I tried 6 top Snowflake alternatives to find the best data platform in 2025

Snowflake is a huge name in cloud data warehousing, and for good reason. It’s powerful, it scales, and it completely changed how we think about separating data storage from the power needed to analyze it. But let’s be real, that power comes with a price tag that can get out of hand, fast. I’ve seen teams get hit with shocking bills and spend more time trying to cut costs than actually using their data.

When you add in the platform’s growing complexity and the nagging fear of being locked into a single vendor, it makes sense why so many of us are starting to look around.

My team and I decided to do a deep dive into the world of Snowflake alternatives. We weren’t just looking for a cheaper version of the same thing. We wanted to find the right tool for the right job, because not every problem needs a massive data warehouse to solve it. This guide is what we found after comparing six top contenders on everything from performance to price.

And here’s a little spoiler: sometimes the best alternative isn’t another data warehouse at all. It’s often a more direct way to solve the business problem you were trying to tackle in the first place.

What is a cloud data warehouse and why look for Snowflake alternatives?

Before we get into the comparisons, let’s make sure we’re on the same page. A cloud data warehouse is a central place where a company keeps all its structured and semi-structured data for analysis. Think of it as a massive, super-organized digital library for all your business information.

The big idea that Snowflake helped make popular was separating storage (where the data lives) from compute (the engine that runs the analysis). This means you can scale them independently. If you need to run a huge, complex query, you can fire up a powerful compute engine for an hour and then shut it down, all without touching your storage. This is incredibly useful for business intelligence (BI), reporting, and is often the foundation for bigger AI projects.

How we picked the best Snowflake alternatives

To keep our comparison fair and focused, we measured every platform against the same five criteria. This helped us cut through the marketing hype and see how they stacked up on what actually matters.

• Architecture & Performance: How is it built, and more importantly, how fast can it run the kind of queries we care about?

• Cost Model: Is the pricing easy to understand and predict? Or are we just setting ourselves up for another surprise bill at the end of the month?

• Ease of Use: How quickly can a team get up and running? We wanted to know if we’d see value in minutes, weeks, or months.

• Ecosystem & Integrations: How well does it connect with the other tools in our tech stack, like our helpdesk, BI tools, and internal wikis?

• Primary Use Case: What is this platform really built for? Is it for general analytics, real-time dashboards, or something more specific like machine learning?

A quick comparison of the top Snowflake alternatives

Here’s a simple table to give you a bird’s-eye view of our findings.

Alternative	Best For	Pricing Model	Key Advantage
eesel AI	Direct business outcomes (e.g., support automation)	Per-interaction SaaS	Bypasses data warehouse complexity for AI
Databricks	AI, ML, and data science teams	Consumption-based (DBUs)	Unified lakehouse architecture
Google BigQuery	Serverless simplicity in GCP	Pay-per-query/slot-based	Zero infrastructure management
Amazon Redshift	Deep AWS integration	Instance-based (On-demand/Reserved)	Predictable costs in the AWS ecosystem
Azure Synapse	Microsoft-centric organizations	Consumption-based (DTUs/vCore)	Unified analytics within Azure
ClickHouse	Real-time analytics dashboards	Open-source / Managed service	Sub-second OLAP queries

The 6 best Snowflake alternatives for 2025

After spending weeks testing, configuring, and running queries, here’s our detailed breakdown of the platforms that can truly compete with Snowflake.

1. eesel AI

Why it’s on this list: This one is a bit of an outlier, and it’s my top pick for a reason. A lot of companies I talk to invest in a data warehouse like Snowflake with one big goal: to analyze customer support data and build an AI chatbot. That’s a project that can take months, cost a fortune, and requires a whole team of data engineers. eesel AI flips that entire process on its head by solving the business problem directly, giving you a powerful AI support agent in minutes.

Description: To be clear, eesel AI isn’t a data warehouse. It’s an AI platform that connects directly to the tools you already use. It unifies knowledge from your helpdesk (like Zendesk or Freshdesk), learns from your past support tickets, and pulls from your internal documents in Confluence or Google Docs to automate your frontline support. It gets you the business result you wanted from that big data project, but without the data engineering headache.

Pros:

• Go live in minutes: The setup is ridiculously fast and you can do it yourself. We connected our Zendesk account and help center, and had a working AI agent in under 10 minutes. No sales calls, no mandatory demos, and no data pipelines to build.

• Immediate results: Instead of waiting months for a data project to maybe show a return, eesel AI starts deflecting tickets and helping agents from day one.

• Unifies knowledge automatically: It learns from your actual support history, so its answers are relevant to your customers and match your brand’s voice.

• Risk-free simulation: This was a real standout feature for us. You can test the AI on thousands of your past tickets to see exactly how it would have performed. This gives you a solid forecast on its resolution rates before you ever turn it on for live customers.

The eesel AI simulation dashboard shows how different Snowflake alternatives can predict automation performance before implementation.

Cons:

• It’s not a general-purpose data warehouse. You can’t use it to analyze your sales or finance data.

• Its focus is very specific to customer service, IT service management, and internal knowledge use cases.

Pricing: The pricing is transparent and predictable, which is a breath of fresh air. Plans start at $299/month, and they don’t charge you per ticket resolved. You know exactly what your bill will be, which makes budgeting so much easier than with Snowflake’s fluctuating credit consumption.

2. Databricks

Why it’s on this list: Databricks is Snowflake’s biggest direct competitor. If your company is all-in on data science, machine learning, and advanced analytics that go way beyond standard SQL, this is probably the platform for you.

Description: Databricks is built around the "Lakehouse" architecture, which blends the raw, flexible storage of a data lake with the performance and structure of a data warehouse. This makes it great for handling the massive, often messy datasets that data science teams work with.

Pros: It’s designed specifically for complex AI and ML workloads, can handle just about any data type you throw at it (structured, semi-structured, unstructured), and is built on open-source standards like Delta Lake, which helps you avoid getting stuck with one vendor.

Cons: Honestly, it has a much steeper learning curve than Snowflake. For a team that just needs to run BI reports, Databricks is probably overkill and can be too complicated.

Pricing: Its pricing is based on Databricks Units (DBUs), a consumption model that, much like Snowflake’s credits, can be difficult to forecast accurately.

3. Google BigQuery

Why it’s on this list: For teams that want to completely forget about managing infrastructure, BigQuery’s fully serverless model is a dream. It’s the closest you can get to a "just run queries" experience.

Description: With BigQuery, there are no clusters to set up or virtual warehouses to configure. You just upload your data and start writing SQL. It’s a core part of the Google Cloud Platform and feels like a natural choice if you’re already in that ecosystem.

Pros: The biggest benefit is having zero infrastructure to manage. It scales automatically with every single query, which is pretty amazing. It also has great integration with other Google tools, including the surprisingly capable BigQuery ML for running machine learning models with just SQL.

Cons: The pay-per-query model can be tricky. An unoptimized query can accidentally scan terabytes of data and lead to a massive, unexpected bill. It also keeps you pretty tightly within the Google Cloud ecosystem.

Pricing: BigQuery offers two main models: on-demand (you pay per terabyte of data your query scans) and capacity-based (you reserve a certain amount of processing power for a flat rate).

4. Amazon Redshift

Why it’s on this list: If your company is already heavily invested in AWS, Redshift is often the simplest path forward. It offers deep integration and potentially more predictable costs than Snowflake.

Description: Redshift has been around for a while in the cloud data warehousing world. It’s a mature, powerful platform built on a more traditional cluster-based architecture. You set up a group of nodes, and that’s your warehouse.

Pros: The integration with the AWS ecosystem is seamless. Pulling data from S3 or using other AWS services for data prep is incredibly straightforward. With reserved instance pricing, you can lock in costs for one or three years, making your budget far more predictable than a pay-as-you-go model.

Cons: It’s less flexible than Snowflake. Resizing a cluster isn’t instant, and it requires more hands-on tuning and maintenance to keep it running well. It’s also an AWS-only solution, so it’s not a great fit for multi-cloud setups.

Pricing: It’s primarily instance-based. You pay for the cluster you have running, with big discounts available if you commit to reserved instances upfront.

5. Azure Synapse Analytics

Why it’s on this list: For any organization that runs on Microsoft Azure and uses tools like Power BI for reporting, Azure Synapse is the natural fit.

Description: Synapse is Microsoft’s attempt to create a single, unified analytics service. It bundles traditional data warehousing, big data processing with Apache Spark, and data integration pipelines all into one interface called Azure Synapse Studio.

Pros: The native integration with the entire Azure stack is its biggest selling point. It helps cut down on the number of different tools you need by combining multiple analytics functions in one place. It also offers both serverless and dedicated resource models, giving you some flexibility.

Cons: It can be very complex. Finding your way around the all-in-one studio feels overwhelming at first, and it definitely has a steep learning curve. Its data sharing features also feel a few years behind Snowflake’s.

Pricing: It has a dual pricing model. You can pay for dedicated SQL pools based on "Data Warehouse Units" or for serverless pools based on the terabytes of data processed by your queries.

6. ClickHouse

Why it’s on this list: Sometimes, you just need pure speed. For real-time dashboards and interactive analytics, ClickHouse is an open-source powerhouse that can deliver sub-second query performance that Snowflake often can’t match for this specific use case.

Description: ClickHouse isn’t a general-purpose data warehouse. It’s a specialized columnar database built to do one thing exceptionally well: run analytical queries extremely fast.

Pros: The query performance is seriously impressive. It’s also very cost-effective since the software itself is free, and it’s designed to scale out easily.

Cons: This is not a tool for beginners. It requires a lot of in-house technical skill to set up, manage, and scale properly. It’s also missing many of the features you’d expect from a full data warehouse, like strong support for transactions.

Pricing: The software is open-source and free, but your total cost will be the infrastructure it runs on plus the salaries of the engineers needed to maintain it. There are also managed cloud versions available from various vendors if you don’t want to run it yourself.

This video provides some pro tricks for building cost-efficient analytics, comparing Snowflake, BigQuery, and ClickHouse.

How to choose the right Snowflake alternatives for your business

Picking the right platform can feel like a huge decision, but it gets a lot easier if you ask the right questions first. Here’s a quick checklist to guide your thinking:

• Start with the business problem. Don’t get distracted by the technology itself. Are you trying to build better reports, power a new AI feature, or monitor operations in real time? Your goal should pick the tool, not the other way around.

• Look at your cloud strategy. If your whole company runs on AWS, GCP, or Azure, their native data warehouse is often the simplest and most cost-effective choice. Going multi-cloud sounds great in theory but adds a lot of complexity in practice.

• Be realistic about your team’s skills. Do you have a team of expert engineers who can manage a complex open-source system like ClickHouse? Or do you need a fully managed, self-serve platform that just works out of the box?

• Challenge the need for a warehouse. This is the most important step. Before you sign a six-figure contract for a massive data project, ask yourself: is there a more direct path to the result I need? For challenges like improving customer support, a solution like eesel AI can deliver better results faster, cheaper, and with far less risk.

Snowflake alternatives: Think beyond the warehouse

While Snowflake is an excellent piece of technology, it’s not your only option. The modern data landscape is full of powerful Snowflake alternatives designed for specific needs, whether that’s Databricks for AI, BigQuery for serverless simplicity, or ClickHouse for raw speed.

But the biggest lesson from our research is this: the best choice isn’t always another data warehouse. For high-impact goals like transforming your customer support with AI, a more direct solution can give you a faster and more predictable return. Instead of spending months building data pipelines, you can get straight to solving the problem.