We are a Redshift consultancy, so you should read this with that in mind. We also spend a lot of time migrating data between these three platforms in both directions, and a comparison that pretends Redshift wins everywhere would not be useful to you or to us. Here is how we actually advise clients in 2026.
Where the three platforms have converged
Ten years ago these were different products. Today all three:
- Separate storage from compute and bill them separately
- Offer a serverless or auto-scaling compute mode
- Read and write Apache Iceberg tables in object storage
- Share data across accounts without copying
- Run SQL-invoked machine learning and have a generative-AI assistant in the query editor
- Ingest from streams and from operational databases with little or no pipeline code
The feature-matrix comparison that decided platform choices in 2018 is mostly a tie. What differs is where each platform is strongest, what it costs for a given shape of workload, and what it integrates with naturally.
Where Snowflake is genuinely better
Snowflake's strengths are real and we tell clients so:
- Multi-cloud and cross-cloud sharing. If your data consumers are on Azure or Google Cloud, or your partners are, Snowflake's marketplace and sharing across clouds is unmatched.
- Operational simplicity. There is very little to tune. Virtual warehouses are sized in T-shirt sizes; clustering is optional. A small team without a database specialist gets further, faster.
- Semi-structured data ergonomics.
VARIANThandling remains a little more pleasant than Redshift'sSUPER, though the gap has narrowed. - Ecosystem. More third-party tools were built against Snowflake first.
The cost of that simplicity is credits. Snowflake's per-credit pricing at scale, particularly for always-on ingestion and heavy transformation, is where we most often see CFOs start asking about alternatives.
Where Databricks is genuinely better
- Spark and Python-first workloads. If your data engineering is notebooks, PySpark and ML pipelines rather than SQL, Databricks is the natural home. Redshift is a SQL warehouse and does not pretend otherwise.
- Lakehouse-native from the start. Delta Lake and Unity Catalog make "the lake is the warehouse" the default rather than an add-on.
- ML platform depth. MLflow, feature store, model serving and GPU clusters in one place. Redshift ML hands off to SageMaker, which is excellent, but it is a second console.
Databricks SQL has become a capable warehouse, but for BI-heavy workloads with thousands of concurrent dashboard queries we still see better price-performance on a tuned Redshift or Snowflake deployment.
When Redshift wins
1. You are an AWS shop, and your data is already there. This is the big one. Zero-ETL from Aurora, RDS and DynamoDB; native streaming ingestion from Kinesis and MSK; IAM and Lake Formation as the single permission model across Redshift, Athena, EMR and SageMaker; S3 Tables for Iceberg with maintenance handled; CloudWatch, KMS and Organizations for everything else. No other warehouse integrates with AWS services this directly, because no other warehouse is an AWS service. Every cross-platform integration we build for Snowflake-on-AWS customers is a thing Redshift customers do not build.
2. Steady, heavy workloads where reserved pricing applies. A 24/7 transformation and reporting load on reserved RA3 nodes is the cheapest way to run a warehouse at that scale that we know of. Snowflake has no equivalent of a three-year node reservation; its committed-spend discounts are real but smaller.
3. Spiky workloads on Serverless with data sharing. The producer/consumer pattern (reserved RA3 producer, Serverless consumers) lets you pay reserved rates for the steady part and per-second rates for the rest, with chargeback by consumer. It is a cost structure that is hard to match elsewhere.
4. Predictable, high-concurrency BI. Auto WLM, query priorities, result caching, materialized views with automatic rewrite, and Concurrency Scaling were built for thousands of dashboard users hitting the same tables. Tuned properly, Redshift's cost per dashboard query is very competitive.
5. Compliance boundaries that must stay inside AWS. Healthcare, public sector and financial customers who have already done the compliance work for AWS (HIPAA eligibility, FedRAMP, the BAA) get Redshift inside that boundary. Adding a second vendor means a second round.
6. You already have Redshift and it is neglected, not wrong. A surprising number of "we need to move to Snowflake" conversations start with a DC2 cluster built in 2018, never migrated to RA3, with interleaved sort keys and a WLM config from the launch date. A migration to RA3 or Serverless plus a cost-tuning pass often delivers what the re-platforming was supposed to, in weeks instead of quarters.
Where Redshift still loses
Candidly: multi-cloud requirements, Python-centric engineering teams, and organizations that want a warehouse nobody has to tune. If that is you, we will say so in the first call, and we will help you migrate off Redshift properly if that is the outcome.
How to decide
Run the same representative workload (the top 50 queries by cost, plus the ingestion pattern) on each candidate for a week, at realistic concurrency, and compare the bill and the latency. Every vendor will help you do this. Then weigh the integration and compliance factors above, which the benchmark cannot measure. A Redshift POC scoped this way takes two to three weeks and gives you numbers rather than opinions.