Breaking Down Amazon Redshift Pricing: A Comprehensive Guide for 2025

Introduction

Over the past decades, numerous technologies have evolved, and few have had as much impact on data engineering as cloud-based data warehouses. Today, we’re going to dive deep into the world of Amazon Redshift pricing—a subject that might seem intricate at first, but with the right approach, can be understood clearly. Whether you’re designing a new architecture or refining an existing one, this guide is intended to help you grasp the nuances of redshift pricing. By breaking down every pricing factor in a straightforward manner, you’ll be better equipped to optimize your Redshift deployments for both performance and budget.

What is Amazon Redshift?

Amazon Redshift is AWS’s fully managed, petabyte-scale data warehousing service. In simple terms, it’s designed to help you store, process, and analyze large volumes of data quickly and efficiently. Redshift leverages modern hardware and smart query optimization to run complex SQL queries across massive datasets. This makes it an ideal solution for businesses that need to transform raw data into actionable insights without getting bogged down by traditional data warehouse management tasks.

Beyond its core capabilities, Redshift integrates seamlessly with the broader AWS ecosystem. It offers the flexibility to scale storage and compute resources independently, automates backup and recovery processes, and even incorporates machine learning directly into your queries. This adaptability makes it a powerful tool for both startups and large enterprises, ensuring that every deployment is optimized for performance and cost control.

Before diving into the specific factors that determine redshift pricing, it’s important to recognize that the overall cost of an AWS Redshift deployment consists of multiple components. These include compute and storage costs, data transfer fees, concurrency scaling, and specialized services such as Redshift Spectrum and Redshift ML. In the sections that follow, each pricing element will be examined in detail, revealing how every factor influences redshift pricing.

For a deeper dive into how Amazon Redshift works and the benefits it offers, be sure to check out our other blog post dedicated to exploring what Amazon Redshift is all about.

1. AWS Redshift Pricing Models: Provisioned vs. Serverless

Provisioned Pricing

On-Demand Pricing:

With on-demand pricing, you pay for your Redshift cluster by the hour with no upfront commitments. Billing is done in one-second increments (after the initial billable state change), which means you’re charged only for the actual usage. The flexibility to pause your cluster and resume later can help you manage costs—especially for development or testing environments .

Reserved Instances:

For workloads with steady demand, Reserved Instances offer significant discounts over on-demand pricing by committing to a one- or three-year term. This option is ideal when your usage is predictable, ensuring you get more performance per dollar spent.

Redshift Serverless Pricing

Amazon Redshift Serverless is designed for those who prefer not to manage clusters directly. Instead of provisioning fixed capacity, you pay only for the compute capacity your data warehouse consumes when it’s active. Pricing can start as low as $3 per hour, and usage is measured in Redshift Processing Units (RPUs) billed on a per-second basis (with a 60-second minimum). As a reference, each RPU-hour costs $0.375. One of its major benefits is that the data warehouse automatically scales up or down to match analytics workload demands and shuts down during periods of inactivity, reducing both administration time and costs.

Several configurable settings help fine-tune performance and manage expenses:

• Base Capacity:
  • This setting specifies the minimum RPU capacity that is always available to serve queries. You can adjust the Base from 8 RPUs up to 512 RPUs in increments of 8. A higher base capacity can improve query performance—especially for data processing and ETL jobs that handle large volumes of data and complex transformations.
• Max RPU-Hours:
  • This option lets you set usage limits over a daily, weekly, or monthly period. By capping the compute usage, you can maintain greater control over costs.
• MaxRPU (Max Capacity):
  • This setting determines the maximum number of RPUs that Amazon Redshift Serverless can scale to during peak demand. A higher MaxRPU enhances query throughput when automatic scaling is triggered; however, once this limit is reached, the system will not scale up further.

Additional advantages include no charges for data warehouse startup time, and features like concurrency scaling and Redshift Spectrum are automatically included—there’s no need for separate payments for these services. Primary storage is billed as Redshift Managed Storage (RMS) at the same rates as with provisioned clusters, and you can restore your data warehouse to specific points within the last 24 hours at a 30-minute granularity free of charge. Data transfer and machine learning (ML) costs apply separately, following the same pricing structure as for provisioned clusters.

‍2. Node Types and Cluster Configuration

‍Your choice of node type plays a pivotal role in both performance and cost. Amazon Redshift offers two primary types:

• RA3 Nodes:
  • RA3 nodes bring a new level of flexibility by decoupling compute from storage. With Redshift Managed Storage (RMS), you pay only for the data you actually store, independent of compute capacity. This means you can scale your storage needs without the overhead of provisioning additional compute nodes, which can lead to significant cost savings  and .
• DC2 Nodes:
  • DC2 nodes combine compute and local SSD storage, making them ideal for smaller datasets (typically under 1 TB uncompressed) where you need maximum performance at a lower price.

Choosing between RA3 and DC2 depends on your workload. For rapidly growing datasets or when you need fine-tuned control over compute versus storage, RA3 nodes are usually the smarter investment.

‍3. Amazon Redshift Managed Storage Pricing

‍Managed Storage is a key feature of RA3 clusters that simplifies cost management by allowing you to pay a fixed rate for the data you store. With this model, you’re billed based on your actual data usage—measured in GB-months—at a fixed rate of $0.024 per GB. Pricing is calculated hourly based on the total data present, ensuring that you only pay for the storage you actually use without having to pre-provision extra capacity. This approach not only keeps costs aligned with actual usage but also eliminates data transfer fees between RA3 nodes and managed storage. However, note that Managed Storage charges do not include backup storage costs from automated or manual snapshots; these are billed separately. Additionally, if the cluster is terminated, charges for retaining manual backups will continue.

‍4. Amazon Redshift Spectrum Pricing

‍Redshift Spectrum extends your data warehouse’s reach by allowing you to run SQL queries directly against data stored in Amazon S3. With Spectrum, you pay only for the data scanned by your queries. Charges are calculated on a per-byte basis, rounded up to the next megabyte, with a 10 MB minimum per query. For example, scanning 10 gigabytes of data would cost approximately $0.05, while scanning 1 terabyte would cost about $5.00. Here’s what to keep in mind:

• Data Format Optimization:
  • Storing your data in compressed, partitioned, and columnar formats (such as Apache Parquet or ORC) can significantly reduce the amount of data that needs to be scanned, thereby lowering your costs.
• Additional S3 and Service Costs:
  • Since Spectrum queries operate on data stored in Amazon S3, standard S3 storage and request rates apply. Any charges from integrating AWS Glue (for the data catalog) or AWS KMS (for encryption) will also apply. Importantly, Data Definition Language (DDL) statements—like CREATE, ALTER, or DROP TABLE used for managing partitions—are not charged.
• Integration with Redshift Serverless:
  • When using Amazon Redshift Serverless, queries on external data in S3 are included within the overall RPU-hour billing, so there are no separate charges for Spectrum usage.

This pay-per-scan model ensures that you only pay for the exact amount of data processed, making it a cost-effective way to extend your analytics capabilities directly to your data stored in Amazon S3.

‍5. Concurrency Scaling

‍Amazon Redshift employs Concurrency Scaling to maintain fast query performance even under heavy loads. When the main cluster reaches capacity, Redshift automatically provisions additional transient clusters—without any extra cost for startup or shutdown times—to handle the increased demand.Each Redshift cluster earns up to one free hour of Concurrency Scaling credits per day (with the potential to accumulate up to 30 hours). These free credits usually cover most workloads. However, if your usage exceeds these credits, you’re charged at a per-second on-demand rate based on the type and number of nodes in your cluster, with a minimum charge of one minute each time a transient cluster is activated.

For example, consider a 10-node DC2.8XL cluster in the US-East region that costs $48 per hour. If you utilize two transient clusters for an extra five minutes beyond the free credits, the per-second rate is calculated as $48 divided by 3,600 seconds, or approximately $0.013 per second. Over 300 seconds (5 minutes), the cost for the two transient clusters would be 0.013 x 300 x 2 ≈ $8, bringing the total cost to around $56 for that period.

With Amazon Redshift Serverless, resource scaling is automatic and integrated into the overall pricing model, meaning there are no separate charges for Concurrency Scaling. This dynamic capability ensures that performance remains consistently high—even during unexpected spikes in query demand.

‍6. Redshift ML Pricing

‍Redshift ML integrates machine learning directly into your data warehouse, allowing you to build, train, and deploy models using simple SQL commands. New users benefit from a generous free tier: if you haven’t previously used Amazon SageMaker, you qualify for two free CREATE MODEL requests per month for the first two months—each handling up to 100,000 cells.

Each CREATE MODEL request incurs minimal Amazon S3 charges (typically under $1 per month) because S3 is used to store both the training data generated by the SELECT query and the model artifacts needed for prediction. With garbage collection enabled, these files are automatically removed after the model is created. Beyond the free tier, pricing for Redshift ML scales with the size of your training data, measured in the number of cells processed. To help control costs, you can set a MAX_CELLS limit. If you don’t specify one, the default is 1 million cells, which in most cases keeps training costs below $20. When the training data exceeds this limit, pricing increases in tiers as follows:

• First 10 million cells: $20 per million cells
• Next 90 million cells: $15 per million cells
• Over 100 million cells: $7 per million cells

For example, processing 2,000,000 cells would cost about $40, while processing 23,000,000 cells might cost approximately $395. If the training data produced by the CREATE MODEL request exceeds your MAX_CELLS limit, Redshift ML will randomly select a subset of records—roughly MAX_CELLS divided by the number of columns—to train the model, ensuring that the training cost remains within bounds and reducing the risk of bias.This tiered approach ensures you pay proportionately for the computational effort required, making Redshift ML a cost-effective way to incorporate advanced analytics into your data warehouse.

‍7. Additional Cost Factors

‍Several other elements can influence the overall cost of your Redshift deployment:

• Zero-ETL Integration Costs:
  • Amazon Redshift features zero-ETL integrations with popular OLTP databases and enterprise applications—including Amazon Aurora, Amazon DynamoDB, Amazon RDS for MySQL, Salesforce, ServiceNow, Zendesk, and more. These integrations allow you to automatically replicate specific data tables into Redshift, enabling unified analytics across multiple data sources. There is no extra fee for the integration itself; you only pay for the underlying resources, such as additional Redshift storage for replicated data, compute resources (or RPUs in Amazon Redshift Serverless) for processing the replication, and any applicable cross-AZ data transfer charges. The ongoing processing of data changes through these integrations is offered at no additional cost.
• Backup Storage:
  • Redshift automatically creates backups and snapshots of your data warehouse. While automated snapshots (created through Redshift’s snapshot scheduling feature) are free for up to 35 days, manual snapshots taken via the console, API, or CLI are billed separately. For Amazon Redshift Serverless, recovery points under 24 hours old are free, but those kept longer are charged as part of Redshift Managed Storage (RMS). In RA3 clusters, the primary data is billed at RMS rates, whereas manual snapshots are charged as backup storage at standard S3 rates. For dense compute (DC) and dense storage (DS) clusters, the storage for backups is maintained externally in Amazon S3, and any storage beyond the provisioned capacity is billed at regular S3 rates.
• Data Transfer:
  • Data transferred between Amazon Redshift and Amazon S3 within the same AWS region—used for backup, restore, load, and unload operations—is free of charge. However, data transfers across regions or outside AWS incur standard AWS data transfer rates. Additionally, if your Redshift cluster is hosted within an Amazon VPC, data transfers over JDBC/ODBC to the cluster endpoint are charged according to standard AWS data transfer fees. When using Enhanced VPC Routing to unload data to Amazon S3 in a different region, standard data transfer charges apply. Furthermore, Redshift charges for data sharing across regions and for snapshot copy operations. Specifically, data sharing is billed at $0.02 per GB for inbound transfers, while snapshot copy data transfer is billed at $0.02 per GB for outbound transfers from the source region.

By understanding these additional cost factors, you can better manage and predict the overall expenses associated with your Amazon Redshift deployment.

‍8. Amazon Redshift Free Trial

‍For those new to Amazon Redshift and interested in exploring its capabilities without an upfront investment, the Amazon Redshift Free Trial offers a compelling introduction. If you have never used Amazon Redshift Serverless before, you are eligible for a $300 credit, valid for 90 days, to cover compute and storage usage. The consumption rate for this credit depends on your actual usage and the compute capacity of your serverless endpoint. In regions where Amazon Redshift Serverless is not yet available, customers can take advantage of a free trial for provisioned clusters. Specifically, you’re eligible for a two-month free trial using DC2 large nodes, where your organization receives 750 free hours per month. This allowance is enough to continuously run one DC2 large node equipped with 160 GB of compressed SSD storage. Once the free trial period expires or if usage exceeds 750 hours per month, you have the flexibility to either shut down your cluster to avoid further charges or continue operating at the standard on-demand rate.

Conclusion

Understanding Amazon Redshift pricing is a multi-faceted process that involves evaluating a range of components—from computing capacity and node types to managed storage, Redshift Spectrum, concurrency scaling, and machine learning integration. Whether you opt for provisioned models with on-demand or reserved pricing, or choose the flexibility of AWS Redshift Serverless pricing, aligning your architecture with your performance requirements and budget constraints is key. Each element plays its part:

• Provisioned vs. Serverless Pricing: Offers options for predictable workloads or flexible, on-demand usage.
• Node Types and Cluster Configuration: Allow you to tailor performance and cost, with RA3 and DC2 nodes meeting different needs.
• Managed Storage and Spectrum Pricing: Ensure you pay only for the data you store and process, with cost reductions available through data format optimization.
• Concurrency Scaling and Redshift ML: Provide dynamic performance boosts and advanced analytics capabilities, scaling costs in line with usage.
• Additional Factors: Such as Zero-ETL integrations, backup storage, and data transfer charges also influence the overall bill.

Moreover, the Amazon Redshift Free Trial gives new users a risk-free opportunity to explore these capabilities, ensuring that you can test and optimize your data warehouse setup without an initial investment. By leveraging tools like the AWS Redshift pricing calculator and staying informed about the nuances of aws redshift pricing, you can create a cost-effective, scalable solution that drives actionable insights while keeping expenses under control. This comprehensive understanding empowers you to make informed decisions and fully harness the power of Amazon Redshift for your data-driven initiatives.