Snowflake vs. BigQuery: What Are the Differences, and Which Should You Choose?

As more companies trade their on-premises infrastructures for cloud data warehouses, two of the top contenders are Snowflake vs. BigQuery. Both platforms are cost-effective and scalable, but differ significantly in their architecture, pricing models, and overall performance.

If you’re looking to see which cloud data platform is best for your needs, this comprehensive guide is a must-have for making an informed, strategic choice. 

How Is Snowflake’s Architecture Structured?

Snowflake separates its architecture into three layers: storage, compute, and services. Keeping these layers separate lets you scale storage and compute independently and give each workload its own compute.

Storage Layer

Snowflake’s storage layer holds all data in a centralized repository. Your account runs on AWS, Azure, or Google Cloud, and Snowflake manages the underlying object storage for you.

Data is divided into immutable micro-partitions and compressed in a columnar format. Because Snowflake compresses and organizes data automatically, there’s little to no manual maintenance.

Compute Layer

Snowflake’s compute layer consists of virtual warehouses: independent clusters of compute that execute queries and data manipulation tasks. Warehouses are stateless, so they can be started, stopped, resized, and cloned without affecting data.

Because each warehouse runs in isolation, workloads on separate warehouses don’t compete for resources. Within a warehouse, multi-cluster scaling adds clusters as concurrency rises, and auto-suspend stops the warehouse when it’s idle.

Services Layer

Snowflake’s services layer coordinates operations across the platform: metadata management, security enforcement, authentication, infrastructure management, query parsing and optimization, and access control.

Other Highlights of Snowflake’s Architecture

• Centralized storage combined with distributed compute.
• Storage and compute scale independently to meet changing workload demands.
• Multi-cluster warehouses for concurrency and workload isolation.
• Time Travel to access historical data.
• Result caching and micro-partition pruning to reduce query latency.

How Is BigQuery’s Architecture Structured?

Google BigQuery’s architecture has three main components: distributed storage, a compute engine, and a high-speed network. This design lets BigQuery handle large datasets while staying flexible across diverse workloads.

Distributed Storage

BigQuery uses Google’s Colossus file system, a highly fault-tolerant, available, and durable system. Colossus stores data in a columnar format, enabling fast analytical queries. 

Data is organized into datasets that hold tables. Each table has a schema that defines columns and data types, with support for nested and repeated fields. That gives you a logical way to model complex data structures.

BigQuery also supports partitioning and clustering to reduce the data each query scans.

Compute Engine

BigQuery processes SQL queries using Dremel, a massively parallel query engine that uses a tree architecture: 

• A root server that receives the query from the client and routes it to mixers
• Mixers aggregate intermediate results from leaf nodes
• Leaf nodes read the data, perform initial computations, and send the result to mixers
• Slots are the smallest computational unit, combining CPU, memory, and networking resources to execute tasks

Networking and Orchestration

BigQuery’s compute engine runs on Jupiter, Google’s high-speed internal network that connects compute and storage. Borg, Google’s cluster management system, orchestrates Dremel’s query execution.

Together they handle resource allocation and communication between components, and keep latency low.

Other Highlights of BigQuery’s Architecture

• Columnar storage that scans only the columns a query needs.
• A serverless model with no resources to provision or manage.
• Integration with the Google Cloud ecosystem, including Cloud Storage and Bigtable.

Snowflake Pricing

Snowflake’s pricing model is based on usage and consists of three components: 

• Storage: files staged for bulk loading and unloading, database tables, historical data (Time Travel and Fail-safe), clones, and more.
• Compute: Snowflake credits consumed when you run queries, load data, or perform other DML operations on a virtual warehouse. Larger warehouses consume more credits per hour.
• Data transfer: applies when you move data out of a region or to another cloud provider.

Your Snowflake costs also depend on your edition (Standard, Enterprise, Business Critical, or Virtual Private Snowflake) and whether you buy capacity up front (Capacity) or pay as you go (On-Demand).

Snowflake also offers Snowpark Container Services (SPCS), a fully managed container offering that runs containerized workloads inside Snowflake. Snowflake’s AI services, including document processing, text completion, translation, answer extraction, and sentiment analysis, are priced and billed separately.

To track and cap spend, Snowflake provides resource monitors, budgets, and per-warehouse auto-suspend settings.

BigQuery Pricing

BigQuery offers two compute pricing models:

• On-demand pricing: you pay per TiB of data your queries scan ($6.25 per TiB in us-central1, with the first 1 TiB each month free).
• Capacity pricing: you pay for slots (virtual CPUs) per slot-hour, in Standard, Enterprise, or Enterprise Plus editions. The slot autoscaler adds capacity as demand rises, and one- or three-year commitments lower the rate.

Storage is billed separately at active and long-term rates. Long-term rates apply to tables or partitions that haven’t been modified for 90 days. BigQuery Omni, BigQuery ML, BI Engine, and streaming each have their own pricing.

Prices above are for us-central1 as of October 2026. For current rates in your region, see Google’s BigQuery pricing page.

Key Factors When Choosing Snowflake vs. BigQuery

The right choice depends on your data architecture, your priorities, and how much of the platform your team wants to manage directly. If Databricks is also on your shortlist, our Snowflake vs. Databricks comparison applies the same lens to that decision.

We’ve covered architecture and pricing. Here are the other factors that should drive the decision.

Performance

In an often quoted TPC-H benchmark report, Snowflake outperforms BigQuery by about 10%. While that can be a helpful topline figure, it doesn’t take into account variance in workloads and that, in some situations BigQuery may come out ahead.

The useful test is your own. Run your top queries on both platforms with comparable compute, and compare cost per query, not just runtime.

Scalability

Snowflake offers fine-grained control when it comes to scaling storage and compute resources, with each layer functioning independently of the other. On the other hand, BigQuery scales automatically with its serverless model. 

If control over scale and cost efficiency is a top priority for you, Snowflake is a better choice. If, on the other hand, you want to hand off scalability to a machine, BigQuery has better native functionality.

There’s also a third option that gives you the best of both worlds: Keebo works on top of Snowflake’s controls and keeps them tuned continuously, so you keep the control without doing the tuning by hand. Set up a demo to see our platform in action. 

Ease of Use

Both Snowflake and BigQuery are fully managed and don’t require access to the underlying cloud infrastructure.

Snowflake runs natively on AWS, Azure, and Google Cloud, so you can deploy in the cloud you already use. BigQuery runs on Google Cloud and can query data in AWS and Azure through BigQuery Omni.

Data Sharing

Both platforms let you share live data without copying it.

BigQuery shares datasets through BigQuery sharing (formerly Analytics Hub), centered on Google Cloud. Snowflake Secure Data Sharing and Marketplace reach consumers on AWS, Azure, and Google Cloud, across regions.

If your partners or business units run on more than one cloud, that reach is the deciding factor.

Snowflake vs. BigQuery: Which Platform Should You Choose?

Each platform has clear strengths.

In our view, Snowflake is the stronger fit when you want direct control over compute and cost, run on more than one cloud, or share data with partners outside Google Cloud. BigQuery is the stronger fit when you’re standardized on Google Cloud and want scaling handled for you.

Snowflake also gives you direct control over cost and performance: warehouse size, auto-suspend, and multi-cluster scaling. Keebo builds on those controls, adjusting them continuously as your workloads change so costs stay aligned without compromising performance.