Snowflake vs Redshift: Cloud Data Warehouses Compared (2026)

Snowflake is the default warehouse for most data teams today — multi-cloud, compute-storage separation, almost zero tuning. Redshift is the warehouse you pick when you're deep in AWS and want everything under one roof. If your company runs on AWS and your data already flows through S3, Glue, and SageMaker, Redshift keeps you in one ecosystem with real cost savings. If you need to run on multiple clouds, share data externally, or want zero-knob scaling, Snowflake wins clearly.

How do the architectures compare?

This is the foundational difference. Everything else — pricing, concurrency, how much your team needs to tune — follows from it.

          SNOWFLAKE                               REDSHIFT
 
  ┌─────────────────────────┐          ┌──────────────────────────┐
  │    Cloud Services        │          │    Leader Node            │
  │ (metadata, optimization) │          │  (query planning, coord)  │
  ├─────────────────────────┤          ├──────────────────────────┤
  │  Virtual Warehouse (XS)  │          │   PROVISIONED MODEL       │
  │  Virtual Warehouse (M)   │          │  Compute Node (dc2.large) │
  │  Virtual Warehouse (XL)  │          │  Compute Node (ra3.xlplus)│
  │                          │          │  Compute Node (ra3.4xl)   │
  │  Independent clusters.   │          │                           │
  │  Spin up/down per team.  │          │   OR  SERVERLESS MODEL    │
  │  Auto-scale by adding    │          │  RPUs (Redshift Processing│
  │  clusters behind one WH. │          │  Units) — auto-allocated  │
  ├─────────────────────────┤          ├──────────────────────────┤
  │  Centralized Storage     │          │  Redshift Managed Storage │
  │  (S3/GCS/ADLS, managed)  │          │  (auto-tiered to S3)     │
  │  Fully separated from    │          │  Separated on ra3 nodes;  │
  │  compute layer.          │          │  coupled on dc2 nodes.    │
  └─────────────────────────┘          └──────────────────────────┘
   Multi-cloud: AWS, Azure, GCP          AWS only

Snowflake separates storage and compute completely. You create virtual warehouses — independent compute clusters — sized from XS to 6XL. Each one runs its own workloads. Your BI dashboards, your ETL transforms, and your ad-hoc analysts can each get their own warehouse with no resource contention. Warehouses suspend automatically when idle and resume in seconds. Auto-scaling adds clusters behind a single warehouse endpoint when concurrent queries pile up.

Redshift comes in two flavors. Provisioned Redshift is cluster-based: you pick node types (dc2 for SSD compute, ra3 for managed storage), set the cluster size, and that's your capacity. The ra3 nodes separate compute from storage — dc2 nodes don't. Redshift Serverless launched in 2022 and eliminates cluster management by allocating RPUs (Redshift Processing Units) automatically. It's closer to Snowflake's model, but the provisioned path is still what most large deployments use.

The practical gap: Snowflake teams rarely think about infrastructure. Redshift Provisioned teams spend real time on distribution keys, sort keys, vacuum operations, and cluster sizing. Redshift Serverless closes this gap but costs more per query than a well-tuned provisioned cluster.

How does pricing actually work?

Different billing models, and neither is universally cheaper.

  COST COMPARISON: 10 TB stored, moderate query workload
 
  Snowflake (Enterprise, AWS, on-demand):
  ┌──────────────────────────────────────────────────┐
  │  Storage:  10 TB x $23/TB           =   $230/mo │
  │  Compute:  ~200 hrs Medium WH                   │
  │   (4 credits/hr x $3/credit x 200h) = $2,400/mo │
  │                                                  │
  │  Estimated total:               ~$2,600/mo      │
  └──────────────────────────────────────────────────┘
 
  Redshift Provisioned (3x ra3.xlplus nodes):
  ┌──────────────────────────────────────────────────┐
  │  Compute:  3 nodes x $1.086/hr x 730h = $2,378/mo│
  │  Managed Storage: 10 TB x $0.024/GB  =   $240/mo │
  │                                                  │
  │  Estimated total:               ~$2,618/mo      │
  └──────────────────────────────────────────────────┘
 
  Redshift Serverless:
  ┌──────────────────────────────────────────────────┐
  │  Base RPU: 8 RPU x $0.375/RPU-hr x usage hours  │
  │  200 hrs active use:              = $600/mo      │
  │  Storage: 10 TB x $0.024/GB       = $240/mo     │
  │                                                  │
  │  Estimated total:                ~$840/mo        │
  │  (but RPU cost scales with query complexity)     │
  └──────────────────────────────────────────────────┘
 
  * Snowflake: auto-suspend drops cost to zero during idle
  * Redshift Provisioned: you pay 24/7 unless you pause the cluster
  * Redshift Serverless: you pay only for active processing time
  * Reserved Instances cut Redshift Provisioned cost by 30-60%

Snowflake charges storage ($23/TB/month compressed) plus compute credits. Credits cost $2-4 each depending on your edition — Standard, Enterprise, or Business Critical. An XS warehouse burns 1 credit/hour, Medium burns 4, Large burns 8. Per-second billing with auto-suspend means idle time is free. The mental model is straightforward: warehouse size times active hours.

Redshift Provisioned charges by the hour per node. A dc2.large runs $0.25/hour. An ra3.xlplus is $1.086/hour. The cluster runs 24/7 by default — you can pause it, but that's a manual or scheduled action, not automatic. Reserved Instances (1 or 3 year) bring significant savings, which is how most mature Redshift deployments keep costs reasonable.

Redshift Serverless charges $0.375 per RPU-hour, billed per second. You set a base RPU capacity (minimum 8) and Serverless scales within that. No always-on cost — you pay only when queries run. This is the most directly comparable model to Snowflake's auto-suspend, though the per-query economics differ.

For steady, predictable workloads, a right-sized Redshift Provisioned cluster with Reserved Instances is often the cheapest option. For bursty, variable workloads, Snowflake's auto-suspend and auto-scale model tends to win. Redshift Serverless splits the difference.

How does concurrency compare?

This is where Snowflake pulls ahead for most teams.

Snowflake handles concurrency by design. Spin up multiple virtual warehouses — one per team, per use case, or per priority level. Each runs independently, so your dashboard refresh doesn't slow down your data scientist's heavy join. Within a single warehouse, auto-scaling adds clusters to handle concurrent queries without queueing. You don't configure this per query. It just works.

Redshift uses Workload Management (WLM) queues. You define queues with memory allocation and concurrency limits. Queries get routed to queues based on user groups or query groups. If a queue fills up, queries wait. Concurrency Scaling is an add-on feature that spins up transient clusters to handle bursts, but it costs extra and has cold-start latency. It works, but it requires configuration and monitoring.

If you have ten analysts hammering dashboards while the pipeline runs hourly transforms, Snowflake handles it out of the box. Redshift needs WLM tuning and possibly Concurrency Scaling enabled.

How do they handle semi-structured data?

Snowflake's VARIANT type handles JSON, Avro, Parquet, ORC, and XML natively. You load a JSON document, query it with dot notation (data:customer.name), and Snowflake handles the parsing at query time. No schema needed upfront. The VARIANT column stores semi-structured data alongside relational columns in the same table. It's genuinely flexible — I've loaded messy API responses into VARIANT columns and queried them without any pre-processing.

Redshift's SUPER type handles JSON and came later. You query it with PartiQL syntax (data.customer.name). It works, and Amazon has improved it steadily, but it started as a catch-up feature. The SUPER type lacks the breadth of Snowflake's VARIANT — no native Avro or Parquet in-table support (you process those through Spectrum or Glue before loading). For heavy JSON-first workloads, Snowflake is the stronger choice.

Side-by-side comparison

What about data sharing and cloning?

These are two of Snowflake's biggest differentiators, and they're worth calling out separately.

Snowflake data sharing lets you grant live, read-only access to tables and views across Snowflake accounts. No data copying, no ETL, no file exports. A partner with their own Snowflake account queries your data in place and always gets current results. Snowflake Marketplace takes this further — data providers publish datasets that any Snowflake customer can subscribe to. If your business involves sharing data with external partners, this is hard to beat.

Redshift added cross-account data sharing for provisioned clusters, and it works within AWS. But the ecosystem is smaller, the feature is newer, and there's no equivalent to Snowflake Marketplace's commercial data exchange.

Zero-copy clones are a Snowflake exclusive. You clone a database, schema, or table in seconds — no data duplication, no storage cost until the clone diverges. Need a dev environment that mirrors production? Clone the production database. Need to test a migration? Clone the table. Redshift doesn't have this. You snapshot and restore, which copies data and takes minutes to hours depending on size.

For teams that frequently spin up test environments or share data across organizational boundaries, these features alone can justify Snowflake's premium.

When does Redshift's AWS integration actually matter?

Redshift's biggest advantage isn't the warehouse itself — it's how it fits into the AWS ecosystem you're probably already running.

S3 integration is native and fast. Redshift Spectrum lets you query data in S3 directly, without loading it — useful for cold data or data lake queries. COPY and UNLOAD commands move data between S3 and Redshift without third-party tools.

Glue handles the catalog and ETL. Lake Formation manages permissions across your data lake and Redshift in one place. SageMaker connects for ML workflows — train a model in SageMaker, run inference from Redshift with CREATE MODEL. QuickSight is the native BI tool with no egress costs.

If your data platform is S3 + Glue + SageMaker + QuickSight, adding Redshift is one more piece in a coherent stack. Adding Snowflake means running a second vendor alongside all those AWS services. That's fine if Snowflake's advantages justify it, but it's real operational overhead.

Snowflake counters with Snowpark (Python/Java/Scala in the warehouse), Cortex AI (built-in LLM functions), and a broader BI ecosystem — it works with every major tool, not just one vendor's. But the AWS-native advantage is genuine for teams already committed to that ecosystem.

How to decide

Pick Snowflake if:

• You're multi-cloud or might be in the future
• Concurrency is a pain point — many users, many workloads, minimal tuning budget
• You need data sharing with external partners or Marketplace access
• Zero-copy clones would save your team real time in dev/test workflows
• You want the least infrastructure to manage and don't mind paying for convenience
• Your BI tool is Looker, Tableau, Sigma, or anything non-AWS

Pick Redshift if:

• You're all-in on AWS and plan to stay
• Your data pipeline already runs on S3 + Glue + Lake Formation
• You want the cheapest possible cost with Reserved Instances on a predictable workload
• QuickSight is your BI tool (or you want to avoid adding another vendor)
• SageMaker is your ML platform and you want tight inference integration
• Redshift Serverless covers your flexibility needs without leaving AWS

Pick either if:

• You run standard SQL analytics workloads at moderate scale. Both handle this well. The tiebreaker is usually which cloud ecosystem you've already committed to.

Where Fastero fits

Fastero connects to both Snowflake and Redshift — along with 30+ other data sources. You don't need QuickSight or Sigma or a separate BI tool. Plug in your warehouse credentials and get AI-powered dashboards on top of your data. If you're running both warehouses (common during migrations, or when different teams chose differently), Fastero queries across them from one place.

We're not selling you a warehouse. We're the analytics layer that sits on whichever one you already picked.

FAQ

Is Redshift cheaper than Snowflake? It can be. A well-tuned Redshift Provisioned cluster with 1-year Reserved Instances is often 30-50% cheaper than Snowflake Enterprise at similar scale. But "well-tuned" is doing a lot of work in that sentence — it means you've picked the right node type, sized the cluster correctly, set up distribution and sort keys, and configured WLM. Snowflake's auto-suspend and per-second billing often wins for bursty or unpredictable workloads where a Redshift cluster would sit idle burning money.

Can I migrate from Redshift to Snowflake? Yes. Both use SQL, so most queries transfer with minor syntax changes. The harder parts: Redshift distribution keys and sort keys have no Snowflake equivalent (Snowflake handles this automatically), COPY/UNLOAD commands need rewriting for Snowflake's COPY INTO, and any AWS-specific integrations (Spectrum, Glue, SageMaker) need alternatives. Budget 2-6 weeks depending on complexity. Data itself moves through S3 — export from Redshift, load into Snowflake.

What is Redshift Serverless and how does it compare to Snowflake? Redshift Serverless eliminates cluster management by automatically provisioning and scaling RPUs. You set a base capacity and it handles the rest. It's the closest Redshift gets to Snowflake's operational model. The tradeoffs: it's more expensive per query than a right-sized provisioned cluster, the RPU scaling can be less predictable than Snowflake's warehouse sizing, and it's still AWS-only. For teams that want Redshift's AWS integration without the cluster management overhead, it's a solid middle ground.

Does Snowflake run on AWS? Yes. Snowflake runs natively on AWS, Azure, and GCP — you pick a cloud and region when you create your account. A Snowflake account on AWS stores its data in S3 and runs compute on AWS infrastructure. The multi-cloud advantage is that you can also have Snowflake accounts on other clouds and share data across them.

Should I use Redshift Spectrum or just load everything into Redshift? Spectrum queries data in S3 without loading it — useful for cold or infrequently accessed data. For hot data that you query often, loading into Redshift (especially on ra3 managed storage) gives better performance. The common pattern is: recent and frequently queried data in Redshift tables, historical or exploratory data queried through Spectrum. Think of it as a tiered storage strategy within the Redshift ecosystem.

Related posts:

• Snowflake vs BigQuery: Cloud Warehouses Compared
• Redshift vs BigQuery: AWS vs Google Cloud Warehouses
• Snowflake vs Databricks: Warehouse vs Lakehouse
• Best Tools for Data Engineering Teams (2026)

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