How Amazon Redshift Pricing Works: The Ultimate Guide

Choosing a cloud data warehouse (CDW) in today's data-driven environment is a strategic decision with significant financial implications. Among the leading contenders, Amazon Redshift stands out for its ability to scale efficiently and process petabyte-level datasets. However, maximizing the cost-effectiveness of Redshift deployments requires a thorough understanding of its complex pricing structure.

Navigating the Redshift pricing can be challenging. Per-hour charges, storage fees, and the choice between serverless and on-demand options create a complex cost landscape. This blog post provides a clear and comprehensive roadmap to demystifying Redshift's billing structure and optimizing your spending patterns.

We will delve into the key components of Redshift pricing, from hourly compute costs to storage fees and additional charges. We will then explore various optimization strategies, including rightsizing clusters, leveraging reserved instances, and utilizing serverless effectively. By the end of this exploration, you will be equipped with the knowledge and tools to make informed decisions that maximize the value of your Redshift investments while minimizing unnecessary costs.

What is Amazon Redshift?

Amazon Redshift is a fully managed, petabyte-scale data warehouse service offered by Amazon Web Services (AWS). It is designed to efficiently store, manage, and analyze large datasets for business intelligence (BI) and analytics workloads.

Key characteristics of Amazon Redshift:

• Scalability: It can elastically scale storage and compute resources to handle varying data volumes and query demands.
• Performance: It optimizes query performance through massively parallel processing (MPP) architecture and columnar storage format.
• Cost-effectiveness: It offers pay-as-you-go pricing, allowing organizations to scale resources based on their needs and avoid upfront costs.
• Ease of use: It is fully managed, eliminating the need for infrastructure provisioning and management.
• Integration: It integrates seamlessly with other AWS services and supports various BI and analytics tools.

The most important architectural components of Amazon Redshift include the following:

• Nodes: Redshift clusters consist of compute nodes that perform data processing and query execution. These nodes are powered by Amazon's custom-designed hardware optimized for data warehousing.
• Leader node: The leader node manages cluster metadata, coordinates workloads, and distributes tasks among compute nodes.
• Storage: Data is stored in a distributed fashion across the compute nodes on high-performance Amazon Elastic Block Store (EBS) volumes.
• Columnar storage: Data is stored in columns rather than rows, enabling faster retrieval and filtering based on specific data points.
• Massively parallel processing (MPP): Queries are divided and processed in parallel across multiple compute nodes, significantly increasing performance for large datasets.
• Data loading and ingestion: Data can be loaded from various sources like relational databases, flat files, and streaming services using tools like Redshift Spectrum and Amazon S3.

Understanding Amazon Redshift Pricing Components

Amazon Redshift utilizes a multi-faceted pricing structure with various elements. It is crucial to break down these components into distinct categories to gain a clear understanding of the charges involved. In the next few sections we will touch upon each of these pricing components in detail.

Compute Costs

Choosing the right compute configuration for your Redshift data warehouse is a balancing act between power and budget. Let's explore the key aspects and their associated costs:

Node Types and Sizes

• Dense Compute (DC2): These nodes, starting at $0.25 per hour for a dc2.large (2 vCPUs), prioritize processing speed and are ideal for intensive analytics requiring rapid data manipulation.
• Dense Storage (DS2): Starting at $0.85 per hour for a ds2.xlarge (4 vCPUs), these nodes offer cost-effective data warehousing for large datasets. While processing power is slower than DC2, they're perfect for scenarios where data access is less frequent but storage capacity is paramount.
• RA3 with Redshift Managed Storage: These hybrid nodes, starting at $1.086 per hour for an ra3.xlplus, blend SSDs for frequently accessed "hot" data with S3 for cold data, creating a cost-effective solution for diverse access patterns. RA3 nodes offer separate scaling for compute and storage, allowing for granular cost control based on your workload needs.

On-Demand vs. Reserved Instances

On-demand instances provide unparalleled flexibility, allowing you to scale your cluster up or down instantly to meet fluctuating workload demands. However, this convenience comes at a cost, with charges accumulating for every hour a node is active. Reserved instances offer significant cost savings (up to 75%) through upfront commitments for specific node types and sizes over predefined durations. This approach is ideal for predictable workloads where sustained resource utilization is expected.

The pause and resume capability in Redshift for on-demand nodes gives users more control over costs by allowing them to temporarily stop billing for cluster nodes when they are not in use. This feature can be invoked manually or scheduled in advance.

Important points to keep in mind for Redshift compute costs::

• Hourly charges accumulate for each active node, even for partial hours.
• Costs vary based on node type, size, and reserved instances versus on-demand pricing.
• For RA3 nodes, the data stored in managed storage is billed separately.
• Data transfer charges can apply when moving data between nodes or regions.

Redshift Serverless Pricing

Redshift Serverless introduces a dynamic pricing model that breaks free from the traditional hourly charges for provisioned clusters. With a starting price of $3 per hour, you pay only for the compute capacity your data warehouse consumes when it's actively processing queries. With serverless you don’t need to pay for automatic scaling, concurrency scaling, and Redshift Spectrum as these services are included in the serverless pricing. This pay-per-use approach makes Serverless ideal for infrequent or unpredictable workloads, offering significant cost savings compared to persistent clusters.

Understanding the Serverless Costs:

• Redshift Processing Units (RPUs): Serverless uses RPUs to measure data warehouse capacity. Your charges are based on RPU-hours, calculated on a per-second basis (with a minimum of 60 seconds). Prices start from $0.36 per RPU-hour with a minimum base data warehouse capacity setting of 8 RPUs. This granular billing ensures you only pay for the resources actually utilized during query execution.
• Storage: Redshift Managed Storage (RMS) charges apply for primary storage, and standard backup rates for user snapshots. Storage rates are similar to provisioned clusters.
• Data Transfer and ML: Data transfer and ML costs apply separately, just like with provisioned clusters.
• Snapshot Replication and Data Sharing: These features incur transfer charges based on the data movement involved.

Storage Costs

Amazon Redshift uses two types of storage - managed storage for the actual cluster data, and backup storage for snapshots etc.

Redshift Managed Storage (RMS)

Redshift Managed Storage is only available for RA3 node clusters. You pay a fixed rate per GB per month for this managed storage. This covers all the data residing on the nodes but doesn’t include charges for any snapshots or backups. Usage is calculated hourly based on total data size. Rates vary by region and start at $0.024 per GB-month for data stored in the US East (Ohio) region. As mentioned earlier, Redshift Serverless also uses RMS,

Backup Storage

Backup storage refers to manual snapshots and retained automated snapshots. You are charged standard S3 rates for manual snapshots created via API/CLI. Redshift offers free automated snapshots of your data for 35 days. Beyond that, they incur the standard S3 charges.

With Dense Compute and Dense Storage nodes, storage is included. But backups still use S3 and are charged separately. Snapshots continue to be billed until they expire or are deleted.

For RA3 clusters, managed storage and backup storage are billed independently based on usage. So a 10TB cluster with 30TB of snapshots would entail 10TB of managed storage charges and 30TB of backup storage charges.

Additional Charges

Redshift Spectrum Pricing

Redshift Spectrum allows running SQL queries against exabytes of data stored in Amazon S3. You are charged based on the volume of data scanned per query, rounded up to the next MB (minimum 10MB per query).

Pricing is $0.005 per MB scanned. So a query scanning 10GB would cost $0.05, while a 1TB scan would be $5. There are no charges for DDL statements to manage external tables or failed queries. You can optimize Spectrum costs by compressing and partitioning data in columnar formats like Parquet. This reduces scanned data volume.

With Redshift Serverless, Spectrum queries are included in the overall serverless pricing based on credits used.

Additional charges beyond data scanning:

• Redshift cluster charges if used to run Spectrum queries
• S3 storage charges for underlying data
• S3 request charges for bucket access
• AWS Glue Data Catalog charges if used for table metadata
• KMS charges for encrypted S3 data

Concurrency Scaling Pricing

Amazon Redshift Concurrency Scaling allows your cluster to handle spikes in concurrent users and queries by automatically adding extra capacity as needed. This temporary scaling happens without you having to manually provision or manage any additional resources.

You get 1 hour of free Concurrency Scaling cluster credits for every 24 hours your main cluster is running. These credits can accumulate up to 30 hours. They are earned hourly and can only be used by the same cluster.

Beyond the free credits, you are charged per second of excess usage. The pricing starts from $0.0003 for a ra3.xplus node and varies with the node type and size. Minimum billing is 1 minute each time a Concurrency Scaling cluster activates. There are no charges for the starting up or shutting down of transient clusters. You only pay for excess concurrent usage beyond credits.

For Redshift Serverless, concurrency scaling is included by default at no extra cost. The serverless model automatically provides resources to match workload needs.

Redshift ML Pricing

Amazon Redshift ML is a feature that allows creating and applying machine learning models directly within Amazon Redshift using standard SQL, without needing to move data out of the data warehouse.

When you first use Redshift ML, you qualify for the Amazon SageMaker free tier if not used before. This includes 2 free CREATE MODEL requests per month for 2 months, up to 100,000 cells per request.

Small S3 charges apply for model artifacts - typically less than $1/month. Garbage collection removes these after model creation.

Pricing:

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

Zero-ETL integration Costs

The zero-ETL integration between Redshift and Aurora enables real-time analytics without complex ETL pipelines. There are no additional fees for this capability.

You only pay for the existing Aurora and Redshift resources used - such as storage, I/O, and data transfer. The initial snapshot export and ongoing change data replication do not incur charges beyond the underlying resource usage.

Data Transfer Costs

Moving data between Redshift and S3 in the same region is free for backup, restore, loading, and unloading workflows. This enables efficiently getting large datasets into and out of your data warehouse without transfer fees.

However, all other Redshift data transfers are charged at standard AWS rates. For example, if your Redshift cluster is in a VPC, any data transferred over JDBC or ODBC connections to the cluster endpoint will incur fees. Similarly, unloading data to S3 buckets in another region using Enhanced VPC Routing results in cross-region charges.

Data sharing with other accounts and regions is billed based on volume in the region accessing the shared data. And snapshot copy across regions is charged in the source region.

Optimizing Redshift Cost-Effectiveness

Beyond understanding the pricing structure, maximizing the cost-effectiveness of Redshift deployments requires careful resource optimization. This section will delve into three key strategies to ensure your data warehouse operates efficiently and economically:

Rightsizing Clusters

Choosing the optimal cluster configuration is crucial for balancing power and cost. Consider the following factors:

• Workloads: Analyze your workload characteristics, including data volume, query demands, and concurrency needs. Dense Compute nodes may be ideal for intensive analytics, while Dense Storage nodes offer cost-effective scalability for large datasets with less frequent access.
• Resource Utilization: Monitor cluster utilization to identify idle periods and potential for downsizing. Leverage Redshift's scaling capabilities to adjust node size or number based on real-time demands.
• Reserved Instances: For predictable workloads, reserved instances offer significant cost savings by committing to specific nodes for defined durations. Carefully calculate your anticipated resource needs to make informed reservation decisions.

Leveraging Serverless

Redshift Serverless introduces a pay-per-use model, ideal for infrequent or unpredictable workloads. You only pay for the compute resources consumed during query execution, potentially avoiding unnecessary charges for persistent clusters. However, it's crucial to optimize queries for efficient data processing to minimize RPU-hours and associated costs.

Implementing Best Practices

Various techniques can further improve your cost efficiency:

• Query Optimization: Analyze query execution plans and identify bottlenecks. Optimize data filtering, indexing, and join strategies to minimize resource consumption.
• Data Partitioning: Partitioning tables based on logical criteria allows you to focus queries on specific subsets of data, reducing the amount of data scanned and associated charges.
• Cost Monitoring and Alerts: Utilize Redshift's cost management tools to track expenses and set budget alerts. This proactive approach allows you to identify potential cost spikes and take corrective actions promptly.

By implementing these strategies and embracing an optimization mindset, you can ensure your Redshift deployment delivers efficient and cost-effective data analytics capabilities, supporting your data-driven decisions without exceeding budgetary constraints.

Conclusion

Understanding the multifaceted pricing structure of Amazon Redshift is crucial for maximizing the cost-effectiveness of this cloud data warehouse. This guide has comprehensively explored the key components of Redshift pricing, from compute costs and storage fees to serverless pricing and additional charges. We have also provided insights into various optimization strategies, such as rightsizing clusters, leveraging reserved instances, and utilizing serverless effectively.

Equipped with this knowledge and practical recommendations, you can now make informed decisions that align your Redshift deployment with your specific workload and budget. By carefully managing and optimizing resources, you can unlock the value of Redshift for data-driven insights while minimizing unnecessary costs. For more information about Redshift pricing check out Redshift pricing page and their cost model.

Once you have decided to use Redshift, you might want to learn about how to query the Redshift data or more advanced users might be interested in learning about how build Customer 360 using Redshift and RudderStack Profiles.