Key Points

• RDS with PostgreSQL is cost-effective for general-purpose databases, while Redshift seems likely to be more economical for large-scale data analytics due to lower storage costs.
• Use cases is using RDS for transactional applications and Redshift for analytical queries on big datasets, with costs varying by workload.
• An unexpected detail is that Redshift's compute costs per hour can be higher, but its storage is significantly cheaper, affecting total cost based on data size.

Pricing Comparison

RDS with PostgreSQL pricing includes compute costs (e.g., $0.384/hour for a db.m5.xlarge instance) and storage costs (e.g., $0.125/GB-month for gp2). For example, a 1 TB setup might cost around $405.32 monthly.

Redshift offers on-demand nodes (e.g., $1.30/hour for ds2.xlarge) and serverless options ($0.375/RPU-hour), with storage at $0.024/GB-month. For 1 TB, a ds2.xlarge setup might cost $973 monthly, while serverless could reach $1,119 for similar compute.

When to Use Each

Use RDS with PostgreSQL for applications needing traditional relational database features, like transactions. Opt for Redshift for data warehousing and complex analytical queries on large datasets, leveraging its optimized architecture.

Amazon Web Services (AWS) offers robust solutions for database management, with Amazon Relational Database Service (RDS) supporting PostgreSQL and Amazon Redshift catering to data warehousing needs. This note provides a comprehensive analysis of their expected pricing and guidance on when to use each, based on current understanding as of February 26, 2025.

Introduction to the Services

Amazon RDS is a managed relational database service that supports multiple engines, including PostgreSQL, ideal for applications requiring traditional relational database functionalities such as ACID compliance, transactions, and complex SQL queries. It automates administrative tasks like hardware provisioning, database setup, patching, and backups, allowing users to focus on application development.

In contrast, Amazon Redshift is a fully managed data warehouse service designed for large-scale data analytics, capable of handling petabyte-scale datasets. It uses a columnar storage format, derived from PostgreSQL, but optimized for online analytical processing (OLAP) and business intelligence (BI) applications, excelling in complex queries against large datasets.

Pricing Models and Comparison

To compare expected prices, we examine both compute and storage costs, acknowledging the complexity due to varying workload requirements.

RDS with PostgreSQL Pricing

• Compute Costs: RDS instances are priced per hour based on instance type. For example, a db.m5.xlarge instance in US East (N. Virginia) costs $0.384 per hour, billed in 1-second increments with a 10-minute minimum.
• Storage Costs: Storage options include General Purpose SSD (gp2) at $0.125 per GB-month and Provisioned IOPS SSD (io1) at $0.125 per GB-month plus $0.10 per IOPS-month. For Multi-AZ deployments, storage costs effectively double due to dual instances.
• Example Calculation: For a db.m5.xlarge with 1 TB gp2 storage:
  • Monthly compute: 730 hours * $0.384/hour ≈ $280.32
  • Monthly storage: 1000 GB * $0.125/GB-month = $125
  • Total: Approximately $405.32 per month
• For io1 with 3000 IOPS, storage cost is $125 + $300 = $425, total ≈ $705.32, highlighting performance impact on costs.

Additional costs include backup storage, snapshot exports ($0.013/GB), and data transfer, with options for Reserved Instances offering 1- or 3-year terms for potential savings.

Redshift Pricing

• Compute Costs: Redshift offers on-demand pricing for nodes (e.g., ds2.xlarge at $1.30/hour) and a serverless option. Serverless pricing is based on Redshift Processing Units (RPUs), with each RPU at $0.375 per hour, starting at $3/hour for 8 RPUs, scaling in 8-unit increments.
• Storage Costs: Redshift Managed Storage (RMS) is priced at $0.024 per GB-month in US East (N. Virginia), with no additional charge for data transfer between nodes and storage.
• Additional Features: Concurrency Scaling offers 1 hour free credits per day, with excess usage at on-demand rates. Redshift Spectrum charges $5/TB scanned, and Redshift ML has a free tier for initial use, with costs escalating based on cell counts.
• Example Calculation for On-Demand Node: For ds2.xlarge with 1 TB storage:
  • Monthly compute: 730 hours * $1.30/hour ≈ $949
  • Monthly storage: 1000 GB * $0.024/GB-month = $24
  • Total: Approximately $973 per month
• Serverless Example with 4 RPUs: Cost per hour is $1.50 (4 * $0.375), monthly compute ≈ $1,095, total with storage ≈ $1,119, noting automatic scaling benefits for variable workloads.

Redshift also offers Reserved Instances for 1- or 3-year terms, potentially reducing costs, and zero-ETL integrations without additional fees, leveraging existing resource pricing.

Comparative Analysis

Comparing the examples, RDS with PostgreSQL at $405.32 for a similar compute setup appears cheaper than Redshift's $973 for on-demand or $1,119 for serverless. However, this comparison is nuanced:

• Storage Cost Advantage: Redshift's $0.024/GB-month versus RDS's $0.125/GB-month makes it significantly cheaper for large datasets, e.g., 1 TB storage costs $24 vs. $125.
• Compute Cost Disparity: Redshift's higher per-hour rates (e.g., $1.30 vs. $0.384) suggest higher initial compute costs, but its optimization for analytics may reduce total compute hours needed for certain workloads.
• Workload Impact: For data-intensive analytical queries, Redshift's architecture (columnar storage, optimized query engine) may offer better performance, potentially offsetting higher compute costs with fewer resources needed.

Research suggests that for large data sets, Redshift's lower storage costs and analytical efficiency make it more cost-effective overall, while RDS is preferable for general-purpose, transactional workloads where storage cost differences are less impactful.

When to Use Each

The choice between RDS with PostgreSQL and Redshift depends on the application's nature:

• RDS with PostgreSQL: Ideal for transactional applications requiring ACID compliance, such as e-commerce platforms, CRM systems, or any application needing a traditional relational database. It's suitable for smaller datasets where storage cost differences are minimal, and for users needing flexibility with multiple database engines (e.g., MySQL, Oracle).
• Redshift: Best for data warehousing and BI applications, such as analyzing sales data, customer behavior, or financial reports across large datasets. It's optimized for OLAP workloads, offering superior performance for complex queries, and leverages lower storage costs for big data, making it cost-effective for analytics-focused organizations.

Considerations and Unexpected Details

An unexpected detail is that despite Redshift's higher compute costs per hour, its storage pricing at $0.024/GB-month is notably lower than RDS, potentially making it more economical for organizations with vast data storage needs. This contrasts with initial expectations where compute costs might dominate, highlighting the importance of data size in cost calculations.

Additionally, Redshift's serverless option offers flexibility for variable workloads, potentially reducing costs compared to fixed nodes, but requires careful monitoring to ensure cost control, especially with automatic scaling.

Tables for Clarity

Below is a table summarizing key pricing components:

Another table for use case guidance:

Conclusion

In conclusion, while RDS with PostgreSQL is more cost-effective for general-purpose relational databases, especially for transactional workloads, Redshift offers better value for large-scale data analytics due to its lower storage costs and optimized architecture. Organizations should assess their data size, query patterns, and application needs to determine the most suitable and economical choice, using tools like the AWS Pricing Calculator for precise estimates.

This analysis, current as of February 26, 2025.