Amazon Redshift Pricing Models Explained
Amazon Redshift offers flexible pricing models tailored to different workloads. Here's what you need to know:
- Provisioned Clusters: Ideal for consistent, predictable workloads. Pricing starts at $0.543/hour. Reserved Instances (RIs) can save up to 75% for long-term commitments (1- or 3-year terms). Features like "pause and resume" help reduce costs during idle times.
- Redshift Serverless: Best for unpredictable or intermittent workloads. Charges are based on Redshift Processing Units (RPUs), starting at $1.50/hour for a 4-RPU minimum. It scales automatically and stops charging when idle.
- Storage Costs: Managed storage is priced at $0.024/GB per month for both models.
- Additional Costs: Spectrum queries cost $5/TB scanned, and Concurrency Scaling is free for most users (up to 30 hours/month).
Quick Comparison
| Feature | On-Demand (Provisioned) | Reserved Instances (Provisioned) | Redshift Serverless |
|---|---|---|---|
| Flexibility | High | Low | High |
| Cost Savings | None | Up to 75% | Up to 24% with reservations |
| Idle Costs | Billed unless paused | Billed full term | No charge when inactive |
| Billing Granularity | Hourly | Hourly (fixed term) | Per-second (60s minimum) |
| Best For | Ad-hoc/Dev-Test | Steady workloads | Variable workloads |
Selecting the right model depends on your workload patterns. For long-term, steady use, Reserved Instances offer the lowest costs. For unpredictable activity, Serverless is more efficient. Monitor additional charges like storage and Spectrum to avoid surprises.
Amazon Redshift Pricing Models Comparison: On-Demand vs Reserved Instances vs Serverless
On-Demand Pricing for Provisioned Clusters
How On-Demand Pricing Works
On-demand pricing operates on a pay-as-you-go basis, meaning there are no upfront costs or long-term commitments. You’re charged based on an hourly rate per node, multiplied by the number of nodes in your cluster. For instance, Amazon Redshift Provisioned pricing starts at $0.543 per hour, though the exact rate depends on the type of node you select.
The pricing model varies between node families:
- RA3 Nodes: Compute resources and managed storage are billed separately, so you can scale them independently.
- DC2 Nodes: The hourly rate covers both compute power and local SSD storage in a single package.
Another important detail: partial hours are billed by the second after any status change, such as creating, deleting, pausing, or resuming a cluster.
In multi-node clusters, only compute nodes are billed, as the leader node is free of charge. But if you use a Multi-AZ (Availability Zone) configuration, compute costs double since the cluster runs in two zones simultaneously.
Here’s a quick comparison of the two main node types:
| Node Type | Storage Type | Billing Structure | Best For |
|---|---|---|---|
| RA3 | Managed Storage (SSD + S3) | Node price + separate storage fee | Large datasets, dynamic storage needs |
| DC2 | Local NVMe SSD | All-inclusive hourly node price | High-performance workloads, small datasets (<1 TB) |
For small datasets under 1 TB (compressed), DC2 nodes are a good fit. If you’re dealing with larger or growing datasets, RA3 nodes offer scalable storage options.
This pricing flexibility also enables cost-saving features like pause and resume functionality.
Pause and Resume Functionality
The pause and resume feature is designed to help you save money on on-demand compute costs when your cluster isn’t in use. While the cluster is paused, compute charges drop to $0 per hour. However, backup storage fees - and, in the case of RA3 nodes, managed storage fees - still apply.
You can pause and resume clusters manually or schedule these actions. For example, you could pause a cluster every evening and resume it the next morning. This is especially useful for workloads that only run during business hours, as it eliminates overnight compute costs.
When to Use On-Demand Pricing
On-demand pricing is ideal for scenarios where flexibility is key. It works well for development and testing environments, where clusters may sit idle for long stretches. By pausing clusters during downtime, you can significantly reduce expenses.
This model also suits workloads with unpredictable demand. It allows for rapid deployment, scaling, and adjustments to node types or capacity. Features like Elastic Resize and the Resize Scheduler make it easy to add or remove nodes to handle fluctuating workloads.
That said, for continuous workloads, Reserved Instances might offer better cost savings over time.
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Reserved Instances for Provisioned Clusters
Reserved Instances (RIs) offer a billing discount for committing to a specific cluster setup over a set period. Unlike on-demand pricing, where you only pay when your cluster is active, RIs are billed hourly throughout the commitment term, regardless of whether the cluster is in use. This makes them unsuitable for workloads that vary greatly but perfect for production environments with consistent and predictable demand.
The discount applies only when your cluster's region, node type, and node count align with the reservation. Any additional nodes beyond the reservation are charged at on-demand rates. For RA3 nodes, the RI discount covers compute costs only, while managed storage is billed separately.
This setup works well for environments with steady workloads, making it worth exploring payment options and potential cost savings.
Commitment Terms and Payment Options
Reserved Instances come with 1-year or 3-year terms and offer three payment structures: No Upfront, Partial Upfront, and All Upfront:
- No Upfront: No initial payment; you pay a discounted monthly fee throughout the term.
- Partial Upfront: A portion of the cost is paid upfront, with the rest divided into monthly payments.
- All Upfront: The full cost is paid in one go, providing the highest discount.
No Upfront offers around 20% savings, while Partial and All Upfront options can lead to discounts ranging from 42% to 76%.
Cost Savings and ROI
The ROI potential of Reserved Instances becomes clear when compared to on-demand pricing. For example, opting for a 3-year All Upfront commitment can reduce costs by up to 76%.
| Payment Option | Upfront Cost | Monthly Charges | Savings vs. On-Demand | Best For |
|---|---|---|---|---|
| No Upfront | None | Yes | ~20% | Companies needing cash flow flexibility |
| Partial Upfront | Partial | Yes | 41% – 73% | Balancing upfront costs with significant discounts |
| All Upfront | Full | None | 42% – 76% | Long-term projects aiming for maximum cost savings |
These savings extend across all accounts under a single AWS payer account, thanks to consolidated billing, allowing multiple teams to share reserved capacity.
When to Use Reserved Instances
Reserved Instances are an excellent choice for consistent, steady-state production workloads. If you're running always-on systems like analytics dashboards, data pipelines, or reporting tools, RIs can significantly lower your annual costs.
Start with on-demand pricing during development and testing phases. Once your workload stabilizes in production, switch to Reserved Instances to lock in lower rates. AWS Cost Explorer can help identify the most cost-effective reservation levels based on your historical usage patterns.
Keep in mind that when a reservation term ends, your cluster automatically switches back to on-demand pricing unless a new reservation is purchased. If you need to adjust a reservation before the term expires, you’ll need to open a support case through the AWS Console.
Redshift Serverless Pricing
Redshift Serverless simplifies billing by charging only for the compute power you actually use. Instead of paying for continuously running nodes, costs are based on Redshift Processing Units (RPUs) consumed while your data warehouse processes queries. When there's no activity, the service automatically shuts down, and billing pauses entirely.
Each RPU includes 16 GB of memory, and charges are calculated per second, with a minimum of 60 seconds per active session. The standard rate in the US East (N. Virginia) region is about $0.375 per RPU-hour. Pricing also covers Spectrum and Concurrency Scaling, which are billed separately in provisioned clusters. Storage costs are handled separately as Redshift Managed Storage at approximately $0.024 per GB-month, the same rate as the RA3 provisioned model.
The following sections explore the technical details of this pricing structure.
Understanding RPU-Based Pricing
This model tracks actual compute usage rather than charging for reserved capacity. You can configure a base capacity between 8 and 512 RPUs (or 4 to 1,024 in some configurations), which sets your starting performance level. The system can scale up to a maximum of 5,632 RPUs if needed.
Billing begins when a transaction starts and ends when it’s completed. However, idle transactions can use resources for up to six hours. To manage costs, you can set "Maximum RPU hours" limits on a daily, weekly, or monthly basis. Even lightweight queries, like SELECT 1, trigger the 60-second minimum billing period, but you can adjust health-check intervals to minimize unnecessary charges.
Automatic Scaling Features
Redshift Serverless offers dynamic scaling that adjusts resources automatically based on workload demands. When queries become more complex or user activity spikes, the system scales RPUs in real time. As demand subsides, it scales down and shuts off completely during idle periods.
The system also learns from historical usage to better allocate resources. You can influence its behavior using a "Price-performance target" slider, which lets you prioritize between "Optimizes for cost", "Balanced", or "Optimizes for performance" options.
AWS specialists Ricardo Serafim and Milind Oke conducted benchmark tests in May 2024 using a TPC-DS 3TB dataset with 15 concurrent queries repeated 355 times (53,250 queries over 10 iterations). Results showed that the "Optimized for Performance" setting delivered queries 4× faster than the "Optimized for Cost" setting. However, this came with a 19.39% cost increase ($304.39 vs. $254.32 per test). Notably, no queries exceeded 10 minutes in the performance-optimized setup, compared to 204 queries in the cost-optimized configuration.
There’s no charge for the time it takes for the serverless warehouse to start up. Maintenance and patching are handled seamlessly in the background, so you don’t need to worry about downtime.
When to Choose Serverless
Redshift Serverless is ideal for workloads that are unpredictable or occur intermittently. It’s a great option for development and testing environments, departmental data warehouses, or systems that generate periodic reports. For small teams, it removes the hassle of managing infrastructure, choosing node types, or manually scaling capacity. Unlike provisioned clusters, Serverless automatically shuts down when not in use.
The recently introduced 4-RPU minimum configuration (about $1.50 per hour of active workload time) makes it accessible for smaller datasets and entry-level analytics. This setup supports up to 32 TB of managed storage and 64 GB of memory. However, for steady workloads with predictable patterns, provisioned Reserved Instances might offer lower costs, thanks to long-term discounts ranging from 41% to 76%. For consistent serverless usage, Serverless Reservations can reduce compute costs by up to 24% compared to on-demand rates.
To keep track of your costs, use the SYS_SERVERLESS_USAGE system table. This provides detailed insights into RPU consumption per query, helping you identify ways to optimize usage and stay within your budget.
Additional Redshift Costs to Consider
When budgeting for Amazon Redshift, compute charges are just one part of the equation. Several additional cost factors can influence your final bill. Understanding these components is key to avoiding unexpected expenses and creating accurate financial forecasts.
Managed Storage Pricing
With RA3 node types and Redshift Serverless, Redshift Managed Storage (RMS) enables independent scaling of compute and storage. RMS uses high-performance SSDs for frequently accessed data (often referred to as "hot" data) while automatically transferring less-used ("cold") data to Amazon S3 for long-term durability. Managed storage is priced at $0.024 per GB per month, whether your data resides on SSDs or S3. There are no extra charges for data transfers between RA3 nodes and managed storage.
To estimate monthly storage costs, use this formula:
(Total GB-Hours used in a month / Total hours in that month) * Regional GB-Month Rate.
You can monitor storage usage through CloudWatch or the AWS Console to keep tabs on your spending.
Spectrum, Concurrency Scaling, and ML Costs
Redshift Spectrum allows you to query data directly from Amazon S3 at a cost of $5 per terabyte (TB) of data scanned. The minimum charge per query is 10 MB. For provisioned clusters, Spectrum and Concurrency Scaling are billed separately, but these features are included in the base RPU-hour pricing for Redshift Serverless without extra fees.
To reduce Spectrum costs, consider using columnar data formats like Apache Parquet or ORC. For example, querying one column in a 100-column Parquet file costs just 1/100th of querying the same data in a text file. Additionally, applying GZIP compression and partitioning your data in S3 can significantly lower scanning costs.
For Concurrency Scaling in provisioned clusters, AWS provides one free hour of credits for every 24 hours of activity, with a maximum of 30 free hours. Most customers (97%, according to AWS) find this free tier sufficient. If you exceed these credits, charges are applied per second based on your cluster's node type, with a one-minute minimum per activation.
Redshift ML training costs depend on your dataset size, measured in "cells" (rows × columns). Pricing is tiered:
- The first 10 million cells cost $20 per million cells.
- The next 90 million cells cost $15 per million cells.
- Beyond 100 million cells, the rate drops to $7 per million cells.
The default MAX_CELLS parameter is set to 1 million, keeping most training costs under $20. Remember to account for additional charges such as Amazon S3 rates for storing training data and artifacts, as well as Amazon SageMaker fees once the AWS Free Tier is exceeded.
Backup storage is free up to 100% of your provisioned storage size for active clusters. Any backups exceeding this limit - or those retained after cluster termination - are billed at standard Amazon S3 rates.
Estimating Total Costs
To calculate your total costs, combine compute (on-demand, reserved, or serverless) and storage charges, then factor in Spectrum, Concurrency Scaling, and ML training expenses. Use techniques like columnar data formats and compression to minimize Spectrum costs. For ML, apply tiered pricing to estimate training costs based on your dataset size.
While most users stay within the free tier for Concurrency Scaling, it’s wise to account for potential overages if you expect heavy concurrent workloads. Tools like the AWS Pricing Calculator can help you build detailed cost estimates, and usage tracking via SYS_SERVERLESS_USAGE or CloudWatch can refine your predictions. These steps will help you compare Redshift pricing models effectively and plan your budget with confidence.
Comparing Amazon Redshift Pricing Models
Choosing the right pricing model can make a big difference in managing AWS costs effectively. Each option comes with its own advantages, tailored to different workload patterns and needs.
Pricing Model Comparison Table
Amazon Redshift offers three main pricing models: on-demand (provisioned), reserved instances (provisioned), and serverless. Here's how they stack up across key features:
| Feature | On-Demand (Provisioned) | Reserved Instances (Provisioned) | Redshift Serverless |
|---|---|---|---|
| Flexibility | High (No commitment) | Low (1- or 3-year term) | Optimal (Automatic scaling) |
| Cost Savings | None (Baseline) | 20% to 76% discount | Up to 24% with Serverless RIs |
| Workload Suitability | Ad-hoc/Dev-Test | Steady Production | Variable workloads |
| Billing Granularity | Hourly (per-second after 1st min) | Hourly (Fixed term) | Per-second (60s minimum) |
| Management Overhead | Requires active management | Requires active management | Fully automated |
| Idle Costs | Billed continuously unless paused | Billed for full term | No charge when inactive |
To illustrate, let's break down the monthly cost for a 4-node RA3.xlplus cluster with 40 TB of storage in the US East (N. Virginia) region. Using a compute cost of $1.086 per hour per node and $0.024 per GB-month for storage, here's how the numbers look:
| Pricing Model | Compute Cost (Monthly) | Storage Cost (40 TB) | Total Estimated Monthly Cost |
|---|---|---|---|
| On-Demand | $3,171.12 | $983.04 | $4,154.16 |
| 1-Yr RI (No Upfront ~20% off) | $2,536.90 | $983.04 | $3,519.94 |
| 3-Yr RI (All Upfront ~75% off) | $792.78 | $983.04 | $1,775.82 |
| Serverless (Variable) | Based on RPU usage | $983.04 | Varies by activity |
These figures underscore how aligning your workload needs with the right pricing model can lead to significant savings.
Key Factors to Consider
While the table provides a snapshot of the differences, there are a few critical factors to weigh when deciding on a pricing model.
Workload predictability plays a major role. Reserved instances offer big discounts - up to 76% for three-year all-upfront commitments - but are only worth it if you can accurately predict your capacity requirements. For unpredictable workloads, serverless is often a better fit as it eliminates the need for manual scaling and only charges when the database is active.
Budget and commitment levels are also important. On-demand pricing is ideal for short-term projects, such as development or proof-of-concept environments, since it requires no upfront investment. Once your usage patterns stabilize, switching to reserved instances can help lock in lower costs over time.
Management overhead is another key consideration. Serverless takes the hassle out of managing clusters, automatically scaling resources and shutting down during idle periods. Provisioned models, on the other hand, require ongoing management. For example, Schneider Electric uses RA3 nodes with concurrency scaling to handle thousands of users, demonstrating how well-managed provisioned clusters can meet demanding workloads.
For workloads with intermittent or unpredictable activity, serverless shines by charging only when the database is in use. However, for round-the-clock production environments, reserved instances typically offer the most cost-effective solution, especially with multi-year commitments.
How Opsima Optimizes AWS Costs for Redshift
Managing Redshift commitments manually can be a time-consuming task, often leading to over-commitment or missed savings opportunities. Opsima takes the hassle out of this process by automating cost management for Redshift. It ensures your workloads are covered by the most cost-efficient pricing strategy, eliminating the need for constant manual adjustments. This directly addresses the inefficiencies that can drive up costs.
Key Features of Opsima
Opsima’s automated commitment management continuously monitors your Redshift usage and dynamically selects the best mix of Reserved Instances and Savings Plans. It works seamlessly with both RA3 clusters and Redshift Serverless, adjusting in real time as your workloads change. This is particularly important because Redshift reservations incur costs whether your cluster is active or paused.
The platform also provides detailed cost visibility by breaking down Redshift expenses across accounts, environments, teams, applications, and regions. This granular insight makes it easier to pinpoint cost drivers and allocate expenses accurately. Additionally, real-time anomaly detection alerts you to unexpected spending spikes, helping you address issues before they escalate.
By optimizing costs directly at the billing level, Opsima ensures savings without requiring changes to your infrastructure or impacting query performance and data security.
Benefits of Using Opsima for Redshift
Opsima simplifies Redshift cost management by automatically handling the complex layers of reservations, ensuring you always pay the lowest rates. This is especially beneficial for Redshift Serverless, where RPU-based reservations can be shared across multiple workgroups and linked accounts at the payer account level.
The platform’s flexible approach to commitment management eliminates the risk of being locked into 1-year or 3-year contracts that don’t align with your actual usage. As your needs evolve, Opsima rebalances your coverage to capture savings that would otherwise require constant manual oversight. With the potential to cut AWS bills by up to 40%, and support for major services like EC2, RDS, and Lambda, Opsima streamlines cost optimization across your entire AWS environment.
Conclusion
Amazon Redshift provides three pricing options tailored to different workload needs. On-Demand Provisioned clusters offer flexibility without long-term commitments, Reserved Instances provide cost savings for predictable workloads, and Redshift Serverless automatically scales and charges only for active usage.
Choosing the right pricing model depends on understanding your workload patterns and administrative preferences. If your analytics run consistently with predictable capacity, Reserved Instances can offer the lowest overall cost. For irregular or unpredictable workloads, Serverless is ideal, as it eliminates idle costs by scaling on demand. Meanwhile, On-Demand is a good option when you're still observing usage patterns or need short-term flexibility.
It’s important to monitor additional costs, such as those from storage, Spectrum, and Concurrency Scaling, as they can accumulate quickly without proper oversight. Serverless environments, in particular, can incur unexpected compute charges from lightweight health-check queries or similar activities.
Managing these pricing layers manually - tracking usage, purchasing reservations, and adjusting commitments as needs evolve - can be time-consuming. Opsima’s automated commitment management simplifies this process by continuously optimizing your Reserved Instances and Savings Plans across Redshift and other AWS services. This ensures you’re always paying the lowest rate possible without requiring infrastructure changes or manual adjustments.
Ultimately, whether you’re using RA3 clusters or Redshift Serverless, financial efficiency comes down to aligning your pricing model with actual usage patterns. Leveraging automation tools like Opsima helps prevent overspending on idle resources and ensures you secure the best reservations for your needs. This balance not only reduces costs but also sets the stage for smarter cost management moving forward.
FAQs
What’s the difference between Amazon Redshift Serverless pricing and provisioned cluster pricing?
Amazon Redshift Serverless uses a pay-as-you-go model, where you're charged only for the compute capacity you actually use, measured down to the second. This means you only pay when queries are being processed, and there are no costs during idle periods.
On the other hand, provisioned clusters operate on an hourly billing model. Here, you're charged for the resources you allocate - like compute nodes and storage - whether or not they're being fully utilized. While this approach offers cost predictability, it can lead to higher expenses if resources sit idle.
How can Reserved Instances help save costs with Amazon Redshift?
Reserved Instances for Amazon Redshift offer a great way to save on costs by providing discounted rates when you commit to a one-year or three-year term. This pricing model works well for businesses with steady, predictable workloads, as it helps cut expenses compared to on-demand pricing.
By reserving instances, you can better control your cloud spending without sacrificing performance or scalability. It's an excellent choice for organizations aiming to handle long-term data analytics requirements more efficiently.
How can I avoid unexpected charges with Amazon Redshift pricing?
When managing costs with Amazon Redshift, consider using Reserved Instances. These can offer big savings compared to on-demand pricing, making them a great choice for consistent workloads. On the other hand, if your workload varies a lot, the serverless option could be more cost-effective since you only pay for the resources you actually use.
It's also a good idea to keep an eye on your capacity settings, like maximum RPU hours, to ensure they match your budget. Regularly checking your usage and adjusting resources as needed can help you balance costs while maintaining strong performance.
