The 6 best Snowflake alternatives to consider in 2025

Snowflake really shook up the data warehousing world with its cloud-native setup. It’s powerful, slick, and honestly, it’s become the go-to for a lot of companies. But let's be real, it isn't the perfect solution for every team or every budget.

After a few friends complained about their costs getting out of hand and feeling a bit stuck, I figured it was time to properly dig into the world of Snowflake alternatives. I wanted to see what else was out there and if any of them could offer a better deal for certain situations, whether that’s a different set of features, a more predictable bill, or just less of a headache to manage.

So, here’s my breakdown of the top six Snowflake alternatives for 2025. I’ve done my best to cut through the marketing fluff and give you a straight-up comparison to help you figure out what might work for you.

What is Snowflake and why even look for Snowflake alternatives?

First, a quick refresher on what we're talking about. Snowflake is a cloud data platform known for its smart architecture that separates storage from compute. This is its headline feature, letting you scale up your query power for a big job without having to change anything about how your data is stored. It’s fairly easy to use (especially compared to the old on-premise dinosaurs), works on all the major clouds, and has a solid data-sharing marketplace.

So, if it’s so great, why is anyone looking for a change? From what I've seen, it usually comes down to one of these three pain points:

• Surprise bills: Snowflake's pay-for-what-you-use pricing sounds great in theory. But a busy month, a few inefficient queries, or an unexpected analytics project can leave you with a bill that’s way higher than you planned for. It makes budgeting feel like a bit of a guessing game.

• Feeling locked in: Sure, Snowflake runs on AWS, GCP, and Azure, but the platform itself is its own thing. Once you've built all your data pipelines and workflows in the Snowflake ecosystem, the thought of moving to another platform is enough to give anyone a migraine.

• It’s just… a lot: If all you need to do is run some fast queries on a data lake or handle straightforward analytics, Snowflake’s full set of features can be total overkill. You might find yourself paying for a high-end multi-tool when all you really needed was a good screwdriver.

How I picked the best Snowflake alternatives

The data platform market is crowded and loud. To cut through the noise, I decided to focus on the things that actually matter to the people who use these tools day in and day out. Here’s the checklist I used:

• Architecture: How is the thing actually built? Is it a traditional data warehouse, a modern "lakehouse," or totally serverless? The architecture tells you a lot about how you'll manage it (or not) and what it's really good for.

• Performance & Scalability: How well does it handle huge datasets and a bunch of people querying at once? And more importantly, how much of a pain is it to scale up or down when things change?

• Cost Model: Is it pay-per-query, provisioned servers, or some kind of hybrid? I was looking for clarity and predictability, not just the cheapest option on paper.

• Ecosystem Integration: Does it play nice with AWS, GCP, Azure, and the rest of your data tools? A platform that doesn't fit into your current workflow is a non-starter.

• What’s it really for?: Every tool has its sweet spot. Is it built for general business intelligence, real-time dashboards, or heavy-duty machine learning?

A quick comparison of the top Snowflake alternatives

Before we get into the details, here’s a cheat sheet. It’s a simple way to see where each platform stands out.

Platform	Best For	Architecture	Cost Model	Key Advantage
Databricks	AI/ML & big data engineering	Lakehouse	Consumption (DBUs)	Unifies data lakes & warehouses
Google BigQuery	Serverless analytics & GCP users	Serverless	Pay-per-query/Flat-rate	Zero infrastructure management
Amazon Redshift	AWS-native ecosystems	Cluster-based Warehouse	Provisioned Instance	Deep AWS integration
Azure Synapse	Microsoft-centric organizations	Unified Analytics Platform	Provisioned/Pay-per-use	Combines warehouse & big data
ClickHouse	Real-time analytics dashboards	Columnar Database	Open-source/Managed	Insanely fast query speed
PostgreSQL	Full control & cost-effectiveness	Relational Database	Open-source (Free)	No vendor lock-in & flexibility

The 6 best Snowflake alternatives for your data strategy in 2025

Okay, let's get into the good stuff. Here’s a more detailed look at each of the top six contenders.

1. Databricks

Databricks is probably Snowflake's most direct competitor, and for good reason. They came up with the "lakehouse" idea, which tries to give you the best of both worlds: the cheap, flexible storage of a data lake combined with the speed and reliability of a data warehouse. This makes it a beast for companies that want to do data engineering, SQL analytics, and machine learning all in one spot.

• Best for: Teams with big AI and machine learning goals, and data engineers who need one platform to handle both neat, structured data and messy, unstructured stuff.

• Pros: It’s fantastic for machine learning projects, can handle pretty much any data you can imagine, and is built on open standards like Apache Spark and Delta Lake, which means you’re less locked in.

• Cons: It’s definitely more complex than Snowflake. If all you’re doing is running BI reports, this platform might feel a bit like cracking a nut with a sledgehammer.

• Pricing: Databricks has a consumption-based model, priced in Databricks Units (DBUs) per hour. The cost of a DBU changes based on the computing power you need and your cloud provider, but for data warehousing, it starts around $0.22/DBU.

2. Google BigQuery

Google BigQuery is what you get when you wish all the annoying parts of managing a data warehouse would just disappear. It’s completely managed and serverless, so you never have to think about provisioning servers or managing clusters. You just load your data and start asking it questions. It scales up automatically behind the scenes, making it super easy to get started with huge amounts of data.

• Best for: Anyone already using Google Cloud Platform (GCP) or teams that want to spend 100% of their time on analysis and zero time on infrastructure.

• Pros: The serverless model is a dream for simplicity. Its pay-per-query pricing can be incredibly cheap if you don't run queries all the time, and the built-in machine learning features are a nice touch.

• Cons: That pay-per-query model can come back to bite you. If you have lots of users running lots of queries, the costs can become unpredictable and climb quickly. You also don't get as much fine-grained control over performance.

• Pricing: BigQuery has two main pricing options. The on-demand model charges you for the amount of data your query scans, starting around $6.25 per terabyte (and your first TB each month is free). For more predictable bills, you can buy dedicated compute "slots," which starts at about $0.04 per slot-hour.

3. Amazon Redshift

As one of the original players in the cloud data warehouse game, Amazon Redshift is a mature, powerful, and very robust choice. It’s a managed, cluster-based system that’s stitched directly into the AWS fabric. If your company runs on AWS, Redshift often feels like the most natural fit.

• Best for: Businesses that are all-in on AWS and need a predictable, high-performance warehouse for their BI and analytics tools.

• Pros: The connection to other AWS services like S3 and Glue is completely seamless. The pricing is much more predictable, especially with reserved instances, and its performance with structured data is rock-solid.

• Cons: It’s not as flexible as Snowflake, since compute and storage are more closely linked. It also takes a bit more hands-on effort to tune and manage to get the best possible performance.

• Pricing: Redshift's pricing is based on the cluster of servers you set up. On-demand pricing starts at $0.25 per hour for a basic node. If you know you'll be using it for a while, you can get big discounts (up to 75%) by committing to 1- or 3-year Reserved Instances. There's also a Serverless option that starts at $0.36 per RPU-hour.

4. Azure Synapse Analytics

Azure Synapse Analytics is Microsoft's ambitious play to create one service to rule all your data. It bundles together a data warehouse, big data processing with Spark, and data integration tools into a single workspace.

• Best for: Companies that are heavily invested in the Microsoft Azure world and use tools like Power BI and Azure Machine Learning.

• Pros: The way it works with other Azure services is fantastic. It also gives you the flexibility to choose between pre-configured resources for predictable workloads or a serverless model for random, one-off queries.

• Cons: It packs a lot in, which can make it complicated to set up and manage. Its data sharing features also don't feel quite as polished as Snowflake's.

• Pricing: The pricing model is flexible, but it can be a bit confusing to wrap your head around. For the serverless option, you pay per terabyte of data processed (around $5/TB). For dedicated resources, you pay for "Data Warehouse Units" (DWUs), which start at about $1.51 per hour.

5. ClickHouse

ClickHouse is a completely different kind of animal. It's an open-source, columnar database built for one thing and one thing only: incredible speed. It is a monster at real-time analytical queries (OLAP) and can chew through billions of rows of data in the blink of an eye. While it won't replace Snowflake for every single use case, it's pretty much unbeatable for powering fast, interactive dashboards.

• Best for: Real-time analytics, sifting through log data, and building user-facing dashboards where you need answers instantly.

• Pros: It is ridiculously fast for analytics. Because it's open-source, it's very budget-friendly, and it's designed to be scaled out across a bunch of servers.

• Cons: It’s not a managed, all-in-one service out of the box, so it’s missing some of the user-friendly security and governance features of Snowflake. Think of it as a specialized tool, not a general one.

• Pricing: The open-source software is free; you just pay for the servers to run it on. If you want a managed version, ClickHouse Cloud has plans where storage is about $25/TB per month and compute starts at around $0.30 per unit per hour.

6. PostgreSQL

You might be thinking, "Wait, isn't PostgreSQL a regular database for apps?" And you're right. But for small to medium-sized analytics needs, a well-configured Postgres instance can be a surprisingly good, and extremely cheap, data warehouse. It gives you total control and means you never have to worry about vendor lock-in.

• Best for: Startups, small teams, or anyone who cares more about control and cost-savings than anything else and has the technical chops to manage their own database.

• Pros: It’s completely free and open-source. There’s a huge ecosystem of tools and extensions for it, and you're never tied to a single company's platform.

• Cons: This is the ultimate DIY solution. You need serious in-house expertise to manage, tune, and scale it for heavy analytics. It also doesn't separate compute and storage in the slick way that modern cloud warehouses do.

• Pricing: The software is free. You just pay for the cloud servers and storage you decide to run it on.

How to choose the right Snowflake alternatives for your team

So, how do you actually make a decision? The "best" platform is really just the one that fits your specific needs. Here's a quick way to think about it:

• Are you deeply tied to one cloud provider? Then sticking with their native tool (Redshift for AWS, BigQuery for GCP, Synapse for Azure) is usually the easiest path. You'll get smoother integrations and likely save some money on data transfer fees.

• Is your main focus AI and machine learning? Databricks is the clear front-runner. It was built for these kinds of jobs and provides a much more cohesive experience for data scientists.

• Do you need lightning-fast, real-time dashboards? A specialized engine like ClickHouse will give you that sub-second response time that general-purpose warehouses can sometimes struggle with.

• Are budget and control your absolute top priorities? Then an open-source option like PostgreSQL is unbeatable. It gives you complete freedom, assuming you have the team to manage it.

This video provides a comparison of Snowflake, BigQuery, and ClickHouse to help you build cost-efficient analytics.

Your data is organized, now put it to work with AI

Choosing a Snowflake alternative isn't just about picking a new place to store your data. It's about setting up a solid foundation so you can actually use that data to make smarter decisions. Whether you end up with Databricks for its AI power, BigQuery for its simplicity, or another platform, the goal is always to create a single, reliable source of truth.

But once your data is all in one place, clean, and accessible… what’s the next step? The real value comes when you get that information flowing through your business.

That’s where a tool like eesel AI can make a huge difference. In the same way a data warehouse brings together all your company's structured data, eesel AI unifies your company's knowledge. It connects to all the places your team's expertise is scattered, your help docs, support tickets, Confluence, and Google Docs, and turns that mess into a single, powerful knowledge base.

This is the perfect fuel for intelligent AI agents for customer support. With your data organized, the next logical move is to use a tool like eesel AI to automate your frontline support, help your agents draft perfect replies, and give your customers instant, accurate answers.

Ready to put your company's knowledge to work? Start a free trial with eesel AI and see how you can get your support automated in just a few minutes.