Snowflake and Amazon Redshift are cloud data warehouses used to store and analyze data that feeds AI systems. For AI workloads, they serve as the foundation for feature engineering, training data preparation, and analytics on model outputs. The choice affects data architecture, cost, and integration with ML tools.

Architecture

Snowflake separates compute from storage completely. Virtual warehouses (compute) can be started, stopped, and scaled independently. Multiple compute clusters can query the same data simultaneously. Storage is managed automatically with transparent micro-partitioning.

Redshift traditionally coupled compute and storage in node clusters. Redshift Serverless now offers compute-storage separation. RA3 nodes also separate compute from managed storage. Traditional DC2 nodes use local SSD with fixed capacity.

Snowflake’s architecture is more flexible for variable workloads. Redshift Serverless closes the gap but is newer.

AI-Specific Features

FeatureSnowflakeRedshift
ML integrationSnowpark ML, Cortex AIRedshift ML (Amazon SageMaker AI, Amazon Bedrock)
In-database MLSnowflake Cortex (AISQL functions, LLM and ML functions)Redshift ML (CREATE MODEL, CREATE EXTERNAL MODEL)
Python UDFsSnowpark PythonLambda UDFs (native Python UDFs deprecated, end of support June 30, 2026)
Vector data typeVECTOR type (generally available)No native VECTOR type
Data sharingSecure Data Sharing (cross-account, cross-cloud)Redshift data sharing (cross-cluster)
External tablesYes (S3, Azure Blob, GCS)Yes (Redshift Spectrum on S3)
Streaming ingestionSnowpipe (continuous loading)Streaming ingestion from Amazon Kinesis Data Streams and Amazon MSK

Both platforms have moved in-database generative AI from preview to production. Snowflake Cortex AISQL functions (including AI_CLASSIFY, AI_TRANSCRIBE, AI_EMBED, AI_SIMILARITY, AI_TRANSLATE, AI_EXTRACT, AI_SENTIMENT) reached general availability in November 2025, and the Snowflake VECTOR data type with vector similarity functions has been generally available since May 2024, enabling semantic search and retrieval directly in SQL. On the AWS side, Redshift ML adds an Amazon Bedrock integration: a CREATE EXTERNAL MODEL statement registers a foundation model (for example Anthropic Claude, Amazon Titan, Meta Llama, or Mistral AI models served through Bedrock) and exposes it as a SQL inference function for text generation, summarization, classification, and sentiment analysis. Redshift itself has no native VECTOR type, so vector storage and similarity search in the AWS stack typically live in services such as Amazon OpenSearch Service or PostgreSQL with pgvector (Amazon RDS and Amazon Aurora).

Feature Engineering

Both platforms are used for SQL-based feature engineering:

Snowflake advantages:

  • Snowpark allows writing feature engineering in Python, Scala, or Java that executes within Snowflake’s compute
  • Time travel (up to 90 days) enables point-in-time correct feature computation for training data
  • Instant cloning creates test environments without copying data

Redshift advantages:

  • Tight integration with Amazon SageMaker AI for end-to-end ML workflows, now unified through Amazon SageMaker Lakehouse and SageMaker Unified Studio, which expose S3 and Redshift data through a single Apache Iceberg compatible lakehouse
  • Zero-ETL integrations pull data from operational databases and SaaS applications into Redshift and the lakehouse in near real time, reducing pipeline code for feature pipelines
  • Redshift Spectrum queries data directly in S3 without loading
  • Materialized views with automatic refresh for pre-computed features
  • Familiar PostgreSQL based SQL dialect

Data Volume and Performance

Snowflake handles variable workloads well. Auto-scaling can spin up additional compute clusters during peak feature engineering jobs and shut them down after. No capacity planning needed. Performance scales linearly with warehouse size.

Redshift with RA3 nodes or Serverless handles large datasets well. Concurrency scaling adds transient compute for burst workloads. AQUA (Advanced Query Accelerator) acceleration is now applied automatically by Redshift rather than configured per cluster, as the manual AQUA configuration controls have been retired. Manual cluster resizing is available for RA3.

For unpredictable ML workloads (batch feature engineering that runs intensively for hours then sits idle), Snowflake’s auto-scaling and per-second billing provide better cost efficiency.

Cost

Snowflake: Per-second billing for compute (virtual warehouses). Storage: $23-40/TB/month depending on region and edition. Compute: $2-4/credit depending on edition. A medium warehouse costs ~$4/hour.

Redshift Serverless: Per-second billing for compute (RPU). $0.375/RPU-hour. Storage: $0.024/GB/month (same as S3). More predictable for steady workloads.

Redshift Provisioned: Per-hour billing for nodes. dc2.large: ~$0.25/hour. ra3.xlplus: ~$1.09/hour. Cheaper for steady, predictable workloads with reserved instance discounts.

For AI workloads with variable compute demands, Snowflake’s auto-suspend and auto-resume typically results in lower costs. For steady workloads, Redshift provisioned with reserved instances can be cheaper.

Ecosystem Integration

Snowflake integrates broadly: works with any cloud (AWS, GCP, Azure), supports all major BI tools, connects to Spark, Databricks, dbt, and most data integration platforms. Snowpark provides native Python execution.

Redshift integrates deeply with AWS: native SageMaker integration, S3 access via Spectrum, Kinesis streaming ingestion, Glue for ETL, QuickSight for BI. The AWS-native integration is seamless.

For AWS-centric organizations, Redshift’s native integration reduces friction. For multi-cloud or cloud-agnostic organizations, Snowflake’s portability is an advantage.

When to Choose Snowflake

  • Variable workload patterns (burst feature engineering, idle periods)
  • Multi-cloud data strategy
  • Need Snowpark for Python-based feature engineering
  • Want in-database vector search and generative AI in SQL (Cortex AISQL functions, the VECTOR type, Cortex Search)
  • Cross-organization data sharing is important
  • Team prefers Snowflake’s operational simplicity

When to Choose Redshift

  • AWS-centric architecture
  • Tight Amazon SageMaker AI integration is valuable
  • Need in-database generative AI through Redshift ML and Amazon Bedrock
  • Steady, predictable workloads (cost-effective with reserved instances)
  • Need Redshift Spectrum for S3 data lake queries, or the Amazon SageMaker Lakehouse for unified analytics and ML on one copy of data
  • A PostgreSQL based SQL dialect and driver compatibility matter for existing tools (Redshift derives from PostgreSQL but is not fully compatible)

Both are capable data warehouses for AI workloads. The choice is most often determined by existing cloud commitment and data architecture rather than by AI-specific features.

For related comparisons, see /comparisons/athena-vs-redshift/, /comparisons/delta-lake-vs-iceberg/, and /comparisons/huggingface-vs-bedrock/.

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