Databricks vs Snowflake Cost: Which Platform Saves You More Money in 2025?

An average company will waste 32% of their cloud spend budget because of poor visibility into their platform. So it makes sense when you’re evaluating if you want to use Snowflake or Databricks that you’re carefully considering the price. 

Both platforms are built for speed, scale, efficiency, data protection, and cloud-native applications. But each has a unique approach to architecture, use cases – and, most importantly, cost – which can make finding the best choice tricky. 

Don’t worry – we’ve done the heavy lifting for you here. 

In this article, we’ll break down: 

• How Snowflake costs work
• How Databricks costs work
• Total cost of ownership for both platforms
• Which platform is better for your situation 
• Cost optimization strategies for both Databricks and Snowflake

Snowflake vs Databricks: Quick Cost Comparison Table

This table shows a quick breakdown of Databricks vs Snowflake costs, though you’ll see some difference with real-world costs depending on your personal usage patterns and optimization efforts. 

Now that we’ve covered the basics, let’s dive into how each platform structures their costs.

Snowflake Cost Breakdown

Snowflake costs are technically pay-as-you-go. Or, more accurately, pay-as-you-use. You pay for resources as you consume them using credits. . This is very different from Databricks’ payment system, and can give you more transparency into how Snowflake pricing works… with a few drawbacks. 

There are three places Snowflake charges you:

• Compute
• Storage
• Data transfer

Let’s take a look at how each of these processes work and see what your monthly budget will look like after using these services regularly. 

Compute Costs

Your Snowflake compute costs are based entirely on your: 

• Virtual warehouse compute: User-managed clusters where you control credit consumption directly that scale up or down based on your workload needs
• Serverless compute: Snowflake automatically manages these resources for tasks like Snowpipe data loading and certain query operations
• Compute pools: Dedicated resources for specific workloads that provide more predictable performance
• Cloud services compute: Background operations like query parsing and metadata management 

The majority of compute costs come from virtual warehouses. 

Storage Costs

You’ll pay $25-40 per terabyte per month for compressed data storage, though that number varies depending on your provider and region. The good news is that Snowflake’s automatic compression reduces your storage needs by 70-80% – though this varies from workload to workload – so you’ll be paying less than you would trying to keep raw data. 

And if you’re looking for the added security of Time Travel and Fail-Safe features? That’ll cost you extra. 

You can purchase Time Travel storage for one to 90 days, and Fail-Safe for up to seven. You’re looking at paying about $0.50-1.00 per TB a day, increasing your total Snowflake storage costs by about 20-30%. But investing in Time Travel and Fail-Safe storage means you have the added peace of mind knowing you can revert your data if needed. 

Data Transfer Costs

Any Snowflake data ingress is free. Data egress, or moving data out or between regions, is where you’ll see the bills pile up. 

Keep an eye on things like cross-cloud transfers. Those prices are easy to overlook when you’re evaluating platforms, but can easily pile up if you’re running data-heavy applications. Costs can sometimes reach as high as $0.09 per GB. 

Hidden Costs to Watch

Snowflake costs can get complicated fast. Even seasoned data scientists can be easily shocked when they get that first bill.

Other costs that always catch users by surprise include: 

• Snowpipe serverless compute services that automatically load streaming data but charge per compute hour used
• Cloud services layer costs (query parsing, metadata management) are only free to up to 10% of daily compute usage but then you’ll pay standard rates
• Marketplace data purchases
• Third-party integrations 

Databricks Cost Breakdown

While Snowflake keeps things simple with credits, Databricks takes a different approach. Let’s see how its pricing structure changes completely. 

DBU Pricing Structure

Databricks uses Databricks Units (DBUs) as pricing currency. 

Costs vary by workload type: 

• Compute starts at $0.07 per DBU for lightweight ETL tasks
• All-purpose compute ranges from $0.40-0.55 per DBU for interactive workloads
• SQL Compute for BI workloads cost $0.22-055 per DBU 

And as you start looking at Premium and Enterprise tiers, you’ll have to add a 30-100% cost premium. 

Cloud Infrastructure Costs

Here’s where Databricks gets complicated: you’ll have to pay both Databricks and your cloud provider. Databricks will handle cluster orchestration. But AWS, Azure, or GCP will charge you separate for: 

• EC2 instances
• Storage 
• Networking 

Take your typical m5.large instance. It costs $0.096/hr from AWS, plus your Databricks markup. If you’re working with a compute-intensive job, your cloud infrastructure might make up 40-60% of the total costs. 

This dual billing structure makes cost prediction and optimization much trickier in comparison with Snowflake. 

Storage Costs

All storage costs come directly from your cloud provider (Azure Blob, S3, etc.) at standard rates, typically between $0.02-0.05 per GB per month. 

Delta Lake’s versioning and transaction logs will add an additional 10-20% storage overhead, but investment in features like data compaction and Z-ordering can improve query performance to counteract that. 

Hidden Costs to Watch

The biggest Databricks costs to watch out for are: 

• Idle cluster time: Interactive clusters will keep running – and charging – you until manually terminated 
• Cross-region data transfer: Costs for transfers between Databricks workspaces and cloud storage can be quite steep
• Multi-cloud deployment: Compounds any cross-region data transfer costs

Total Cost of Ownership Analysis: Databricks vs Snowflake

So, how much does it cost to run Databricks and Snowflake for similar workloads? 

Snowflake’s SQL-first approach and managed infrastructure mean you’re paying less for overhead. As long as your team comes prepared with some SQL skills, they can manage Snowflake easily, taking advantage of the platform’s optimization features. 

Databricks, on the other hand, requires a more technical team. You’ll need experts well-versed in Apache Spark, cluster management, and cloud infrastructure. The steeper learning curve does mean you’ll get more flexibility for complex use cases. 

Let’s take a look at some examples of Databricks vs Snowflake costs for certain scenarios: 

Scenario 1: Traditional BI Workloads (SMB – 50TB, 100 users) 

• Snowflake: $8,000/month ($5,000 compute, $1,500 storage, $1,500 data transfer)
• Databricks: $11,000/month ($4,000 DBUs, $5,000 cloud compute, $1,500 storage, $500 transfer) 
• Winner: Snowflake 

Scenario 2: ML-Heavy Workloads (Enterprise – 500TB data, ML pipeline) 

• Snowflake: $35,000/month (limited ML capabilities require external tools)
• Databricks: $32,000/month ($18,000 DBUs, $10,000 cloud compute, $3,000 storage, $1,000 transfer) 
• Winner: Databricks

Scenario 3: Mixed Workloads (Large Enterprise) 

Running both platforms is also a solution. You can use Snowflake for BI/analytics and Databricks for ML/data science. Though you’ll have to eat the costs of a 20-30% increase in licensing, you’ll usually get better ROI by matching your workloads to more optimal platforms. 

Which is Better: Databricks vs Snowflake

Snowflake vs Databricks: which platform is better? It ultimately depends on what you’re trying to accomplish. 

When to Choose Snowflake

Pick Snowflake when:

• You’re focusing on BI/analytics
• You have limited technical resources
• You have predictable workloads
• You need to meet compliance requirements 

When to Choose Databricks

Pick Databricks when: 

• Your workloads are ML/data science heavy
• You need real-time processing
• You have a very technical team
• You have an existing cloud investment 

Cost Optimization Strategies

You know which platform is best for your organization, and you have an idea how much you’ll have to pay… that is, how much you’ll have to pay without the right cost optimization strategies. 

Here’s what you should do once you have Snowflake or Databricks set up for optimization performance, and an optimal bill: 

Snowflake Cost Optimization 

At Yuki, these are the four places we recommend our clients first look when they’re getting started with cost optimization: 

1. Right-sizing virtual warehouses
2. Query optimizations
3. Automated scaling policies
4. Storage optimization

Databricks Cost Optimization 

When it comes to Databricks optimization, try these three tactics to keep costs manageable: 

1. Cluster management
2. Workload optimization
3. Resource allocation

Choose the Right Platform for Your Budget

The choice between Snowflake and Databricks ultimately comes down to your specific use case and technical needs. Snowflake is best for traditional BI and analytics with its predictable costs, and Databricks is better for those who need more flexibility for ML workloads. 

And the best way to keep costs low for cloud tools? Investing in a third-party automation tool that does all of the work for you. Plug and play tools like Yuki mean you can start saving with your Snowflake spend from day one, no extra dev work needed. 
Curious to see how much Yuki can help your Snowflake setup save? Get a free demo today.