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 (SageMaker Autopilot)
In-database MLSnowflake Cortex (LLM functions, ML functions)Redshift ML (CREATE MODEL)
Python UDFsSnowpark PythonPython UDFs via Lambda
Vector data typeComing (VECTOR type)No native support
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 Kinesis/MSK

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 SageMaker for end-to-end ML workflows
  • Redshift Spectrum queries data directly in S3 without loading
  • Materialized views with automatic refresh for pre-computed features
  • Familiar PostgreSQL syntax

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) speeds up certain query patterns. Manual cluster resizing 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
  • Cross-organization data sharing is important
  • Team prefers Snowflake’s operational simplicity

When to Choose Redshift

  • AWS-centric architecture
  • Tight SageMaker integration is valuable
  • Steady, predictable workloads (cost-effective with reserved instances)
  • Need Redshift Spectrum for S3 data lake queries
  • PostgreSQL compatibility is important for existing tools

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.