Cost-cutting strategies for Amazon Redshift

Amazon Redshift is a fully managed cloud data warehouse built for fast, cost-efficient analysis of large datasets. It handles structured and semi-structured data with optimized SQL execution and seamless integration with BI tools.

While designed for performance and low total cost of ownership, Redshift costs can vary depending on configuration, region, query patterns, and storage requirements. This article examines the factors that frequently lead to high usage costs and offers cost-cutting strategies to help you optimize the value of your Redshift warehouse.

Redshift pricing unpacked

AWS offers several pricing options for Redshift, based on a pay-as-you-go model. Which pricing option you choose depends on how consistent your workloads are, how much flexibility you need, and how quickly your data is growing.

In addition to information available on AWS’s pricing page, CloudZero provides a fuller description of the following simplified pricing options:

• Provisioned (on-demand vs reserved)
• Serverless
• Concurrency Scaling and Redshift Spectrum
• DC2 vs RA3 nodes and Redshift Managed Storage

Provisioned (on-demand vs reserved instances)

On-demand pricing - With AWS on-demand pricing, you pay hourly per node, with flexibility to modify or pause clusters as workloads shift. Pausing suspends compute charges but retains backup storage costs. Although this is the most flexible pricing model, it can be the most costly.

Reserved instances - Reserved instances offer up to 75% savings for one- or three-year commitments. This option requires accurate forecasting to avoid overpaying for unused capacity or experiencing performance issues due to overcapacity.

Serverless

Redshift Serverless - Redshift Serverless bills per second (with a 60-second minimum) based on active Redshift Processing Units (RPUs). It incurs no charges during idle time, making it ideal for dynamic workloads. This option is great for spiky demands or unpredictable ETL. However, it can lead to runaway RPU spend if workloads aren’t adequately monitored.

Redshift Serverless includes Concurrency Scaling and Redshift Spectrum.

Concurrency Scaling and Redshift Spectrum

Concurrency Scaling - Automatically adjusts cluster capacity to handle spikes in concurrent queries. You get one free hour every 24 hours (and can bank up to 30 hours); beyond that, billing is per second. Amazon reports that the free credits cover 97% of customer needs.

Redshift Spectrum - Query S3 data directly without importing it into Redshift. Pricing is based on the number of bytes scanned, with a minimum of 10 MB per query. Querying uncompressed or unpartitioned data sets can quickly spike query costs.

DC2 vs RA3 nodes with Redshift Managed Storage

DC2 nodes - Bundle storage and compute on SSDs, making them fast and cost-effective for datasets under 1 TB. However, DC2 nodes require simultaneous scaling of both storage and compute, which can become out of sync and costly.

RA3 nodes - For larger datasets, RA3 nodes separate compute from storage via Redshift Managed Storage (RMS), which automatically tiers frequently accessed data to SSDs. You’re charged hourly for storage—not for the number of nodes—offering more flexibility and predictable costs. RMS also powers Redshift Serverless.

Finding the right pricing strategy is just the beginning. The following section discusses several additional Redshift cost escalation challenges.

Cost escalation factors in Redshift

Redshift’s pricing flexibility is valuable, but without strategic planning, monitoring, and refinement, costs can escalate quickly.

To manage spend effectively, teams must watch for these common risk factors:

• Idle or over-provisioned clusters
• Inefficient data loads and queries
• Uncontrolled scaling features
• Inadequate workload oversight and cost attribution

Idle or over-provisioned clusters

Inactive clusters drive up costs – On-demand clusters left running during off-hours continue accruing compute charges, even when idle.

Poor planning leads to waste – Inaccurate forecasting and weak workload monitoring result in underused reservations and excess capacity across both on-demand and reserved clusters.

Node selection impacts flexibility – DC2 forces joint scaling of compute and storage, while RA3 decouples them—but unmanaged storage growth can still generate unexpected fees.

Inefficient data loads and queries

Poor query and table design wastes resources – Inefficient SQL and suboptimal schema choices drive up compute by overusing disk and memory.

Full-refresh transformations inflate costs – Rebuilding entire tables instead of updating new rows burns excess compute and lengthens runtime.

Unoptimized Spectrum queries scan excessive data – Running against unpartitioned or uncompressed S3 files triggers unnecessary $5-per-TB scan charges.

Uncontrolled scaling features

Concurrency Scaling can get expensive fast – After the one free hour, per-second fees escalate quickly for high-concurrency workloads if usage isn’t tightly controlled.

Serverless auto-scaling drives unpredictable spend – Rapid scaling during peak demand can balloon per-second RPU charges unless limits are set and usage is monitored.

Elastic Resize and autoscaling need guardrails – These powerful features improve flexibility, but without caps, they risk runaway costs during surges in demand.

Inadequate workload oversight and cost attribution

Cost attribution is complex and manual – Redshift doesn’t offer native model-level reporting, so tracking costs by user or team requires custom SQL and tagging strategies.

Lack of oversight drives hidden spend – Without strong policies and workload monitoring, inefficient queries, idle resources, and redundant jobs can quietly inflate costs.

CI/CD pipelines can bloat budgets – Rebuilding unchanged models or rerunning unneeded tests in automated pipelines adds unnecessary compute charges over time.

These challenges underscore the need for proactive cost management and architectural awareness.

Cost cutting strategies for Redshift

Redshift cost management requires aligning pricing models, cluster design, data practices, and transformation workflows with real-world usage patterns to optimize your Redshift usage.

The following best practices will help you reduce waste, improve performance, and lower costs:

• Optimize pricing, clusters, and node types
• Streamline data management and query design
• Use incremental models to reduce compute spend
• Monitor usage and enforce cost controls

Optimize pricing, clusters, and node types

Choose the right pricing model and cluster configuration – Align pricing and configuration with actual usage patterns. Pause idle on-demand clusters to avoid charges, and use reserved instances only if you can accurately forecast and predict your workloads.

Right-size clusters and node types – Avoid paying for excess capacity by selecting the right node type. For growing datasets, choose RA3 nodes to scale storage independently from compute. If your workloads are bursty or unpredictable, configure Redshift Serverless or Concurrency Scaling with strict usage caps to prevent runaway RPU or per-second fees.

Deploy multiple Redshift instances – Consider adopting multiple Redshift warehouses tailored to specific workloads, assigning dedicated clusters for ETL, development, production, testing, or departmental use, etc. This improves performance, prevents resource contention, and enables granular cost tracking by team or environment.

Streamline data management and query design

Store your data appropriately – Keep high-use, operational data in Redshift for faster performance and offload infrequently used or historical data to S3, where you can use Spectrum for lower-cost querying. Minimize Spectrum’s $5-per-terabyte scan charges by partitioning and compressing external files and aggregating raw data where possible.

Enable data compression – Compression reduces disk I/O and improves efficiency, especially for large or frequently updated tables. Run VACUUM and ANALYZE to reclaim space and optimize query execution. Use dbt to apply performance-aware model configurations, such as sort and distribution keys, directly in model files.

Use dbt to manage transformations – Once semi-structured data is staged in Redshift or made accessible via Spectrum, use dbt to structure and transform it. dbt supports incremental updates, modular SQL, and lineage tracking—reducing scan volume, cutting ETL overhead, and improving transformation consistency across environments. While dbt doesn’t ingest raw data, it excels at managing transformations once the data is queryable in Redshift.

Use incremental models to reduce compute spend

Replace full table refreshes with incremental materialization – Full-table rebuilds are expensive, especially in CI/CD pipelines that reprocess unchanged data. Use incremental materialization in dbt to transform only new or changed rows, reducing query time and compute usage. Combined with Redshift’s materialized view refresh, this approach streamlines deployments and cuts overhead.

Use deferred builds in dbt – Deferred builds enable you to test models in isolation using production artifacts, thereby skipping full rebuilds. This lowers compute costs, shortens development cycles, and avoids redundant storage, especially in large Directed Acyclic Graphs (DAGs) or CI pipelines where full rebuilds are expensive and time-consuming.

Monitor usage and enforce cost controls

Track Redshift performance and usage - Use Amazon Redshift Advisor to identify optimization opportunities and regularly review cluster sizing to eliminate idle capacity. Tag resources and monitor spend with tools like AWS Cost Explorer, Budgets, and Trusted Advisor for clear cost attribution across teams.

Set usage caps - For unpredictable workloads, configure Concurrency Scaling and Redshift Serverless with strict usage caps to prevent runaway charges. Monitor query performance and storage growth to ensure your architecture remains aligned with business needs.

Redshift cost optimization isn’t a one-time fix. By continuously aligning your pricing models, cluster configurations, data management strategies, and transformation workflows with actual workload patterns, you can effectively manage costs while maintaining optimal performance, maximizing the value of Redshift’s speed and scalability.

How dbt Fusion helps optimize Redshift costs

Best practices lay the groundwork for Redshift cost optimization. dbt builds on these foundations, simplifying transformations and driving more strategic use of Redshift resources:

• Smarter configuration and streamlined execution.
• Aligning sort and distribution keys with query patterns.
• Choosing efficient materializations and using incremental models to avoid redundant compute.
• Modular transformation for standardized testing and clear documentation.

dbt Fusion adds advanced orchestration features that make cost optimization even more consistent across your environment:

• Develop and test locally without spinning up Redshift
• Optimize Redshift costs with dbt Fusion’s intelligent runtime
• Accelerate performance with dbt Fusion’s Rust engine

Develop and test locally without spinning up Redshift

Fusion enables you to build and validate models locally, referencing production artifacts without requiring a remote Redshift instance. This speeds up iteration, reduces development overhead, and avoids unnecessary cloud compute costs, especially in CI workflows or large DAGs. Its Rust-based engine powers fast, accurate SQL compilation and comprehension.

Optimize Redshift costs with Fusion's intelligent runtime

Fusion’s state-aware orchestration runs only models impacted by upstream changes, cutting redundant builds and reducing RPU usage. Teams can expect ~10% savings on data platform costs. Fusion also supports ahead-of-time compilation and static SQL analysis, helping your teams catch inefficiencies before they hit the warehouse.

Accelerate performance with Fusion's Rust engine

Fusion’s orchestration engine, written in Rust, delivers 30 times faster parsing and compiles twice as fast as dbt Core, improving CI throughput and shortening development cycles. While not a direct Redshift cost reducer, this speed enables more efficient orchestration and earlier error detection, minimizing full-model rebuilds and excess compute.

Conclusion

Cutting costs in Redshift depends on consistently executing best practices and adopting tools built for efficiency. dbt Fusion help teams streamline workflows, reduce compute overhead, and manage resources more strategically.

Request a demo to see how dbt Fusion can help you improve performance and cut Redshift costs.