integrated data lake query engine is enabled by default.
In the Amazon Redshift console, you can choose new RG instances when you create a cluster.
You can migrate previous-generation instances to RG instances with optimal paths based on your cluster configuration to estimate costs, validate compatibility, and automate execution.
- Elastic Resize—in-place migration with 10-15 minutes downtime for compatible configurations
- Snapshot and Restore—create a RG cluster from an RA3 snapshot. This is best for customers who want to make configuration changes during the migration
Your external tables, schemas, and query syntax—including existing Spectrum queries—remain unchanged. There is no need to recreate external tables or modify application code. To learn more, visit the Redshift Management Guide.
Amazon Redshift now executes data lake queries on cluster nodes—the same compute that processes data warehouse workloads. As a result, Amazon Redshift Spectrum is no longer required. Data lake queries stay within your VPC boundary, use existing IAM roles, and incur zero per-terabyte scanning charges. This removes the $5/TB Spectrum scanning fees that previously added to total Redshift costs.
Now available
Amazon Redshift RG instances are now available in the following AWS Regions: US East (N. Virginia, Ohio), US West (N. California, Oregon), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Taiwan, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, Milan, London, Paris, Spain, Stockholm), and South America (São Paulo). For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For Redshift Provisioned, you can select On-Demand Instances with hourly billing and no commitments or choose Reserved Instances for cost savings. To learn more, visit the Amazon Redshift Pricing page.
Give RG instances a try in the Redshift console and send feedback to AWS re:Post for Amazon Redshift or through your usual AWS Support contacts.
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