Athena and Redshift both run SQL analytics on AWS, but they serve different query patterns and cost profiles. Athena is serverless query-on-demand. Redshift is a managed data warehouse. For AI and ML teams, the choice affects how training data is queried, how features are computed, and how model results are analyzed.

Overview

AspectAmazon AthenaAmazon Redshift
ArchitectureServerless (Trino-based engine version 3)Managed cluster (or Redshift Serverless)
StorageQueries data in Amazon S3Managed storage + S3 (Spectrum)
Pricing$5 per TB scanned (or reserved DPUs)Per-node-hour or per-RPU (Serverless)
ConcurrencyHighModerate (WLM-managed)
Data LoadingNo loading requiredCOPY from S3, or zero-ETL ingestion
PerformanceGood for ad-hocOptimized for repeated queries
ML IntegrationAthena ML (Amazon SageMaker)Redshift ML (SageMaker Autopilot)

Query Patterns

Athena excels at ad-hoc queries against data in Amazon S3. No data loading, no cluster provisioning - point it at your S3 data lake and run SQL. This is ideal for exploratory data analysis, one-off investigations, and querying data that does not justify the cost of loading into a warehouse. Athena runs on a Trino-based engine (engine version 3), so it supports ANSI SQL with large joins, window functions, arrays, and Apache Iceberg tables.

Redshift excels at repeated, complex analytical queries against large datasets. Its columnar storage, query optimization, materialized views, and result caching make repeated query patterns significantly faster than Athena. Dashboard queries, scheduled reports, and feature computation queries that run on a schedule benefit from Redshift’s optimization.

Cost Model

Athena charges $5 per TB of data scanned in most AWS Regions (check the pricing page for your Region). Using columnar formats (Apache Parquet, ORC) and partitioning can sharply reduce costs by minimizing the bytes scanned, since Athena only reads the columns and partitions a query touches. For occasional queries, Athena is extremely cost-effective. For high-volume query workloads, costs can grow quickly, so Athena also offers Provisioned Capacity (reserved Data Processing Units, or DPUs) at a fixed hourly rate for teams that want predictable spend instead of per-scan billing.

Redshift Provisioned charges per node-hour. Redshift Serverless charges per RPU-hour based on compute consumed, billed per second after a 60-second minimum. As of June 2025, Redshift Serverless supports a minimum base capacity of 4 RPUs (starting at roughly $1.50 per hour), which lowered the entry cost for small and development workloads. For sustained query workloads, Redshift Provisioned with Reserved Instances is typically cheaper than Athena at scale. Redshift Serverless provides a middle ground with consumption-based pricing.

ML Integration

Both services integrate with SageMaker for ML. Athena ML lets you call SageMaker endpoints from SQL queries using the USING FUNCTION syntax. You can run inference on query results without moving data out of the query engine.

Redshift ML lets you create models directly from SQL using CREATE MODEL. Redshift ML uses Amazon SageMaker Autopilot under the hood to train models on your Redshift data and deploys them as SQL functions. You can also bring your own model type (for example XGBoost or K-means) or call pretrained large language models from Amazon SageMaker JumpStart to run tasks such as summarization and sentiment analysis from SQL. This is a lower-barrier path to ML for SQL-oriented analysts.

Data Lake Integration

Athena is native to the data lake - it queries S3 directly. Any data in S3 that is cataloged in the AWS Glue Data Catalog is immediately queryable.

Redshift accesses the data lake through Redshift Spectrum, which queries S3 data using external tables. Spectrum lets you join warehouse tables with data lake tables in a single query. However, Spectrum has its own compute costs and concurrency limits.

Redshift can also pull operational data in without a pipeline. Zero-ETL integrations replicate changes from sources such as Amazon Aurora (MySQL and PostgreSQL), Amazon RDS for MySQL, and Amazon DynamoDB into Redshift in near real time, with no extra charge for the integration itself (you pay only for the underlying Redshift storage and compute). This narrows a traditional gap with Athena: teams that previously chose Athena to avoid ETL can now land transactional data in Redshift continuously and query it alongside warehouse tables.

When to Choose Athena

Choose Athena for ad-hoc analysis, data exploration, and query workloads that are infrequent or unpredictable. Athena is ideal for querying raw data in your data lake without ETL, for teams that need SQL access to S3 data without managing infrastructure, and for cost-effective analysis of large datasets that are already partitioned and stored in columnar formats.

When to Choose Redshift

Choose Redshift for predictable, high-volume analytical workloads where query performance matters. Data warehousing, BI dashboards, scheduled feature computation, and complex multi-table joins benefit from Redshift’s optimizer. Choose Redshift when you need sub-second query response times for dashboards or when query volume makes Athena’s per-scan pricing expensive.

Practical Recommendation

Many teams use both. Athena for exploratory analysis and ad-hoc queries against the data lake. Redshift for production analytics, dashboards, and feature pipelines. The shared AWS Glue Data Catalog makes this combination seamless. For ML teams specifically, start with Athena for data exploration and training data extraction, then add Redshift when you need scheduled feature computation or high-performance analytical queries.

For a warehouse-to-warehouse comparison, see /comparisons/snowflake-vs-redshift-ai/. For the pipeline that prepares data for either engine, see /comparisons/dbt-vs-glue/.

Sources and Further Reading