BigQuery vs Snowflake: A 2025 guide to cloud data warehouses

Picking the right cloud data warehouse feels like a huge, high-stakes decision, doesn’t it? It kind of is. This choice affects everything from how you analyze product usage to how you improve the customer experience. When you finally pull all your data into one place, you can start spotting trends, figuring out common support headaches, and finding real ways to get better.

Two of the biggest names you’ll hear are Google BigQuery and Snowflake. They both aim to solve the same core problem, but they go about it in completely different ways, with unique architectures and pricing models. Choose the wrong one, and you could end up with surprise bills or a system that just doesn’t quite click with your team’s workflow.

This guide is here to help you navigate the BigQuery vs Snowflake decision. We’ll get into the nitty-gritty of their architecture, pricing, performance, and what kind of companies they’re best for, so you can make a smart choice for your team in 2025.

What is Snowflake?

First up is Snowflake. You can think of it as the Switzerland of data warehouses, it’s cloud-neutral and plays nice with all the big three (AWS, Azure, and GCP). This is a huge plus for businesses that don’t want to be locked into a single ecosystem or already have their tech stack spread out.

The real magic of Snowflake is its architecture. It has a unique design that separates storage and compute. In plain English, this means you can scale your processing power up or down without touching your data storage, and vice versa. This gives you amazing control over performance and costs. Got a massive dataset but only run heavy-duty queries at the end of the month? You only pay for that extra muscle when you actually need it.

A few things that make it stand out:

• Virtual Warehouses: These are basically independent compute engines you can set up for different teams or tasks. This is a lifesaver for stopping resource turf wars. The data science team’s monster query won’t slow down the marketing team’s daily dashboard refresh.

• Time Travel: This feature lets you look at your data as it existed at any point in the past (up to 90 days, depending on your plan). It’s incredibly useful for recovering data, auditing changes, or undoing a mistake without needing to pull from a backup.

• Secure Data Sharing: You can share live, governed data with partners or other departments without having to create and email clunky, outdated copies. It keeps collaboration smooth and ensures everyone is on the same page.

Snowflake is a great fit for organizations that need multi-cloud support, want predictable performance for lots of concurrent users, and like having fine-grained control over their compute resources.

What is Google BigQuery?

Now let’s look at Google BigQuery. If your team is already living in the Google Cloud Platform (GCP) world, BigQuery is going to feel like coming home. It’s a fully managed, serverless data warehouse that’s deeply woven into the GCP ecosystem.

Its architecture is all about making things simple. Being serverless means you never have to think about provisioning or managing infrastructure. Ever. Built on Google’s legendary Dremel technology, BigQuery automatically finds and allocates the resources needed to run your queries, no matter how big. You just write your code, and Google’s robots handle the rest.

Here are some of its key features:

• Serverless Execution: You don’t configure clusters or virtual warehouses. You just run your queries, and BigQuery handles what’s needed behind the scenes. This makes it much easier for teams without dedicated data engineers to get started.

• BigQuery ML: This lets you build and run machine learning models using standard SQL, right inside the data warehouse. It makes things like predictive analytics much more accessible because you don’t need a data science degree to start building models.

• Real-Time Ingestion: BigQuery is built to handle high-speed streaming data right out of the box, making it a fantastic choice for live dashboards and apps that need the most up-to-date information possible.

BigQuery is an easy choice for teams that are all-in on GCP, want a zero-maintenance experience, and have workloads that are unpredictable or come in bursts.

Architecture and scalability

When you dig in, the BigQuery vs Snowflake choice often comes down to their core philosophies. It’s the classic trade-off: do you want more hands-on control, or do you prefer more automation?

Snowflake’s decoupled architecture

Snowflake’s platform is split into three layers: one for storage, one for compute (your virtual warehouses), and a "brain" layer on top that coordinates everything.

For you, this means you’re in the driver’s seat. You can spin up different-sized virtual warehouses for different needs, a small one for marketing’s daily reports and a beast for the data science team’s heavy lifting. This keeps workloads from stepping on each other’s toes. The flip side is that it requires a bit of active management. You’ll need to keep an eye on warehouse usage to make sure you aren’t overspending and that everything is running smoothly.

BigQuery’s serverless architecture

BigQuery hides all the infrastructure complexity. It’s built on the same internal tech that powers Google Search and YouTube, like Dremel (for queries), Colossus (for storage), and Jupiter (for networking).

For you, this means getting started is incredibly simple. You don’t manage servers or clusters. You just write your SQL query, and Google’s system, which uses "slots" (units of computational power), figures out how much horsepower is needed to get it done. This is perfect for teams who just want to focus on analysis, but it offers less direct control and can sometimes feel like you’re working with a black box.

Pricing models explained: A breakdown of BigQuery vs Snowflake costs

Alright, let’s talk about the part everyone’s really wondering about: the cost. The pricing for BigQuery vs Snowflake can be tricky because they bill you in totally different ways, which can lead to wildly different monthly totals depending on how you use them.

Snowflake pricing in detail

Snowflake’s pricing is pretty straightforward because it’s based on two separate things: compute and storage.

• Compute: You pay for the time your virtual warehouses are running, billed by the second (after the first 60 seconds). This usage is measured in "credits." A bigger warehouse uses more credits per hour but also finishes jobs faster. You can choose:

  • On-Demand: Pay a standard rate for credits as you go. It’s flexible but costs a bit more.

  • Pre-Purchased Capacity: Buy credits in bulk upfront for a nice discount. This is a great option for predictable workloads.

• Storage: You’re charged a flat monthly rate per terabyte (TB) of compressed data you store. On-demand storage costs around $40-$46 per TB, but that rate drops to about $23/TB if you pay upfront for capacity.

BigQuery pricing in detail

BigQuery’s pricing also separates storage and compute, but its compute model gives you more options, which can be both a blessing and a curse.

• Compute: You have two main ways to pay:

  • On-Demand: You pay for the amount of data your queries scan, usually $6.25 per terabyte (TiB). The first 1 TiB you scan each month is on the house. This is great for occasional queries but can get pricey if your team is constantly scanning massive tables.

  • Capacity (Editions): You pay a flat rate for a dedicated amount of processing power (slots), billed per hour. This gives you predictable costs and prevents those "oops" moments on the monthly bill. The Standard Edition starts at $0.04 per slot hour.

• Storage: BigQuery has a smart tiered model that saves you money on data you don’t use often.

  • Active Storage: Costs around $20 per TB per month for any data in tables that have been touched in the last 90 days.

  • Long-Term Storage: If a table sits unmodified for 90 days, the price automatically drops to about $10 per TB per month.

Performance, ecosystem, and turning data into action

Honestly, you’re not going to be disappointed with the speed of either platform. Both are ridiculously fast and can chew through petabytes of data. For standard business intelligence queries, benchmarks sometimes give Snowflake a slight edge due to its smart caching and workload isolation. But for real-time analytics and heavy machine learning tasks, BigQuery’s tight integration with the rest of Google Cloud gives it a home-field advantage.

The ecosystem is where you’ll see the biggest differences. Snowflake’s main selling point is being cloud-agnostic. It’s the clear winner for companies on AWS or Azure, or for anyone who wants to avoid vendor lock-in. BigQuery, on the other hand, is all about its deep roots in GCP. It has seamless connections to powerful tools like Vertex AI for machine learning, Dataflow for data processing, and Looker Studio for visualizations.

Digging into customer support data in either Snowflake or BigQuery can give you some powerful "aha!" moments, like seeing your most common ticket types or what topics are tanking your CSAT scores. But insights are only useful if you do something with them. While you could use BigQuery ML to build a model, a much faster way to see a return is to use an AI tool built for the job.

This video provides a detailed breakdown of the key differences between BigQuery and Snowflake, from their user interfaces to their underlying architecture.

Making the right BigQuery vs Snowflake choice for your team

So, after all that, who wins the BigQuery vs Snowflake showdown? The honest answer is that the "best" one is the one that fits your team.

• Choose Google BigQuery if: Your company is already deep into the Google Cloud ecosystem, you love the idea of a serverless, no-maintenance setup, and your workloads are spiky or need real-time data.

• Choose Snowflake if: You need the flexibility to run on AWS, Azure, or GCP, you want precise control over performance and cost for different teams, and your workloads are fairly predictable and involve a lot of concurrent users.

At the end of the day, the right data warehouse is the one that empowers you to not just understand your data, but to act on it.

Take the next step: Put your data to work

Once you’ve got your data house in order, the real fun begins. Instead of just looking at reports, you can start putting that data to work. While you get your warehouse set up, see how eesel AI can transform your customer service with a self-serve AI agent you can launch in minutes.

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