Redshift vs BigQuery: The 2025 guide to cloud data warehouses

Let’s be honest, making "data-driven decisions" has gone from a trendy buzzword to the absolute backbone of any company that wants to stay competitive. The engine powering all of this? A cloud data warehouse. It’s where you store and make sense of mountains of information. But picking the right one is a huge deal, a decision that will define your company’s analytics game for years.

Two of the biggest names you’ll hear are Amazon Redshift and Google BigQuery. Both are powerhouses, but they come at the problem from completely different angles. If you choose the one that doesn’t fit your team’s workflow, you could be looking at runaway costs, frustrating performance issues, or a ton of manual upkeep you just don’t have time for.

This guide is here to give you a straight-up, practical comparison. We’ll look at how they’re built, how they perform, what it’s like to use them day-to-day, and of course, how they charge you. By the end, you should have a much clearer picture of which one makes the most sense for you.

What is a cloud data warehouse?

Before we get into the nitty-gritty of a head-to-head battle, it helps to get a feel for what each platform is all about. They both aim to help you analyze data, but their DNA is fundamentally different.

What is AWS Redshift?

Amazon Redshift is Amazon’s big, powerful data warehouse service. The easiest way to think about it is like a traditional data warehouse, but redesigned and optimized for the cloud. It’s built around a cluster of nodes. You have a "leader node" that acts as the traffic cop, taking in your queries and figuring out the smartest way to run them. Then you have a bunch of "compute nodes" that store the data and do the actual number crunching.

Under the hood, Redshift uses columnar storage and a Massively Parallel Processing (MPP) architecture. That’s a fancy way of saying it’s designed from the ground up to tear through complex analytical queries at high speed. It’s also plugged deep into the AWS ecosystem, so connecting it to services like Amazon S3 for storage is a breeze. That tight integration is a massive win if your company already runs on AWS, but it does mean you should be ready for a more hands-on approach to get the absolute best performance.

What is Google BigQuery?

Google BigQuery is Google’s answer to the data warehouse, and it takes a completely different, serverless path. Its killer feature is that it separates storage from compute. This might sound technical, but it’s a huge deal because it allows both to scale up or down on their own, automatically.

With BigQuery, you never have to think about servers, clusters, or nodes. Ever. Google handles all that behind the scenes using its colossal global infrastructure. When you run a query, BigQuery grabs the resources it needs and gets to work. This "it just works" feel comes from its history as an internal Google tool called Dremel, which was built to analyze absolutely massive datasets in seconds. Unsurprisingly, it hooks up perfectly with other Google Cloud services, especially things like Google Analytics, which makes it a natural fit for marketing and product analytics teams.

Redshift vs BigQuery: Architecture, scalability, and management

The architectural split between Redshift’s clusters and BigQuery’s serverless model has a massive impact on how you scale and what your daily routine looks like.

The cluster vs. serverless debate

Redshift (Provisioned Cluster): To get started with Redshift, you have to "provision a cluster." You pick a node type and decide how many you need. This approach gives you very predictable performance and a bill you can forecast pretty accurately. The catch is that it requires you to plan ahead and step in manually when you need to scale. If your query load suddenly triples, you have to actively resize the cluster or set up auto-scaling rules to handle it.

BigQuery (Serverless): BigQuery is the complete opposite. There are no clusters to manage. Period. When you run a query, BigQuery instantly calculates how much processing power is needed and assigns it on the fly. This makes it incredibly easy to use and scaling is a total non-issue. You can go from a tiny test query to a petabyte-sized analysis without touching a single configuration setting. The other side of that coin is that performance, and especially cost, can be less predictable if your team isn’t careful about writing efficient queries.

How Redshift and BigQuery handle scaling

Redshift: When you need more muscle, Redshift offers a couple of tools. You can do an "Elastic Resize" to permanently add more nodes, which is great for long-term growth. For sudden bursts of activity, you can use "Concurrency Scaling," which automatically adds temporary clusters to handle the extra load. These are great features, but they need to be configured and have some limits.

BigQuery: Scaling in BigQuery is just… automatic. It was built from day one to handle huge, unpredictable spikes in demand without you having to lift a finger. If a hundred people on your team decide to run heavy queries all at once, BigQuery doesn’t blink. It just works. This makes it extremely resilient and a great choice for workloads that are all over the place.

Day-to-day management effort

Redshift: This is where you really feel the philosophical difference. Redshift requires more ongoing attention. While AWS has automated a ton over the years, you still need to think about performance tuning. This often means defining things like distribution and sort keys to help Redshift organize data efficiently. You also need to occasionally run a "VACUUM" command to clean up space from old data and keep things zippy.

BigQuery: BigQuery is about as close to a zero-management service as you can get. Google handles all the backend optimization, maintenance, and vacuuming for you. This frees up your data team to stop worrying about infrastructure and focus on what they were hired to do: find insights in the data. You just load your data and start asking it questions.

BigQuery is basically 0 admin... You can literally just push data to it and then run queries without thinking about a thing. It's really fast too.

Reddit

Redshift vs BigQuery: Performance and ideal use cases

The "which is faster?" question isn’t the right one to ask. The better question is, which one is built for the kind of work your team does?

Redshift: Best for predictable BI workloads

Redshift really hits its stride with consistent, predictable query patterns. Think about all the dashboards and reports your BI tools are refreshing on the hour, every hour. Because you’ve already allocated the resources, performance is rock solid. That daily sales report for the finance team will run just as fast tomorrow as it did today.

• Ideal Use Case: A big e-commerce company has hundreds of analysts who rely on thousands of daily reports for everything from financial planning to inventory management. The queries are well-known, and consistent performance is non-negotiable.

BigQuery: Best for ad-hoc and exploratory analysis

BigQuery is the star when you’re dealing with unpredictable, "spiky" workloads. It’s built for those big, complex, exploratory queries that data scientists love to run when they’re hunting for a new insight. When a single query needs a massive amount of power for just a few minutes, BigQuery’s ability to summon Google’s resources on demand is a lifesaver.

• Ideal Use Case: A gaming company wants to comb through petabytes of player data to spot a new pattern in user behavior. It’s a gigantic, one-off query that would be a nightmare to plan for in a provisioned system, but it’s a perfect job for BigQuery.

Quick comparison table

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

Redshift vs BigQuery: A full pricing breakdown

Let’s talk about the most important (and often most confusing) part: the price tag. Redshift and BigQuery have totally different models, so understanding them is key to avoiding a bill that makes your finance team raise an eyebrow.

Redshift’s pricing model: Pay for provisioned clusters

Redshift’s pricing is pretty easy to wrap your head around. You pay a set hourly rate based on the nodes in your cluster. You’re basically paying to keep the lights on and the engine warm, whether you’re actively running queries or not.

• Compute Costs:

  • On-Demand Pricing: Pay by the hour with no commitment. For a common "ra3.4xlarge" node, for example, you’re looking at about $3.26 per hour.

  • Reserved Instances: If you know you’ll be using it consistently, you can commit to a 1 or 3-year term and get a steep discount, sometimes over 60%.

  • Redshift Serverless: A newer option that’s a bit more like BigQuery. It charges you in "RPU-hours," so you’re only paying for compute when queries are actively running.

• Storage Costs: With the modern RA3 nodes, storage is billed separately at around $0.024 per GB-month.

BigQuery’s pricing model: Pay for what you use

BigQuery breaks its pricing down into two simple parts: storing your data and running your queries.

• Storage Pricing:

  • Active Storage: You’ll pay about $0.02 per GB-month for any data that’s been touched in the last 90 days.

  • Long-Term Storage: Here’s a nice perk. If a table isn’t modified for 90 days straight, the storage price for it automatically gets cut in half, to about $0.01 per GB-month.

• Compute (Analysis) Pricing:

  • On-Demand: This is the default model. You’re charged for the amount of data your query scans. The going rate is $6.25 per terabyte (TB) processed, but Google gives everyone their first 1 TB free every single month.

  • Capacity (Editions): If you’re a heavy user, you can switch to a flat-rate model. You buy a set amount of processing power (called "slots") for a fixed monthly or annual fee. This gives you predictable spending and can be cheaper if you’re running a lot of queries.

The bottom line on cost: Redshift vs BigQuery

The best choice for your wallet really depends on your workload. Redshift gives you cost predictability and can be cheaper if you have a high, steady stream of queries. BigQuery is often much more cost-effective for teams with less frequent or bursty workloads, but you have to be mindful that inefficient queries can lead to big bills.

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

So, after all that, which one should you go with? It really comes down to your priorities, your team’s skills, and what technology you’re already using.

• Choose Redshift if: You are all-in on the AWS ecosystem, your analytics work is steady and predictable (like those daily BI dashboards), and you want total control over performance and cost. Your team is the type that enjoys tuning a database to squeeze out every last drop of speed.

• Choose BigQuery if: Your main goals are simplicity and effortless scaling. Your query patterns are unpredictable, and you want to free up your team from managing infrastructure so they can spend 100% of their time on analysis.

Choosing a data warehouse is a huge step in centralizing your structured data for BI. But what about all the unstructured knowledge floating around in support tickets, help docs, and internal wikis? While your data team uses Redshift or BigQuery to figure out what happened, your support team needs instant answers to why it happened and how to fix it.

Go beyond analytics: Automate support with unified knowledge

Just like a data warehouse brings all your business data into one place, eesel AI unifies your scattered support knowledge to create a single source of truth for your customer service team. It connects directly to all the places your knowledge lives to power an AI that can handle frontline support, help out agents, and answer internal questions in a snap.

The connection to our discussion is pretty clear:

• Unified Knowledge: eesel AI plugs into your help desks like Zendesk, wikis like Confluence, shared folders in Google Docs, and even your past ticket history to build a complete picture of your business.

• Effortless Setup: A lot like BigQuery’s serverless approach, eesel AI uses one-click integrations so you can be up and running in minutes, not months. No massive engineering project needed.

• Total Control: And just like Redshift gives you fine-grained control, eesel AI has a fully customizable workflow engine. You decide exactly which tickets to automate, what personality your AI should have, and what it’s allowed to do.

eesel AI connects with all your existing knowledge sources in just a few clicks.

Ready to put your support knowledge to work? Try eesel AI for free and see how you can start automating your frontline support today.