A complete Snowflake overview for 2025

Over the last few years, there’s been a huge move to cloud data platforms, and one name keeps popping up everywhere: Snowflake. Thousands of companies, from brand-new startups to the Fortune 500, are using it to handle their analytics and AI.

But even though it’s clearly a big deal, trying to figure out what Snowflake is, how it works, and what it costs can feel like you’re piecing together a puzzle without the box.

That's what this guide is for. We'll give you a clear, straightforward Snowflake overview, breaking down its architecture, features, and pricing. You’ll walk away knowing exactly what it does and, just as importantly, what it doesn't.

What is Snowflake?

At its core, Snowflake is a cloud-based data platform, which you'll often hear called a "data warehouse-as-a-service." Its main job is to store, process, and analyze massive amounts of data for things like business intelligence (BI), data science, and other AI projects.

Here's a simple way to think about it: a regular database is like a cash register at a busy store, built to handle lots of small, quick transactions one after another. Snowflake, on the other hand, is like a massive research library designed to search through huge amounts of information to spot trends and find insights. It’s built for big-picture analysis, not for running your daily operations.

A big piece of its popularity comes from the fact that it runs on the big three cloud providers (AWS, Azure, and Google Cloud). This means companies get all of its power without ever having to buy or manage their own physical hardware.

Infographic comparing traditional databases and Snowflake in this complete Snowflake overview.

Snowflake's unique architecture

The secret sauce behind Snowflake is its architecture, which completely separates data storage from computing power. This is a huge shift from older systems where those two things were stuck together.

Imagine a library where all the books (your data) are stored in one giant, central warehouse. Now, instead of everyone crowding into a single reading room, you can create as many separate, soundproof reading rooms as you need. The marketing team gets one, the finance team gets another, and data scientists can have their own. They all access the same collection of books, but no one’s work slows anyone else down. That's pretty much how Snowflake is set up.

It’s all broken down into three main layers.

A diagram showing the three layer architecture covered in this Snowflake overview.

Database storage

This is the foundation where all of your data is kept. When you load data into Snowflake, it automatically gets organized, compressed, and optimized into a columnar format that's incredibly efficient for running analytics. You never have to mess with this layer directly or worry about file sizes and folder structures; Snowflake just handles it for you.

Query processing (compute)

This is where the actual number-crunching happens. Snowflake uses "virtual warehouses," which are just independent clusters of computers that run your queries. These are the "reading rooms" from our library example.

The real advantage here is that different teams can have their own dedicated virtual warehouses. If the marketing team needs to run a massive report, they can fire up a large warehouse for a couple of hours without disrupting the finance team's daily dashboards. Once they're finished, they can scale it down or shut it off completely to save money.

Cloud services

This is the brain of the whole operation. It’s a group of services that coordinates everything happening on the platform. It handles security and logging in, manages all the infrastructure, optimizes your queries before they even run, and keeps track of your data's metadata. This layer is what makes Snowflake a true "as-a-service" platform because it takes care of all the complicated admin tasks that used to require a whole team.

Core features and common use cases

This setup enables some seriously useful features that have made Snowflake a favorite among data teams.

• Scalability and performance: Since compute and storage are separate, you can scale them up or down independently. Got a ton of data but only a few people running queries? No problem. Need to support hundreds of users running complex reports at the same time? Just add more virtual warehouses. The platform can handle huge workloads without breaking a sweat.

• Data sharing: This is one of Snowflake's most talked-about features. You can securely share live, read-only data with partners, customers, or other departments without having to create slow, out-of-date copies. The data is always current, and you stay in full control of who sees what.

Screenshot of the data sharing feature in this Snowflake overview.

• Time travel: Have you ever accidentally deleted a huge table? With Snowflake, you can get it back instantly. The "Time Travel" feature lets you access older versions of your data from a specific point in the past, basically acting as a powerful "undo" button for your entire data warehouse.

The Time Travel feature as explained in this Snowflake overview.

• Broad ecosystem: Snowflake connects well with just about every tool in the modern data world. It has native connectors for BI platforms like Tableau, data integration tools, and languages like Python and Spark.

• Security and governance: It was built with enterprise-level security in mind, including end-to-end encryption for all data, role-based access controls, and a long list of compliance certifications.

But there’s a key limitation to keep in mind. Snowflake is brilliant for managing structured data (think spreadsheets and database tables) and semi-structured data (like JSON files). It is not, however, designed to understand the unstructured, conversational data you get from support tickets, internal wikis, or chat messages. Getting useful information out of that kind of text requires a totally different tool.

Snowflake pricing

Snowflake runs on a consumption-based, "pay-as-you-go" model. For many, this flexibility is a major selling point, but it can also make your monthly bill hard to predict.

Your costs are split into two main buckets:

1. Storage costs: This part is simple. You pay a flat rate per terabyte (TB) of data you store each month. It's usually predictable and relatively affordable.

2. Compute costs: This is where it gets more complicated. You're billed by the second for the time your virtual warehouses are running. The larger the warehouse, the more "credits" it uses per hour. This is the part of your bill that can get out of hand if you aren't carefully watching your usage.

Here’s a quick look at their main editions, using pricing for AWS in the US East region as an example.

Edition	Price per Credit (AWS/US East)	Who It's For	Key Features
Standard	~$2.00	Startups and small teams	Core functionality, 1-day Time Travel, fully managed.
Enterprise	~$3.00	Large-scale enterprises	Everything in Standard, plus multi-cluster warehouses, up to 90-day Time Travel, advanced security.
Business Critical	~$4.00	Organizations with highly sensitive data	Everything in Enterprise, plus enhanced encryption and compliance (HIPAA, PCI), and data failover.

While this model offers flexibility, the variable compute costs can give finance teams a real headache. It often takes a dedicated person or team (sometimes called FinOps) just to forecast and manage spending. This uncertainty is a common frustration, especially when compared to solutions with more straightforward pricing.

The missing piece: Unstructured support knowledge

So, Snowflake does an incredible job of organizing a company's structured business data. But what about your support and IT teams? They're grappling with a completely different kind of data problem. Their knowledge is almost entirely unstructured and spread out all over the place.

Just think about it: you have thousands of past tickets in Zendesk, internal process guides in Confluence or Google Docs, and endless Q&A threads buried in Slack.

How are you supposed to bring all of that messy, conversational data together to give customers and employees instant, accurate answers? Snowflake isn't built for that; it's the wrong tool for that job. That problem needs a different kind of tool entirely.

Unify your support data instantly with eesel AI

You can think of eesel AI as the "Snowflake for your support knowledge." It's an AI platform built specifically to pull together scattered, unstructured data from all your workplace apps to automate frontline support and power internal Q&A.

Here's how it solves the exact problems we've been talking about:

• It connects everything, right away: While Snowflake is busy with databases, eesel AI links directly to your helpdesk, wikis, and chat tools. It trains on your past support tickets and documents to learn your business from day one.

* **You can be up and running in minutes, not months:** A data warehouse project can take an entire quarter or more. You can set up eesel AI yourself in a few minutes. With one-click integrations for tools like **[Zendesk](https://www.eesel.ai/integration/zendesk)** and **[Confluence](https://www.eesel.ai/integration/confluence)**, there's no complex developer work needed. * **Test it out with zero risk:** Worried about how a new AI tool will perform? eesel AI's simulation mode lets you test your setup on thousands of your own past tickets. You can see exactly how it will perform and what your resolution rate would be *before* it ever talks to a live customer.

Snowflake overview: Use the right tool for the right data

So, what's the bottom line? Snowflake is an incredibly powerful and scalable cloud data platform. It has totally changed the game for business analytics by making it possible to manage and analyze huge amounts of structured data.

But it isn't the solution for every data problem out there. It was never designed to handle the messy, unstructured, conversational knowledge that your customer support and internal teams rely on every day.

* **No surprise bills:** Forget about unpredictable compute credits. eesel AI offers **[simple, predictable plans](https://www.eesel.ai/pricing)** with no per-resolution fees. You won't get a heart attack from your bill after a busy month.

For that, you need a platform that was built for the job, one that can unify your scattered knowledge, connect easily to the tools you already use, and start delivering value in minutes, not months.

This video provides a great visual Snowflake overview, breaking down its architecture and key concepts.

If you’re ready to unlock the value hiding in your support conversations and documents, see how eesel AI can help you get started in minutes.