I tried the top BigQuery alternatives to escape surprise bills: Here’s my 2025 guide

Google BigQuery is an amazing piece of technology for massive data analysis. Don’t get me wrong, it’s powerful. But if you’ve ever opened your monthly cloud bill and felt your stomach drop, you know exactly the feeling I’m talking about. That sticker shock from an unexpected invoice is a pain many of us share. For a lot of teams, especially those working with "medium data" (think gigabytes to a few terabytes), the cost and complexity can feel like using a sledgehammer to crack a nut.

That’s what sent me down the rabbit hole of looking for better options. Here’s what I learned: the best "alternative" isn’t always a direct competitor. Sometimes, it’s about finding a totally different way to solve your problem, one that doesn’t involve the heavy lifting of a traditional data warehouse. This guide covers my top 5 BigQuery alternatives for 2025, from the big-name competitors to a smarter way to use your company knowledge for everyday tasks like customer support.

What is a cloud data warehouse?

Think of a cloud data warehouse as a central, super-organized vault in the cloud. It’s where you store and analyze huge amounts of data from all your different business tools. Its main job is to power business intelligence (BI) dashboards, run reports, and crunch numbers for large-scale analytics.

What makes platforms like BigQuery so potent are a couple of key tricks up their sleeve: columnar storage, which makes reading data for analysis incredibly fast, and massively parallel processing (MPP), which breaks up big jobs into smaller pieces to get them done way quicker.

Why are people looking for BigQuery alternatives?

It’s not just about the money, though that’s definitely a huge part of it. There are a few common reasons why teams start looking for a way out.

• Unpredictable costs: BigQuery’s on-demand pricing is based on how much data your queries scan. Sounds simple, right? The problem is, it can lead to bills that are all over the place. One messy query from an analyst or a dashboard that refreshes too often can accidentally scan terabytes of data, running up costs before you even notice.

• The management headache: Google markets it as "serverless," but getting the most out of BigQuery (and keeping costs down) takes a surprising amount of data engineering work. You have to constantly think about partitioning and clustering your tables. This becomes a hidden tax on your team’s time, especially if you’re trying to run a lean operation.

• Feeling locked in: BigQuery is tied tightly to the Google Cloud Platform (GCP). If you want the freedom to use other cloud providers like AWS or Azure, moving all your data and workflows out of BigQuery is a massive project.

• Using the wrong tool for the job: Let’s be honest, sometimes you don’t need petabyte-scale analytics. You just need a fast, reliable answer for an employee or a customer. Using a full-blown data warehouse for this is total overkill. It’s like building a six-lane highway just to drive to your neighbor’s house.

How I picked the best BigQuery alternatives

To put this list together, I focused on a few things that really matter when you’re thinking about making a switch.

• Pricing model: Is it clear and predictable? Does it give you good value, whether you’re running a few huge jobs or tons of small, interactive queries?

• Performance & scalability: How fast does it actually run? Can it grow with your business without needing a team of engineers to babysit it 24/7?

• Ease of use & management: How much technical skill do you need to get it set up? Can you actually try it on your own, or are you forced to sit through a dozen sales calls first?

• What it’s built for: Is it a general-purpose tool for any kind of analysis, or is it designed for something specific, like real-time dashboards or solving operational headaches like support automation?

A quick comparison of the top BigQuery alternatives

Tool	Best For	Pricing Model	Key Differentiator
eesel AI	Activating company knowledge for support & internal Q&A	Per-interaction, predictable plans	Goes live in minutes; automates actions, not just analysis
Snowflake	Multi-cloud flexibility & enterprise scale	Usage-based (compute credits)	Decoupled storage and compute
Amazon Redshift	Deep AWS integration & performance tuning	Per-hour for provisioned clusters or serverless	Massive Parallel Processing (MPP) architecture
Azure Synapse Analytics	Unified analytics within the Microsoft ecosystem	Per-hour (DWUs) or per-TB processed	Combines data warehousing and big data analytics
ClickHouse	Real-time analytics & open-source flexibility	Open-source (free) or cloud (usage-based)	Blazing-fast performance for OLAP queries

The top 5 BigQuery alternatives and competitors in 2025

Here’s a closer look at the platforms that made the cut.

1. eesel AI

Instead of being just another data warehouse, eesel AI offers a completely different way to get value from your company’s data. Rather than piping all your knowledge into a warehouse for analysts to dig through later, eesel AI connects directly to the tools you already use, like your helpdesk, Confluence, and Google Docs, and uses AI to give instant answers and automate tasks. It solves real business problems like frontline support and internal Q&A without the data engineering marathon.

This infographic shows how eesel AI connects to various company tools to create a centralized knowledge base for providing instant answers and automating tasks, offering a different approach compared to traditional BigQuery alternatives.::

You can genuinely get set up in minutes, not months. The platform learns from your past support tickets and internal docs, so it provides accurate, contextual answers right from the start.

Pros:

• Insanely simple setup: You can go live in a few minutes with one-click integrations. No need to talk to a salesperson just to see if it works for you.

• Unifies your existing knowledge: It plugs directly into where your knowledge already lives, so you don’t have to move a thing.

• Risk-free testing: It has a great simulation mode that lets you test the AI on your historical support tickets. This shows you the exact ROI before you ever turn it on for real customers.

A screenshot of eesel AI's simulation mode, a key feature that makes it one of the most practical BigQuery alternatives for support automation by showing projected ROI.::

Cons:

• It’s not a data warehouse. It’s built for operational tasks (answering support tickets, handling internal questions), not for large-scale BI or data science projects.

• It focuses on unstructured and semi-structured knowledge like text, not the complex relational datasets you’d use for deep analysis.

Pricing:

eesel AI has clear, predictable plans based on "AI interactions" (an AI reply or action). This way, you’re not penalized with per-resolution fees for having a busy month.

The pricing plans for eesel AI are displayed, highlighting the predictable, interaction-based model that contrasts with the consumption-based pricing of many BigQuery alternatives.::

• Team Plan: $299/month ($239/mo if billed annually) for up to 1,000 interactions and 3 bots.

• Business Plan: $799/month ($639/mo if billed annually) for up to 3,000 interactions, unlimited bots, and unlocks training on past tickets and AI actions.

• Custom Plan: For unlimited interactions and more advanced needs.

Final take: If your main goal is to cut down on support tickets, answer employee questions faster, or automate repetitive tasks, eesel AI is a much faster, simpler, and more direct path than building out a complex analytics stack on BigQuery.

2. Snowflake

Snowflake is probably the first name that comes to mind when you think of BigQuery alternatives, and for good reason. It’s a cloud-native data platform known for its unique architecture that separates storage from compute. In simple terms, this means you can scale your processing power up or down without touching your storage, and vice versa. It’s like being able to upgrade your car’s engine without having to buy a whole new car.

Its multi-cloud approach is a huge plus, as it runs on AWS, Azure, and GCP, freeing you from being stuck with one vendor. The auto-scaling and per-second pricing for compute give you much better control over your costs, which can feel a lot more predictable than BigQuery’s "pay for what you scan" model.

Pros:

• Scaling storage and compute independently is a huge help for managing costs.

• Excellent multi-cloud support and easy data sharing across different clouds.

• A clean, user-friendly SQL interface that data teams really like.

Cons:

• The pay-as-you-go pricing can still get expensive if you’re not paying attention. You have to actively monitor your usage to avoid surprises.

• It doesn’t have built-in tools for data ingestion (ETL), so you’ll need other tools to get your data into the warehouse in the first place.

Pricing:

Snowflake’s pricing is based on credits, which are used for compute time. Storage is billed separately.

• Compute: Billed per second. On AWS in US East, the Standard plan is $2/credit, and Enterprise is $3/credit.

• Storage: On-demand storage is around $23 per TB per month, with discounts if you pre-purchase capacity.

• A 30-day free trial is available to get started.

Final take: Snowflake is a fantastic choice for companies that want a high-performance, flexible data warehouse and are committed to a multi-cloud strategy.

3. Amazon Redshift

As one of the original cloud data warehouses, Amazon Redshift is a mature and powerful platform that can handle petabytes of data. It’s built right into the AWS ecosystem and uses a massively parallel processing (MPP) architecture to deliver fast queries on huge datasets.

For companies already deep in the AWS world, Redshift is a natural choice. It connects seamlessly with services like S3, Glue, and SageMaker. The pricing can be very attractive, especially if you have predictable workloads and can use reserved instances. It also gives you more manual control over your cluster than BigQuery, which is a plus for teams that love to tinker and fine-tune performance.

Pros:

• Deep, seamless integration with the entire AWS ecosystem.

• Strong, reliable performance for complex analytical queries.

• Can be very cost-effective if your workload is predictable and you use reserved instances.

Cons:

• It requires more hands-on management (like sizing clusters and doing maintenance) compared to other options.

• Scaling isn’t always smooth; resizing a cluster can sometimes require downtime.

Pricing:

Redshift pricing can be a bit complex, with two main models:

• Provisioned Clusters: You pay per hour for the nodes in your cluster. For example, an "ra3.xlplus" node costs about $1.086/hour on-demand. You can get big discounts (up to 75%) by committing to 1- or 3-year reserved instances.

• Redshift Serverless: This scales automatically, and you pay per second for the capacity you use when the warehouse is active, measured in Redshift Processing Units (RPUs). This costs about $0.36 per RPU-hour.

• A free trial is available for new users.

Final take: Redshift is the best bet for teams committed to the AWS ecosystem who need a powerful, budget-friendly data warehouse and are comfortable with a bit of hands-on management.

4. Azure Synapse Analytics

Azure Synapse Analytics is Microsoft’s ambitious shot at creating an all-in-one analytics platform. It brings together enterprise data warehousing, big data processing (with Apache Spark), and data integration into a single service.

Its biggest draw is the unified experience it offers for teams already in the Azure world. It lets you query data from different places using either serverless on-demand resources or provisioned clusters. Plus, it has deep integrations with other Microsoft tools like Power BI and Azure Machine Learning, which is a huge bonus for organizations that run on Microsoft.

Pros:

• A truly unified platform for data warehousing, big data, and ETL.

• Seamless, native integration with other Azure services.

• Flexible compute options let you choose between provisioned resources or a serverless model.

Cons:

• With so many features packed in, it can be complex and has a steep learning curve.

• It’s really only a great fit if you’re already heavily invested in the Microsoft and Azure ecosystem.

Pricing:

Azure Synapse pricing is pay-as-you-go and has a few different parts:

• Serverless SQL Pool: You pay per terabyte of data processed, starting at around $5 per TB.

• Dedicated SQL Pool: You pay for provisioned Data Warehouse Units (DWUs). "DW100c" starts at about $1.21/hour, with reserved capacity offering big savings.

• Data Integration: You’re charged for pipeline activities and data flow execution.

• A 12-month free trial with limited services is available.

Final take: For businesses running on Azure, Synapse is a compelling option that brings the entire analytics lifecycle under one roof.

5. ClickHouse

ClickHouse is an open-source, columnar database built for one thing: speed. It’s designed specifically for real-time analytics and online analytical processing (OLAP). Its claim to fame is its mind-boggling performance, it can chew through petabytes of data and spit out analytical reports in milliseconds.

If your main goal is to power real-time dashboards or interactive analytics, ClickHouse can often blow other systems out of the water. Since it’s open-source, it gives you incredible flexibility and can be much more cost-effective because you’re only paying for the servers you run it on.

This video offers pro tricks for building cost-efficient analytics, comparing some of the top BigQuery alternatives like Snowflake and ClickHouse.

Pros:

• Blazing-fast query performance for analytical workloads.

• It’s open-source and can be extremely cost-efficient.

• It’s designed to scale out horizontally and is built to be fault-tolerant.

Cons:

• This is not a plug-and-play solution. It requires serious in-house technical skill to set up, manage, and scale.

• It’s not great for transactional workloads (like processing individual orders) and is more of a specialized tool than a full data warehouse.

Pricing:

• Open Source: Free to download and run on your own servers.

• ClickHouse Cloud: A managed service with usage-based pricing. The Scale plan, for example, charges around $25.30 per TB/month for storage and $0.2985 per compute unit/hour on AWS in US East.

Final take: ClickHouse is a phenomenal choice for engineering-heavy teams that need the absolute best performance for real-time analytics and aren’t afraid to get their hands dirty managing the infrastructure.

How to choose the right BigQuery alternatives

Feeling a bit stuck? Here’s a quick checklist to help you decide.

• Be honest about your data scale: Are you really dealing with petabytes, or is it more like terabytes? For "medium data," a super complex system might be more trouble than it’s worth.

• Figure out your main workload: What are you actually trying to do most of the time? Is it running large, scheduled reports? Powering live BI dashboards? Or getting instant, automated answers to questions? Pick a tool that’s built for your main job.

• Look at your team’s skills: Do you have a dedicated data engineering team ready to tune clusters and optimize costs? Or do you need something that just works out of the box?

• Think beyond the warehouse: Could your business problem be solved more directly? For things like customer support, a tool like eesel AI that uses your existing knowledge might deliver value a lot faster than a six-month data warehousing project.

Finding the right BigQuery alternative for the job

There’s no single "best" BigQuery alternative. The right choice for you depends entirely on your data, your budget, your team’s skills, and most importantly, the actual problem you’re trying to solve.

While platforms like Snowflake and Redshift are powerful competitors for traditional data warehousing, don’t forget to think differently. For challenges like cutting your support ticket volume or giving your team instant access to company knowledge, a direct, AI-powered solution can be far more effective. Instead of just analyzing data to find answers, you can give your team a tool that delivers those answers automatically.

Ready to see how an AI-powered knowledge platform can solve your support challenges without the data warehouse complexity? Start your free eesel AI trial today and go live in minutes.