Snowflake vs BigQuery: The ultimate 2025 comparison

Picking the right cloud data warehouse is a huge deal for your company. It’s the bedrock for all your analytics and business intelligence, and it’s what ultimately lets you make smart, data-driven decisions. When you start looking around, two names pop up constantly: Snowflake and Google BigQuery.

While they both do a fantastic job, they’re built on completely different ideas. Snowflake is all about flexibility and not being tied down to one cloud provider. BigQuery, on the other hand, offers a simple, serverless experience that’s baked right into the Google Cloud ecosystem.

This guide will break down the real differences between them. We’ll get into their architecture, pricing, performance, and features to help you figure out which one makes the most sense for your business in 2025.

What is Snowflake vs BigQuery?

On the surface, Snowflake and BigQuery look pretty similar. They’re both modern cloud data warehouses built to chew through massive amounts of data. They store data in columns, use a technique called massively parallel processing (MPP) to run queries fast, and let you pay for storage and computing power separately. But that’s where the similarities end.

What is Snowflake?

Snowflake is a cloud-native data platform that can run pretty much anywhere. You can set it up on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Its core idea is to completely separate storage from compute. This means you can scale up your storage as your data grows without having to pay for more processing power, giving you tight control over performance and costs.

Its biggest draw is flexibility. You can create dedicated compute clusters (which they call "virtual warehouses") for different teams. So, if your data science team is running a massive job, it won’t slow down the dashboards your finance team relies on. This ability to isolate workloads, plus its multi-cloud support, makes it a great choice for companies that want to avoid getting locked into a single vendor.

What is BigQuery?

BigQuery is Google Cloud’s fully managed, serverless data warehouse. The word "serverless" is the key here. With BigQuery, you never see or think about the underlying infrastructure. You don’t have to set up clusters or manage virtual warehouses. You just load your data and start asking questions. Google takes care of all the resource management and scaling behind the scenes using its own heavy-duty tech like Dremel and Colossus.

The main benefit is how simple it is to get started. You can go from having no data warehouse to querying petabytes of data in minutes, all without worrying about servers. This, along with its seamless connection to other Google Cloud tools, makes it a fantastic option for teams that just want to get to the insights as fast as possible.

Snowflake vs BigQuery architecture: Flexibility vs. simplicity

The architectural difference between Snowflake and BigQuery is the most important thing to grasp because it affects everything else, from how much you pay to how your team works.

Snowflake’s multi-cluster shared data architecture

Snowflake’s setup has three distinct layers that can each be scaled on their own:

1. Centralized Storage: This is where all your data lives. Snowflake simply uses the storage service of your chosen cloud provider, like AWS S3, Azure Blob Storage, or Google Cloud Storage.

2. Multi-Cluster Compute: This is where the queries happen. You run them using "virtual warehouses," which are just clusters of compute resources. The cool part is you can have multiple warehouses of different sizes running at the same time, all pulling from the same data. You could have a small one for BI tools, a huge one for big data transformation jobs, and another one just for a few data scientists. They won’t step on each other’s toes.

3. Cloud Services: This is the brain of the operation. It’s a collection of services that manages everything from query planning and security to metadata and infrastructure. It’s what makes the whole system run smoothly without you having to intervene.

BigQuery’s serverless architecture

BigQuery’s architecture is intentionally a bit of a "black box." As a user, you just write SQL queries, and Google’s massive infrastructure figures out the rest. What’s happening under the hood is a combination of some serious tech:

• Dremel: The query engine that takes your SQL, breaks it into tiny pieces, and runs it across thousands of servers at once.

• Colossus: Google’s internal file system that stores your data and handles all the replication and recovery.

• Jupiter: The super-fast internal network that zips data between storage (Colossus) and compute (Dremel).

The bottom line for you is that you don’t have to manage any of it. BigQuery automatically scales to handle your queries, whether you’re scanning a tiny table or a petabyte-sized one. There are no settings to tweak or warehouses to configure.

Why architecture matters for your team

The choice really boils down to control versus convenience.

• Snowflake is for teams that want fine-grained control. You can manage your budget and performance with precision, spinning up powerful warehouses for tough jobs and then shutting them down to save money. It’s ideal for organizations with lots of different teams running queries at the same time who need to guarantee performance for everyone.

• BigQuery is for teams that value simplicity and speed. If you don’t want to spend any time on infrastructure management and prefer a hands-off approach, it’s a great fit. You get incredible power without the administrative headache.

Snowflake vs BigQuery pricing models: Granular control vs. predictable simplicity

Pricing is another area where these two platforms are quite different, and it can really impact your final bill depending on your usage patterns. Both charge for compute and storage separately, but they bill for them in very different ways.

Snowflake’s pricing explained

Snowflake’s pricing is based entirely on usage. You pay for two things:

• Compute: This is billed in "Snowflake credits" and you pay by the second (with a one-minute minimum). The credits you burn through depend on the size of the virtual warehouse you’re running. The moment you suspend a warehouse, the billing stops.

• Storage: This is a separate, flat rate per terabyte, per month. The cost can differ based on your region and whether you choose to pay as you go or pre-pay for capacity to get a discount.

Here’s a quick look at their on-demand pricing. Just remember that these are estimates and you should always check the official Snowflake pricing page for the latest numbers.

Edition	Compute (On-Demand, per Credit)	Key Features
Standard	~$2.00	Core functionality, 1-day Time Travel, fully managed.
Enterprise	~$3.00	All Standard features + Multi-cluster warehouses, up to 90-day Time Travel.
Business Critical	~$4.00	All Enterprise features + Enhanced security & compliance (e.g., HIPAA).

BigQuery’s pricing explained

BigQuery also separates compute and storage, but gives you more options for how you pay for compute.

• Compute: You can choose between two models:

  1. On-Demand: You pay for the amount of data your queries scan (e.g., dollars per terabyte). This is perfect when you’re starting out or have workloads that are hard to predict.

  2. Capacity (Editions): You pay a flat hourly rate for dedicated processing capacity, measured in "slots" (which are like virtual CPUs). This gives you predictable costs for consistent, high-volume work.

• Storage: Billed per gigabyte per month. One nice perk is that if a table isn’t modified for 90 days, the storage price for it automatically gets cut in half.

Here’s a simplified breakdown. Again, for the most accurate details, head over to the official BigQuery pricing page.

Model	Compute Pricing	Storage Pricing (per GiB/month)	Best For
On-Demand	~$6.25 per TiB scanned (first 1 TiB free)	~$0.02 (Active), ~$0.01 (Long-term)	Ad-hoc queries, unpredictable workloads, getting started.
Capacity (Standard Edition)	~$0.04 per slot-hour (Pay-as-you-go)	~$0.02 (Active), ~$0.01 (Long-term)	Consistent, predictable workloads with a need for stable costs.

A few cost scenarios

• Spiky Workloads: Think of a marketing team that runs a few big, complicated queries once a week to analyze campaign results. With BigQuery’s on-demand model, they’d only pay for the data scanned during those specific moments, which could be very cheap.

• Consistent BI Workloads: A company with BI dashboards that are being hit with queries 24/7 might find Snowflake more predictable. They could run a small, dedicated warehouse around the clock for a steady monthly cost.

• ETL and Data Science: A data engineering team that needs a ton of power for a few hours every night could spin up a massive Snowflake warehouse, run their jobs, and then shut it down, only paying for the exact time it was active.

Snowflake vs BigQuery: Performance, ecosystem, and key features

Beyond architecture and price, the little details and surrounding tools often sway the final decision.

Performance and scalability

Let’s be clear: both platforms are incredibly fast at scale. For most jobs, you probably won’t see a huge performance difference. The real distinction is how you manage that performance.

• Snowflake: Performance is directly tied to the size of your virtual warehouse. If a query is slow, you have a simple solution: make the warehouse bigger. This gives you a direct lever to pull to boost performance when you need it.

• BigQuery: Performance is completely managed by Google. It’s known for throwing thousands of slots at big queries automatically, making it lightning-fast for ad-hoc analysis without any tuning. You’re basically trusting Google to handle it, and they’re very good at it.

Ecosystem and integrations

This is where your current tech stack really starts to matter.

• Snowflake: Its main advantage is that it’s cloud-agnostic. Because it runs on AWS, Azure, and GCP, it’s the natural choice for companies with a multi-cloud strategy or those who want to avoid being tied to one ecosystem.

• BigQuery: Its strength is its deep, native integration with Google Cloud. If you’re already using tools like Vertex AI for machine learning or Looker for BI, BigQuery feels less like a separate tool and more like part of a unified platform.

This video offers a clear breakdown of the key differences between Snowflake and BigQuery, from their object structures to their core philosophies.

Both of these data warehouses are great for centralizing business knowledge, like all your historical customer support chats. But while you can analyze that data, getting it into the hands of your frontline teams in real time is a totally different problem. Support agents often struggle because that valuable information is stuck in the data warehouse, while other answers live in a helpdesk like Zendesk or an internal wiki like Confluence.

This is where a tool like eesel AI can connect the dots. It can learn from all that historical ticket data in your warehouse and combine it with your live help docs to create an AI agent that gives instant, accurate answers to your support team.

Snowflake vs BigQuery: Making the right choice for your business

This isn’t about which platform is "better." It’s about which one is a better fit for your team’s skills, workflow, and existing tech.

Factor	Snowflake	BigQuery
Best For	Control, workload isolation, multi-cloud strategy.	Simplicity, auto-scaling, deep GCP integration.
Architecture	Decoupled storage & compute (user-managed).	Serverless (fully managed by Google).
Pricing Model	Pay for compute time (credits) + storage.	Pay for data scanned (on-demand) or capacity (slots) + storage.
Management	Low, but requires managing virtual warehouses.	Near-zero, fully automated.
Cloud Platform	AWS, Azure, GCP.	GCP only.

Snowflake vs BigQuery: Your data is only as good as what you do with it

To wrap it all up, the choice becomes pretty clear once you know your priorities.

Choose Snowflake if you need multi-cloud flexibility and want fine-grained control over performance and cost to manage a wide variety of workloads.

Choose BigQuery if you’re already in the Google Cloud world and you value a simple, fully automated experience that just works.

Either way, remember that having a data warehouse is just step one. The real value comes from actually putting that data to work. Your data warehouse holds a goldmine of customer knowledge, but analysis alone doesn’t solve customer problems in the moment. eesel AI plugs directly into your knowledge sources to power AI agents that deliver instant, accurate support. Instead of just analyzing past tickets, eesel learns from them to automate resolutions today, turning your stored data into a true frontline tool.