Picking the right data warehouse is a huge deal for any business that runs on data. It’s the engine for all your analytics, reports, and big-picture thinking. In the world of cloud data warehouses, two names keep popping up: Snowflake and Amazon Redshift. Both are powerhouses built to handle staggering amounts of information.
This guide is a straightforward comparison of Snowflake vs Redshift. We’ll get into the real differences in how they’re built, how they perform, how you pay for them, and how easy they are to live with day-to-day. Getting your data into one place is a great start, but the real trick is getting that information to the people who need it most, like your customer support team, who need fast, reliable answers on the fly.
What is Snowflake?
Snowflake is a cloud data platform that’s delivered as a service, meaning you don’t have to manage any hardware or software installs. Its killer feature is an architecture that completely separates compute power from data storage. This design gives you an incredible amount of flexibility and makes scaling up or down a breeze.
Snowflake was built to work on any of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). It’s designed to give you top-tier performance without a ton of manual work, taking the headache out of database administration so your team can just focus on finding useful insights in your data.
What is Amazon Redshift?
Amazon Redshift is AWS’s own fully managed, massive-scale data warehouse service. It’s built on a more traditional design, using a cluster of machines (called nodes) that work together to process data using a method called Massively Parallel Processing (MPP).
Being an AWS product, Redshift connects seamlessly with the rest of the AWS universe, which includes tools for getting data in, storing it, and running machine learning on it. This makes it a go-to choice for companies that are already heavily invested in AWS, as it offers a solid mix of performance and cost-effectiveness.
Key differences: A deep dive into Snowflake vs Redshift
While both platforms are trying to solve the same problem, the way they’re built leads to some major differences in how they operate, grow, and are managed on a daily basis.
Architecture: Separate vs. bundled
The biggest difference between Snowflake and Redshift is their fundamental design.
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Snowflake: Snowflake’s setup has three separate layers: one for storage, one for processing queries (called "virtual warehouses"), and one for cloud services. The key takeaway here is that storage and compute are completely separate. This means you can ramp up your compute power for a heavy workload without having to pay for more storage you don’t need. It also means different teams can hit the same data with their own dedicated virtual warehouses without slowing each other down.
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Redshift: Redshift uses a more classic cluster-based approach where compute and storage are tied together on each machine. While their newer "RA3" nodes have started to separate these to keep up with Snowflake, the core idea is still centered around the cluster. To scale Redshift, you usually have to resize the whole cluster by adding more nodes, which can be slow and sometimes requires downtime. Snowflake’s ability to scale its virtual warehouses almost instantly is a different experience altogether.
Scalability and performance
How these two platforms handle growth is a direct result of their architecture.
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Snowflake: This is where Snowflake really stands out. Its architecture allows for what they call "instant and near-unlimited" scaling. You can spin up, resize, or shut down a virtual warehouse in seconds. Features like multi-cluster concurrency automatically add more compute clusters when user demand spikes, keeping query speeds consistent even during the busiest times.
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Redshift: Redshift scales using a feature called "Elastic Resize," which lets you add or remove nodes. The catch is that this can take anywhere from a few minutes to over an hour and often needs to be done during a planned maintenance window. To handle sudden traffic spikes, Redshift has "Concurrency Scaling," which adds temporary capacity. It works, but it was really designed to patch an architectural limitation rather than being a core part of the design. For workloads that are constantly up and down, Snowflake’s built-in elasticity just feels smoother.
Maintenance and ease of use
This is where you’ll feel the biggest difference in your day-to-day work.
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Snowflake: As a true "software-as-a-service" platform, Snowflake is built to be low-maintenance. It automatically handles background tasks like cleaning up storage space ("vacuuming"), updating stats, and organizing data. For a lot of teams, this lets them pretty much set it and forget it, freeing up data engineers to do more important things.
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Redshift: Redshift requires a more hands-on approach. While AWS manages the hardware, it’s typically up to you to run maintenance commands like "VACUUM" and "ANALYZE". It’s a common problem for teams to neglect these tasks, which can cause performance to tank over time. It just requires more careful monitoring.
The ongoing work needed to manage even modern data platforms highlights the value of tools that are truly self-serve. For something like unifying support knowledge from different places, a platform like eesel AI gets you up and running in minutes without needing any engineers, which is a world away from the setup and constant tuning a data warehouse requires.
Data support and ecosystem
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Data Types: Snowflake has always been great at handling semi-structured data like JSON, Avro, and Parquet right out of the box. This lets you query it directly without needing to transform it first. Redshift has worked hard to catch up with its "SUPER" data type, but Snowflake generally still feels a bit more native with a wider variety of formats.
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Ecosystem: Redshift’s biggest strength is its seamless integration with the entire AWS family of services, like S3, Glue, and Kinesis. If you’re an AWS-first company, this is a huge plus. Snowflake’s advantage is that it’s cloud-agnostic, it runs on AWS, Azure, and GCP. This helps you avoid getting locked into one vendor and has helped it build a massive ecosystem of tech partners.
A full breakdown of Snowflake vs Redshift pricing
Getting a handle on the pricing models is super important because both can get complicated and lead to surprise bills if you’re not careful.
The Snowflake pricing model
Snowflake’s pricing is completely separated: you pay for compute and storage individually.
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Storage: You get a monthly bill for the average amount of compressed data you store. This usually works out to around $23 per TB per month in US regions.
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Compute: This is the tricky part. You pay for compute time by the second (with a 60-second minimum) using "Snowflake Credits." A virtual warehouse uses up credits any time it’s running, and the rate depends on its size (X-Small, Small, etc.).
| Edition | Price per Credit (AWS, US East) | Key Features |
|---|---|---|
| Standard | ~$2.00 | 1-day Time Travel, Core platform functionality |
| Enterprise | ~$3.00 | 90-day Time Travel, Multi-cluster warehouses, Column-level security |
| Business Critical | ~$4.00 | Tri-Secret Secure, Private connectivity, Failover/Failback |
| Virtual Private | Custom | Completely isolated environment |
Note: These are estimates from Snowflake’s pricing page and can change based on region and cloud provider.
This model is flexible, but it can make costs hard to predict. A single, poorly written query on a large warehouse can burn through a surprising amount of credits, so keeping an eye on costs is essential.
The Redshift pricing model
Redshift gives you two main ways to pay: Provisioned Clusters and Serverless.
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Provisioned Clusters: You pay a predictable hourly rate for each node in your cluster. This is great for steady, predictable workloads. You can pay as you go or save a bunch (up to 75%) with Reserved Instances if you commit to a 1 or 3-year term.
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Redshift Serverless: This model is more like Snowflake’s. You pay for compute capacity in Redshift Processing Units (RPUs) per second, but only when it’s active. It’s a good fit for spiky or intermittent workloads where you don’t want to pay for idle machines.
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Other Costs: Keep in mind that you’ll also see separate charges for things like Managed Storage (for RA3 nodes) and using Redshift Spectrum to query data directly from S3.
| Service / Node Type | On-Demand Price (US East, N. Virginia) | |---|---|---| | Provisioned: ra3.4xlarge | ~$3.26 per hour | | Serverless | Base cost ~$0.375 per RPU-hour |
Note: Prices are approximate and come from the AWS Redshift pricing page.
These multi-part pricing plans can make budgeting tough. In contrast, tools built for a specific job, like eesel AI, often have much clearer and more predictable pricing. With no per-resolution fees, support teams don’t have to stress about costs spiking during a busy month, a real fear with many usage-based platforms.
When to choose Snowflake vs Redshift: A summary
The right choice really boils down to your company’s specific needs, your current tech stack, and your budget.
Go with Snowflake if:
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Your workload is all over the place, crazy busy one hour and dead the next, and you need scaling to be instant and painless.
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You work across multiple clouds (AWS, Azure, GCP) or you just want to keep your options open.
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You want something that’s easy to use and requires as little manual maintenance as possible.
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You’re constantly working with semi-structured data like JSON or Parquet.
Go with Redshift if:
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Your company is all-in on AWS and you want to take advantage of those tight integrations.
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You have a steady, predictable workload and want to lock in big savings with Reserved Instances.
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Your team wants granular control over the data warehouse configuration and tuning.
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Predictable costs for a consistent, always-on workload are your main concern.
Beyond the Snowflake vs Redshift debate: Putting your knowledge to work for customer support
So, you’ve wrangled all your business data into Snowflake or Redshift. That’s awesome for analytics. But what about all the knowledge your support team uses? For most companies, it’s a mess, scattered across helpdesks like Zendesk, wikis in Confluence, random Google Docs, and endless threads in Slack.
This is where eesel AI comes in. It does for your support knowledge what a data warehouse does for your business data: it brings it all together.
eesel AI connects with all your company's apps to create a single source of truth for your support team.By connecting to all these different sources, including your past support tickets, eesel AI builds a single source of truth. With it, you can:
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Launch an AI Agent that can handle frontline customer questions on its own, 24/7.
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Give your team an AI Copilot that drafts instant, accurate answers right inside your helpdesk.
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Set up an AI Internal Chat in Slack or MS Teams, giving your whole company one place to get answers from all of your internal documents.
The eesel AI Copilot drafts instant, accurate answers for support agents directly within their helpdesk.Best of all, you can get it live in minutes and test it out safely with a simulation mode, a far cry from the months-long projects and engineering resources it takes to set up a data warehouse.
Snowflake vs Redshift: The final verdict
The whole "Snowflake vs Redshift" debate really comes down to a trade-off. Snowflake gives you amazing flexibility, scalability, and ease of use, which is perfect for dynamic, fast-moving companies. Redshift offers deep AWS integration and cost-effective power for more predictable, large-scale work. The right answer depends entirely on your situation, your existing tools, and your long-term data goals.
This video provides a deep dive into the key differences between Snowflake vs Redshift to help you make an informed decision.
Once your data is organized, the next logical step is to arm your teams with the knowledge they need. See how eesel AI can bring your support knowledge together and help automate your frontline support today.
Frequently asked questions
Snowflake’s architecture completely separates compute and storage, allowing them to scale independently. Redshift traditionally bundles compute and storage within a cluster, though newer node types offer some separation for storage.
Choose Snowflake for highly variable workloads, multi-cloud strategies, or a preference for minimal administrative overhead. Redshift is often ideal for AWS-centric companies with steady, predictable workloads who prioritize deep AWS integration and potentially lower costs through reserved instances.
Snowflake offers near-instant and unlimited scaling of compute resources (virtual warehouses). Redshift scales by resizing clusters, which can involve some downtime, but it also provides Concurrency Scaling to temporarily handle traffic spikes.
Snowflake is a fully managed SaaS that automates most maintenance tasks like "VACUUM" and "ANALYZE". Redshift, while managed by AWS, typically requires users to manually execute these maintenance commands to maintain optimal performance.
Snowflake’s pricing separates storage and compute (billed via credits), which offers flexibility but can be less predictable. Redshift offers predictable hourly rates for provisioned clusters or a serverless model based on compute capacity (RPUs) for fluctuating workloads.
Redshift boasts deep, native integration with the extensive AWS ecosystem. Snowflake, on the other hand, is cloud-agnostic, capable of running on AWS, Azure, and GCP, which can be advantageous for multi-cloud strategies or avoiding vendor lock-in.
