BigQuery vs Redshift: A comprehensive comparison in 2025

Making smart decisions with data isn’t just a nice-to-have anymore; it’s how modern businesses stay ahead. The engine for all of this is a cloud data warehouse, the one place all your analytics data calls home. When you start looking for one, two names pop up constantly: Amazon Redshift and Google BigQuery. They’re both heavyweights, but they were built with very different ideas in mind.

Trying to pick between them can feel a bit overwhelming. The best choice really boils down to your team’s needs, budget, and how hands-on you want to be. This guide will cut through the noise in the BigQuery vs Redshift debate, comparing them on architecture, performance, price, and what they’re like to use day-to-day, so you can figure out which one is the right fit for you.

What is a cloud data warehouse?

Think of a data warehouse as the central library for all your company’s data. It pulls information from all over the place, your CRM, sales tools, support platforms, and gets it organized for one specific purpose: analysis.

Not too long ago, companies had to run these warehouses on their own servers in a closet somewhere. It was expensive, clunky, and a total nightmare to scale. Moving to the cloud changed the game, offering a much cheaper and more flexible way to handle massive datasets.

To get why this matters, it helps to know the difference between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems. Your everyday apps, like a point-of-sale system ringing up a purchase, are OLTP. They’re built for lots of tiny, fast transactions. Data warehouses are OLAP systems. They’re designed to scan huge amounts of data to answer big, complex questions, like, "Which marketing campaign brought in our most valuable customers last year?"

What is Google BigQuery?

Google BigQuery is Google Cloud’s fully managed, serverless data warehouse. The magic word here is serverless. You don’t have to provision, configure, or manage any infrastructure. At all. You just load up your data and start writing SQL queries. Behind the curtain, Google’s Dremel engine figures out how much horsepower you need, spins it up, and gets the job done. This makes it amazing for scaling on the fly and running huge, one-off queries without any prep work.

What is Amazon Redshift?

Amazon Redshift is AWS’s giant, petabyte-scale data warehouse. Unlike BigQuery, it’s a cluster-based system, which means you have to provision a "cluster" by picking the number and type of servers (or nodes) you need. This approach gives you a ton of control over performance and cost, making it a solid choice for predictable, reporting-heavy workloads. It’s also plugged into the massive AWS ecosystem, which is a big deal if your company already lives there.

Comparing architecture and scalability

The biggest difference between these two is how they’re built. Their architecture is what shapes how they scale, how you manage them, and ultimately, whether you’ll find them a joy to work with or a constant headache.

BigQuery’s ‘just works’ serverless model

BigQuery was designed to be simple. It keeps its storage and its computing power completely separate. When you run a query, Google just assigns the resources needed (called "slots") to execute it. When it’s done, those resources go away.

• The upside: Scaling is effortless, especially if your workload is spiky or unpredictable. You don’t have to manage any infrastructure, resize clusters, or worry about downtime. It just works.

• The downside: This hands-off approach means you have less direct control. For very specific, consistent workloads, you might find its performance isn’t as predictable as a cluster you’ve tuned yourself.

Redshift’s ‘you’re in control’ cluster model

Redshift uses a more traditional provisioned cluster. You decide how many nodes you need and what type they should be. Even with newer versions like RA3 nodes that separate storage and compute, you’re still managing the compute part as a distinct cluster that you scale up or down yourself.

• The upside: You get really fine-grained control over performance and cost. It’s perfect for stable BI reporting where you need the same queries to run at the same speed, every single day.

• The downside: With that control comes more responsibility. You’re on the hook for manual scaling and for pausing and resuming clusters to save money. This adds a layer of management and requires some technical expertise to optimize things like distribution keys and run maintenance tasks to keep it all humming.

While having that level of control is great for some, the general trend in software is toward simpler tools that don’t require a team of engineers to run. For instance, platforms like eesel AI let support teams build and launch AI agents in minutes without touching a line of code, a job that used to take up weeks of developer time.

Performance and common use cases

Those architectural differences have a big impact on how each platform performs. There isn’t a single "faster" one; it really depends on what you’re doing.

When BigQuery shines for exploring huge datasets

BigQuery is built to throw a ton of parallel resources at a single query. That makes it incredibly fast for ad-hoc, exploratory analysis on massive amounts of data. If your data team is constantly asking new and complicated questions that require scanning giant tables, BigQuery will probably feel like a rocket ship.

• Perfect for: Data mining, prepping data for machine learning models, and running those occasional but very heavy queries. It’s a favorite for data scientists who just want to dig into the data without worrying about the plumbing.

Where Redshift excels with consistent BI and dashboards

Redshift’s strength is its consistency. Since you’ve set aside a dedicated chunk of resources, it’s built to deliver reliable, fast performance for the same queries, time and time again. You can also fine-tune it with things like sort keys to make those repetitive queries even faster.

• Perfect for: Powering BI dashboards in tools like Tableau or Amazon QuickSight, running daily financial reports, and handling lots of concurrent users where predictable speed is everything. It’s often the top choice for enterprise business intelligence teams.

Management and ease of use

Beyond pure speed, it’s worth thinking about what it’s like to live with each platform.

The simplicity of BigQuery

BigQuery is designed to need almost no database administration. There are no indexes to create, no cleanup commands to run, and no clusters to configure. You can be loading data and running queries in minutes. It also handles nested data like JSON natively, which you often have to flatten out before loading into Redshift.

The hands-on approach of Redshift

Redshift puts you in the driver’s seat, which also means you’re the mechanic. You’ll be choosing node types, setting up distribution and sort keys to optimize queries, and running maintenance tasks every so often. This gives power users a lot of levers to pull, but it also means a steeper learning curve and often requires someone with DBA skills on the team.

BigQuery is good if you don't have DBA expertise or resources to manage a cluster. Redshift is for when you need more control/tuning for a predictable workload and you do have the resources.

Reddit

A detailed breakdown of BigQuery vs Redshift pricing

Let’s talk money. Pricing is a huge factor, and both platforms have models that can be either very cheap or surprisingly expensive, all depending on how you use them.

BigQuery’s pay-per-use model

BigQuery splits its pricing into two buckets: compute (running queries) and storage.

• Compute (Analysis) Pricing:

  • On-demand: You pay for the amount of data your queries scan. The standard rate is $6.25 per terabyte (TiB), and you get your first TiB free each month. This is great when you’re starting out or have unpredictable needs.

  • Capacity (Editions): For more predictable costs, you can reserve a set amount of processing power (measured in "slot-hours"). This starts at $0.04 per slot-hour and makes sense for consistent, high-volume workloads.

• Storage Pricing:

  • Active Storage: You’ll pay about $0.02 per GB per month for data in tables that have been changed in the last 90 days.

  • Long-term Storage: If a table hasn’t been touched in 90 days, the price automatically drops to about $0.01 per GB per month.

Just remember that streaming data into BigQuery or using other related services can have their own costs.

Redshift’s provisioned pricing model

With Redshift, you’re mostly paying for the compute cluster you’ve set up.

• Compute (Node) Pricing:

  • On-demand: You pay an hourly rate based on the type and number of nodes in your cluster. A popular "ra3.xlplus" node, for example, costs $1.086 per hour. A huge plus here is that you can pause your cluster when you’re not using it to save cash.

  • Reserved Instances: If your workload is steady, you can commit to a 1- or 3-year term to get huge discounts, sometimes up to 75% off the on-demand rate.

• Managed Storage Pricing (for RA3 nodes):

  • This is billed separately from your compute nodes at around $0.024 per GB per month.

• Serverless Option: To better compete with BigQuery’s simplicity, Redshift now offers a serverless option. It’s billed in "Redshift Processing Units" (RPUs) per hour, starting at $0.36 per RPU-hour.

This video provides a concise and detailed comparison of BigQuery vs Redshift, covering key differences in architecture, performance, and more.

BigQuery vs Redshift: Which data warehouse is right for you?

So, after all that, which one should you choose? The BigQuery vs Redshift decision really comes down to a trade-off: do you want simplicity or do you want control?

• Choose BigQuery if: You want to spend less time managing infrastructure and more time analyzing data. It’s perfect if your query patterns are all over the place, your team lives in the Google Cloud ecosystem, or you have data scientists who need to run massive, exploratory queries without filing a ticket with a DBA.

• Choose Redshift if: You need rock-solid, predictable performance for your BI dashboards and reports. It’s the better pick if your workloads are stable, you want fine-grained control over resources to manage costs, and you’re already heavily invested in AWS.

At the end of the day, there’s no single "best" data warehouse. The right one is the one that fits your team’s skills, your company’s budget, and your actual goals.

Beyond analytics: Connecting your team’s knowledge

While BigQuery and Redshift are amazing for wrangling your structured data, a huge chunk of your company’s real wisdom, old support tickets, internal wikis, project docs, is scattered everywhere in unstructured formats. This is where an AI knowledge platform can make a massive difference.

eesel AI plugs into all your company’s apps and knowledge sources, from help desks like Zendesk and Freshdesk to wikis like Confluence and Google Docs. It pulls all that scattered knowledge together to power AI agents that can automate customer support, help your human agents write better replies, and give instant, accurate answers to your internal teams right in Slack or Microsoft Teams.

An eesel AI agent assists a support representative inside Zendesk, providing instant answers and drafting replies based on the company's knowledge base.

If you’re looking to turn your team’s collective brainpower into an automated support engine that actually works, give eesel AI a try for free.