AWS Cost Efficiency

How to optimize AWS Redshift Costs ?

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that serves as a cornerstone of Big Data infrastructure.Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. In 2024, over 11,202 companies globally have adopted Amazon Redshift as their go-to Big Data solution, commanding a significant market share of 28.31%. Given its widespread use and critical role in managing vast amounts of data, optimizing costs becomes crucial for businesses looking to maximize their return on investment.[1]

In this post, we will learn about effective strategies to reduce Redshift costs and optimize its pricing. By implementing these cost-saving techniques, you can ensure your organization gets the best value from this powerful data warehousing tool while maintaining high performance and scalability.

Understanding Amazon Redshift Pricing

The table below provides a concise overview of Amazon Redshift's pricing across various service components, including node types, serverless options, spectrum, concurrency scaling, machine learning, zero-ETL integration, backup storage, and data transfer.[2]

Service Component Pricing Details
Amazon Redshift Node Pricing DC2 Nodes
On Demand Pricing $0.25 - $4.80 per hour
RA3 Nodes
On Demand Pricing $1.086 - $13.04 per hour
Reserved Instances
No Upfront $0.760 - $9.128 per hour
Partial Upfront $0.728 - $8.737 per hour
All Upfront $0.717 - $8.606 per hour
Amazon Redshift Serverless Automatically scales based on workload demands, charged per RPU-hour on a per-second basis. $0.36 per RPU hour
Amazon Redshift Spectrum pricing Enables querying data directly in Amazon S3 using SQL. $5.00 per terabyte of data scanned
Concurrency Scaling pricing Dense Compute - Additional compute capacity for high concurrency and performance. $0.00133 per sec
Dense Storage - Scales compute capacity for storage-intensive queries. $0.00189 - $0.00024 per sec
RA3 - Concurrency scaling for RA3 nodes, optimizing for cost and performance. $0.00362-$0.0003 per sec
Redshift ML pricing Enables machine learning model training and inference on Redshift data. $7 - $15 per million cells
Zero-ETL integration costs Integrated data loading and transformation without additional charges. No additional fee
Backup storage Charged as per S3 rates for manual snapshots. Automated snapshots are free for up to 35 days. -
Data transfer Data Sharing Data Transfer In To $0.02 per GB

Example to Demonstrate Redshift pricing

Scenario: You use a Multi-AZ cluster deployed across two Availability Zones (AZs). Each AZ hosts four RA3.4xlarge nodes, and you utilize 40 TB of Redshift Managed Storage (RMS) for a month, using on-demand pricing. The charges are calculated as follows:

Redshift RA3 Instance Cost:

  • Number of instances per AZ: 4
  • Cost per instance per hour: $3.26 USD
  • Total hours in a month: 730

Calculation for each AZ: 4 instances×$3.26USD/hour×730 hours=$9,519.20 USD

Since the cost is the same for both AZ1 and AZ2:

Total RA3 Instance Cost=2×$9,519.20USD=$19,038.40USD

RMS Cost:

  • Total storage: 40 TB
  • Conversion: 1 TB = 1,024 GB
  • Cost per GB: $0.024 USD

Calculation: 40TB×1,024GB/TB×$0.024USD/GB=$983.04USD

Total Monthly Cost: $19,038.40USD (RA3 Instances)+$983.04USD (RMS)=$20,021.44USD

So, the total monthly cost for using a Multi-AZ Amazon Redshift cluster with the given configuration and usage is $20,021.44 USD.

Strategies to Optimize AWS Redshift and reduce costs

Below are several comprehensive strategies designed to enhance performance efficiency and significantly reduce Amazon Redshift costs. By implementing these methods, you can ensure optimal resource utilization and cost-effectiveness.[3]

1. Right-Sizing Your Cluster

Determining the optimal size for your Amazon Redshift cluster is crucial for managing costs while ensuring performance meets your workload needs. The AWS Redshift console provides tools to help you analyze performance metrics and workload patterns, allowing you to choose a cluster size that balances cost and efficiency. By tailoring your cluster size based on actual usage and anticipated growth, you avoid over-provisioning resources, which can lead to unnecessary expenses, and under-provisioning, which can cause performance issues and expensive emergency scaling.

Consider the example scenario below to right-size an over-provisioned Amazon Redshift cluster.

Scenario: Over-Provisioned Redshift Cluster

A company currently utilizes Amazon Redshift for its data warehousing needs and has over-provisioned its resources. Their cluster configuration involves using four dc2.8xlarge nodes, each costing $6.67 per hour, resulting in a total hourly cost of $26.68 and a monthly cost of $19,209.60 (for 720 hours). Performance analysis indicates that the average CPU utilization is 30%, and average disk I/O utilization is 25%, demonstrating that the workload does not justify such high provisioned resources. To address these inefficiencies, the company can optimize by switching to eight dc2.large nodes, with each node costing $0.25 per hour.

Current Costs

  • Cost per Node per Hour: $6.67
  • Total Cost per Hour: $6.67 * 4 = $26.68
  • Total Cost per Month (720 hours): $26.68 * 720 = $19,209.60

Optimized Costs

  • Per Node per Hour: $0.25
  • Total Cost per Hour: $0.25 * 8 = $2.00
  • Total Cost per Month (720 hours): $2.00 * 720 = $1,440.00


  • Monthly Savings: $19,209.60 (current cost) - $1,440.00 (optimized cost) = $17,769.60
  • Annual Savings: $17,769.60 * 12 months = $213,235.20

By optimizing their Amazon Redshift cluster from four dc2.8xlarge nodes to eight dc2.large nodes, the company can save approximately $213,235.20 annually.

2. Leverage Redshift Spectrum for Cold Data

Image depicting AWS Redshift Spectrum

Leverage Redshift Spectrum for querying cold data stored in Amazon S3 to reduce Amazon Redshift costs. This approach allows you to store infrequently accessed data in the cheaper S3 storage while keeping frequently accessed data in Redshift. By querying data directly from S3 using Spectrum, you can reduce the storage and compute costs associated with Redshift clusters. This method is particularly useful for managing large datasets where only a portion of the data is frequently queried. Integrating Redshift Spectrum enables cost savings by optimizing data storage and reducing the load on your Redshift clusters.

Here's an example of how to set up and query data using Redshift Spectrum:

-- Create an external schema in Redshift that references an external database in your AWS Glue Data Catalog
DATABASE 'spectrum_db'
IAM_ROLE 'arn:aws:iam::<your-aws-account-id>:role/MySpectrumRole'

-- Define an external table that points to your data in S3
CREATE EXTERNAL TABLE spectrum_schema.sales (
    sales_id INT,
    sales_date DATE,
    amount FLOAT
LOCATION 's3://your-bucket/path-to-data/';

-- Query the external table in S3 using Spectrum
SELECT * FROM spectrum_schema.sales
WHERE sales_date >= '2023-01-01';

-- Load frequently accessed data into Redshift
COPY sales
FROM 's3://your-bucket/path-to-hot-data/'
IAM_ROLE 'arn:aws:iam::<your-aws-account-id>:role/MyRedshiftRole'

-- Combine queries across hot data in Redshift and cold data in S3
WHERE sales_date >= '2023-01-01'
SELECT * FROM spectrum_schema.sales
WHERE sales_date < '2023-01-01';

This strategy helps optimize storage costs while maintaining query performance by effectively utilizing Redshift Spectrum for cold data.

Magellan Rx utilized Amazon Redshift Spectrum to query cold data stored in Amazon S3, reducing operational costs by 20%. Vinesh Kolpe, VP of Information Technology, highlighted this approach for optimizing storage costs and improving performance.[5]

3. Handling Spiky Workloads

Image for Amazon Redshift scaling

Amazon Redshift offers powerful features like Elastic Resize and Concurrency Scaling to efficiently manage varying workload demands while optimizing costs. These features enable flexible resource allocation and cost-effective scaling based on workload characteristics.

Feature Description
Elastic Resize Allows dynamic adjustment of Redshift cluster size based on predictable workload increases.
Sizes cluster for steady-state needs and scales out temporarily during high-demand periods.
Initiated from a single endpoint in minutes, either on-demand or via a schedule.
Maintains session connections and queues queries during resizing to ensure seamless operation without additional costs.
Concurrency Scaling Provides elasticity to handle unpredictable workload spikes without permanently oversized clusters.
Automatically scales out to multiple clusters from a single endpoint within seconds.
Supports virtually unlimited concurrent users while maintaining SLAs.
Per-second billing for additional clusters used, minimizing costs by paying only for actual usage.
Offers one hour of free Concurrency Scaling usage per day, covering 97% of workload needs, reducing costs significantly during peak loads.

These features empower Redshift users to optimize infrastructure costs by scaling resources precisely to match workload demands, whether predictable or unpredictable, ensuring efficient performance and cost savings.

4. Managing Usage Limits for Concurrency Scaling

Concurrency Scaling enables automatic scaling of Redshift clusters to handle varying workload demands, ensuring performance without over provisioning resources. To control costs, administrators can define usage limits based on daily, weekly, or monthly patterns. For example, setting daily usage limits prevents unexpected spikes in scaling costs.

Here's an example using AWS SDK for Python (Boto3) to configure Concurrency Scaling settings and set usage limits:

import boto3

redshift = boto3.client('redshift')

# Define Concurrency Scaling settings
response = redshift.modify_cluster(
        'ClusterIdentifier': 'my-redshift-cluster',
        'NumberOfNodes': 5  # Set the initial number of nodes
    PauseAfter=300,  # Set auto-pause after 5 minutes of inactivity
    MaxConcurrencyScalingClusters=10  # Set maximum concurrent scaling clusters

# Set usage limits for Concurrency Scaling
response = redshift.modify_cluster_concurrency_scaling(
    ConcurrencyScalingMode='auto',  # Auto or manual scaling mode
    MaxClusters=10,  # Set the maximum number of concurrent clusters
    MinClusters=1,  # Set the minimum number of clusters
    PauseRequests=True,  # Pause auto scaling requests
    ResumeRequests=True  # Resume auto scaling requests

Organizations can effectively manage Concurrency Scaling usage, ensuring cost efficiency while maintaining optimal performance in Amazon Redshift environments.

5. Automatic WLM (Workload Management)

Optimizing resources with Automatic WLM (Workload Management) in Amazon Redshift helps significantly in reducing costs by maximizing query throughput and ensuring consistent performance across various workload priorities. By dynamically allocating query slots based on workload priorities (such as BI/Analytics, Data Science, and ETL), Automatic WLM ensures that high-priority queries receive preferential treatment, thus preventing expensive queries from monopolizing system resources. This fair sharing of resources not only enhances operational efficiency but also minimizes idle time and improves overall resource utilization.

VOO slashed costs by 30% with Amazon Redshift's Automatic WLM, boosting query efficiency and resource utilization. Candice Schueremans, VOO's Enterprise Information Management Director, highlighted its impact on reducing idle time.[6]

6. Amazon Redshift Advisor

Image for Amazon Redshift Advisor

Amazon Redshift Advisor plays a pivotal role in optimizing costs and enhancing performance within Redshift clusters. By analyzing usage patterns and system metrics, it provides actionable recommendations to resize clusters based on actual workload demands, adjust query optimization strategies such as distribution keys and sort keys, and effectively utilize features like Concurrency Scaling. These insights ensure efficient resource allocation, minimize idle time, and proactively address performance bottlenecks, thereby maximizing ROI and operational efficiency in data warehouse operations.

7. Amazon Redshift Serverless

Amazon Redshift Serverless offers significant advantages in reducing costs through its efficient use of compute resources. By enabling users to access and analyze data without the need to manage traditional Redshift clusters, Serverless eliminates the overhead of provisioning and maintaining infrastructure. This capability is particularly beneficial for sporadic workloads where compute resources are only paid for when actively used, aligning costs directly with workload demands. Additionally, Redshift Serverless scales automatically based on workload requirements, preventing over-provisioning and ensuring optimal resource utilization. These features collectively reduce operational costs by minimizing idle time and enabling precise scaling, thereby maximizing cost-efficiency in data analytics and warehousing operations.[4]

Example Python code snippet for creating a Redshift Serverless endpoint:

import boto3

client = boto3.client('redshift-data')

response = client.create_cluster(


In this example, create_cluster method is used to provision a Redshift Serverless cluster named my-redshift-serverless-cluster.


In conclusion, optimizing AWS Redshift costs is essential for businesses leveraging its powerful data warehousing capabilities. By implementing strategies such as right-sizing clusters, leveraging Redshift Spectrum for cost-effective data querying, and utilizing features like Concurrency Scaling and Redshift Serverless, organizations can significantly reduce expenses while maintaining high performance. These approaches ensure that AWS Redshift remains a robust solution for managing large-scale data analytics efficiently, aligning costs with actual usage and maximizing return on investment in Big Data infrastructure.


1. Amazon Redshift - Market Share, Competitor Insights in Big Data Infrastructure

2. Cloud Data Warehouse – Amazon Redshift Pricing

3. Optimizing Price-to-performance for Amazon Redshift

4. Easy analytics and cost-optimization with Amazon Redshift Serverless | AWS Big Data Blog

5. Magellan Rx Case Study | Amazon Redshift | AWS

6. VOO Decreases TCO for Database Environments by 30%, Gains Unified Customer Insights Using Amazon Redshift


1. How to Save Costs in Redshift?

To save costs in Redshift, optimize your cluster size and configuration based on workload requirements. Use reserved instances for long-term usage and take advantage of Redshift Spectrum to query data directly in S3. Compress your data and use columnar storage formats like Parquet or ORC to reduce storage and query costs.

2. How Can I Make Redshift Faster?

Enhance Redshift performance by distributing data evenly across nodes, using appropriate distribution and sort keys. Regularly analyze and vacuum tables to remove deleted rows and reclaim space. Leverage workload management (WLM) to manage query queues effectively and use concurrency scaling for high-demand periods.

3. How Do I Use Redshift for Free?

You can use Redshift for free by taking advantage of AWS Free Tier offers, which provide a limited amount of free Redshift usage for new customers. Additionally, you can periodically review and delete unused resources, such as idle clusters and old snapshots, to minimize costs.

4. What is Redshift Optimized For?

Redshift is optimized for data warehousing and large-scale data analytics. It excels in handling complex queries on structured and semi-structured data, providing fast query performance and efficient data storage. Its integration with other AWS services enables seamless data ingestion and analysis workflows.

5. Is Redshift Cost Effective?

Redshift is cost-effective for large-scale data analytics due to its scalable architecture and cost-saving features like reserved instances and data compression. By optimizing resource usage and leveraging features like Redshift Spectrum, users can further reduce costs while maintaining high performance.

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