Redshift is the choice for AWS-heavy environments needing deep integration. Snowflake is the multi-cloud champion of simplicity and concurrency.
I've run production workloads on both Redshift and Snowflake for years, and I've watched teams migrate in both directions. The Redshift-vs-Snowflake debate used to be straightforward — Redshift for AWS-native stacks, Snowflake for everything else. That story got more complicated once Redshift Serverless shipped in 2022 and Snowflake doubled down on Iceberg and Cortex. Today the decision is less about capability gaps and more about where your team wants to live operationally.
A quick history refresher helps. Amazon Redshift was the first serious cloud data warehouse, launched in 2012 as a forked PostgreSQL engine on dedicated cluster nodes. AWS has rebuilt it piece by piece since — RA3 nodes decoupled storage and compute in 2019, Redshift Serverless shipped in 2022, zero-ETL integrations with Aurora and DynamoDB landed in 2023, and the generative-SQL features came in 2024. If you evaluated Redshift seven years ago and wrote it off as painful, the current product is genuinely different.
Snowflake, launched commercially in 2014, went the opposite direction architecturally. No cluster nodes, no VACUUM, no DISTKEY / SORTKEY. Just pick a warehouse size, write SQL, let the platform handle the rest. That philosophy is still the defining feature. Everything Snowflake has added since — Snowpark, Cortex, Iceberg, Streamlit-in-Snowflake, Container Services — layers on top without ever forcing you to care about infrastructure.
When I help teams work through this, four factors do the heavy lifting:
I helped a mid-market SaaS company with a classic pattern: they started on Redshift five years ago because their app ran on AWS. Their data team grew to 12 people, workloads diversified from "daily dashboards" to "daily dashboards plus ad-hoc analyst queries plus customer-facing embedded analytics plus ML feature engineering." The Redshift cluster kept requiring manual intervention — VACUUM schedules, DISTKEY retuning when queries shifted, WLM slot tuning during peak hours. After about 18 months of that toil they migrated to Snowflake. The bill went up about 15%, but the platform-engineering headcount they'd have needed to keep pace with Redshift more than paid the difference. I've also seen the opposite — a steady, predictable AWS-heavy analytics team where moving to Snowflake would have been a solution in search of a problem.
There's no universal winner here. Both are mature, fast, and reliable in 2026. The right answer depends heavily on your cloud commitments and team shape.
Choose Amazon Redshift if:
AUTO_SUSPEND aggressively and leave warehouses running idle over weekends. The "expensive Snowflake" reputation almost always traces back to operational hygiene, not the platform itself.This is an increasingly common pattern at bigger organizations. Redshift handles operational analytics and AWS-native pipelines where zero-ETL shines; Snowflake handles cross-business-unit sharing, customer-facing analytics, and mixed workloads. With Iceberg external tables on both sides, sharing data without duplication is genuinely viable. The caveat is the same as always — running two platforms only pays off if you have the team to operate both.
It's much closer than provisioned Redshift ever was. Redshift Serverless removes cluster management, scales capacity automatically, and bills per RPU-second — conceptually similar to Snowflake's virtual-warehouse model. Snowflake still edges it on a few things: multi-cluster concurrency scaling is smoother, cross-cloud deployment isn't an option on Redshift, and Secure Data Sharing is still unmatched. For AWS-native teams that want simplicity without leaving AWS, Redshift Serverless is now a genuine Snowflake alternative.
Yes, and it's a pattern I see at mid-to-large orgs. Common split: Redshift owns operational analytics and AWS-native zero-ETL pipelines from Aurora, DynamoDB, and Kinesis; Snowflake owns cross-BU sharing, external data sharing, and customer-facing embedded analytics. With Iceberg external tables on both sides you can share data without physical copies. It's worth the complexity only if your team can operate two platforms.
Snowflake, by a meaningful margin. Multi-cluster warehouses add and remove clusters automatically with zero tuning — you just set min/max cluster count. Redshift has Concurrency Scaling which spins up temporary clusters under load, but it has quotas (by default, one hour of free Concurrency Scaling per day), and tuning WLM queues takes more thought. For workloads with 100+ simultaneous dashboard users or burst patterns, Snowflake is the easier path.
Depends on workload shape. For steady, predictable, high-utilization workloads on AWS, Redshift RA3 provisioned is often 20-40% cheaper than Snowflake. For bursty or mixed workloads where auto-suspend catches idle time, Snowflake is usually cheaper because you literally pay zero when the warehouse is asleep. Redshift Serverless lands somewhere in between. The #1 cost lever on either platform is operational hygiene — right-sizing and pause/suspend — not the platform choice itself.
Redshift ML delegates to SageMaker Autopilot — you write CREATE MODEL in SQL and AWS handles training and inference via SageMaker. It works but feels bolted on. Snowflake Cortex is native: hosted LLMs, embeddings, vector search, and Cortex Analyst for natural-language SQL all run inside Snowflake. For teams that want LLM features directly in SQL without leaving the warehouse, Cortex is meaningfully ahead. For teams that already have a SageMaker practice, Redshift ML slots in more naturally.
Both let you query data in object storage without loading it. Spectrum is production-hardened at petabyte scale and tightly integrated with Glue Data Catalog — still the smoothest option if you already have an S3-plus-Glue data lake. Snowflake's Iceberg external tables shipped later but are now fully bi-directional, and Snowflake can read and write Iceberg directly from AWS/Azure/GCS storage. If portability is a priority, store your base tables as Iceberg from day one — Snowflake, Spectrum (via Glue Iceberg connector), Trino, and Databricks can all read them.
Less painful than it used to be, but still real work. Most straightforward Redshift SQL translates directly to Snowflake SQL — both descend from PostgreSQL. You'll rewrite stored procedures (Redshift's are limited; Snowflake Scripting or JavaScript procedures are the replacement), migrate UDFs, and rewrite anything that depended on DISTKEY / SORTKEY (those concepts don't exist in Snowflake). Budget 2-3 months for a mid-size migration. The hard part isn't the SQL — it's downstream BI tools, data pipelines, and cutover planning.