Snowflake vs. Amazon Redshift in 2024: A Consultant's Honest Take

Few technology decisions generate as much debate in data engineering circles as the choice between Snowflake and Amazon Redshift. Both are mature, capable cloud data warehouses. Both have passionate advocates. And both have genuine strengths that make them the right choice in specific contexts. As consultants who have implemented both platforms across dozens of client engagements in Canada, the UK, and Africa, we have developed nuanced views on when each platform wins — and we will share them plainly.

This comparison is written for CTOs, data architects, and IT managers who need to make a well-informed decision, not for vendor marketing teams. We will cover performance, cost, ecosystem integration, operational complexity, and the scenarios where each platform has a genuine edge.

A Brief Orienter on Each Platform

Amazon Redshift is a columnar, massively parallel processing (MPP) data warehouse fully managed within AWS. It comes in two deployment modes: provisioned clusters (where you choose the node type and count) and Redshift Serverless (where AWS manages capacity automatically). Redshift integrates natively with the rest of the AWS ecosystem: S3, Glue, Lake Formation, QuickSight, SageMaker, and more.

Snowflake is a cloud-native data warehouse that runs on AWS, Azure, or GCP. Its signature architectural feature is the complete separation of compute and storage: virtual warehouses (compute clusters) scale independently of the data stored in Snowflake’s managed storage layer. Snowflake pioneered the multi-cloud model and offers unique features like data sharing across organisations and a data marketplace.

Both platforms are SQL-based and ANSI-compliant, so the day-to-day experience of writing analytics queries is similar. The differences that matter are in architecture, cost model, ecosystem integration, and operational characteristics.

Performance: Nuanced, Not Clear-Cut

Neither platform consistently outperforms the other on query performance. The answer depends heavily on workload characteristics:

Redshift advantages:

• Federated queries via Redshift Spectrum (queries against S3 data without loading) are tightly integrated and performant for mixed lakehouse/warehouse workloads
• Redshift’s AQUA (Advanced Query Accelerator) provides hardware-accelerated query processing on ra3 nodes
• Zone map pruning and column-level compression are mature and well-tuned
• For predictable, consistent workloads with well-tuned distribution and sort keys, Redshift provisioned clusters are extremely fast at known cost

Snowflake advantages:

• Workload isolation is simpler: spin up a second virtual warehouse for ad-hoc queries without affecting production pipelines
• Automatic clustering handles data organisation without manual distribution/sort key decisions
• Search optimisation service provides indexed lookups that Redshift lacks
• Better performance on highly concurrent mixed workloads out of the box, without query queue management

In practice, for a well-tuned Redshift deployment with appropriate distribution keys and sort keys, performance is comparable to Snowflake on standard analytical workloads. The Snowflake advantage is most pronounced when workloads are unpredictable or when you need strong isolation between multiple teams without careful queue management.

Cost: The Most Misunderstood Dimension

Cost comparisons between Snowflake and Redshift are frequently misleading because they compare list prices without accounting for usage patterns.

Redshift pricing:

• Provisioned: RA3 nodes at approximately $0.26-$3.26/hour per node depending on size, with 1-year Reserved Instances reducing cost by 40-50%
• Serverless: $0.375 per Redshift Processing Unit (RPU) hour, billed per second, minimum 8 RPUs
• Storage: Managed Storage at $0.024/GB/month for data beyond what fits in the node local SSD

Snowflake pricing:

• Compute: Credit-based, approximately $2.00-$3.00 per credit on AWS Standard tier (enterprise pricing varies)
• Storage: $23/TB/month for on-demand
• The key factor: Snowflake compute runs only when queries are executing, with automatic suspension after a configurable idle period

For bursty, intermittent workloads — a team that runs heavy reports twice a week and nothing in between — Snowflake’s auto-suspend makes it genuinely cheaper because you pay only for compute during query execution. For continuous, high-throughput workloads — pipelines running every 15 minutes, dashboards with constant user load — Redshift provisioned clusters with Reserved Instance pricing typically win on cost by a significant margin.

A practical rule of thumb: if your data warehouse is actively used more than 60-70% of the day, Redshift provisioned with Reserved Instances is almost certainly cheaper. Below that utilisation threshold, Snowflake’s consumption model may be more economical.

AWS Ecosystem Integration: Redshift’s Clearest Advantage

If your organisation is already committed to AWS, Redshift’s native integration with the AWS ecosystem is a meaningful advantage:

• IAM — Redshift uses IAM for authentication and authorisation, consistent with every other AWS service
• AWS Glue — Redshift integrates with the Glue Data Catalog, so metadata is consistent across Redshift, Athena, and EMR
• Lake Formation — column-level security and row-level security policies apply consistently across Redshift, Athena, and Redshift Spectrum
• QuickSight — native integration with no connection configuration required
• S3 — COPY and UNLOAD operations between S3 and Redshift are native, fast, and cheap
• VPC — Redshift clusters live in your VPC, consistent with your network security model

Snowflake runs on AWS infrastructure but is a separate SaaS platform. It has IAM-based authentication options (through SAML/SSO) but does not natively integrate with Lake Formation, Glue, or QuickSight. Connecting Snowflake to QuickSight requires a JDBC connector and custom networking configuration. This is workable but adds operational complexity that compounds over time.

For organisations planning to use dbt on AWS, both platforms are well-supported — dbt-redshift and dbt-snowflake are both mature, first-class adapters.

Data Sharing and Marketplace: Snowflake’s Unique Capability

Snowflake’s Data Sharing feature allows organisations to share live, read-only data with external Snowflake accounts — no data movement, no ETL, no duplication. The receiving party can query the shared data as if it were local. This is genuinely unique: Redshift has its own data sharing feature, but it is limited to accounts within the same AWS organisation.

For businesses that need to share live data with customers, partners, or subsidiaries running on different cloud platforms, Snowflake’s cross-cloud data sharing is a compelling differentiator. For businesses whose data sharing needs are internal, Redshift’s data sharing is sufficient and simpler to manage.

The Snowflake Marketplace — a catalogue of third-party datasets available for direct subscription — is another differentiator. If your analytics workflows depend on purchased third-party data (financial data, weather, demographic enrichment), the Marketplace eliminates ETL pipelines for that data entirely.

Operational Complexity

Redshift provisioned requires the most operational management: choosing node types, managing cluster resizing, tuning distribution and sort keys, and monitoring query queues. Teams without Redshift expertise often under-invest in these areas and run into performance problems that require significant effort to diagnose and fix.

Redshift Serverless eliminates most of this complexity. AWS manages capacity automatically, removes the need for distribution key decisions on most workloads, and provides a simpler operational model. For organisations without dedicated Redshift expertise, Serverless is the safer starting point.

Snowflake is operationally simpler out of the box. Auto-clustering, automatic warehouse sizing, and a clean separation between compute and storage mean fewer knobs to turn. The trade-off is less control over cost optimisation and query performance characteristics.

Our Recommendation Framework

Choose Redshift if:

• You are already on AWS and want to keep your data platform within the AWS ecosystem
• Your workload is predictable and high-utilisation (>60% of the day)
• You need tight Lake Formation integration for fine-grained access control
• Your team has (or is willing to develop) Redshift expertise
• Cost predictability at high utilisation is important

Choose Snowflake if:

• You need true multi-cloud flexibility (data in AWS + Azure + GCP)
• Your workloads are bursty and intermittent
• You need cross-organisational data sharing with external partners on different clouds
• You want the lowest operational complexity and fastest time to value
• You plan to use the Snowflake Marketplace for third-party data

Consider Redshift Serverless as a middle path: it combines AWS-native integration with Snowflake-like operational simplicity, at a pricing model that works for intermittent workloads. For many mid-market AWS customers, Redshift Serverless is the pragmatic choice that Redshift should have offered years ago.

Conclusion

The Snowflake vs. Redshift question does not have a universal answer. Both are excellent platforms, and both have won the market’s confidence at scale. The right choice depends on your ecosystem commitments, workload patterns, cost sensitivity, and operational capabilities.

What matters more than the platform choice is the architecture built on top of it: well-modelled data, tested transformations, documented metrics, and a governance layer that allows your organisation to trust and use the data. A great Redshift deployment beats a mediocre Snowflake deployment, and vice versa.

Infra IT Consulting works with both platforms and will give you an honest assessment based on your specific situation — not on which platform we happen to have a preferred vendor relationship with. Contact us to discuss your requirements.

Related reading:

• Modernising Legacy Data Warehouses on AWS
• Redshift vs. Athena: Choosing the Right Analytics Engine
• dbt on AWS: Transforming Raw Data into Analytics-Ready Models