Snowflake vs Databricks Pricing: Who’s Really Playing Fair?

Introduction: Two Giants, Two Very Different Approaches to Value

Snowflake and Databricks both promise to transform how your business handles data. But their pricing philosophies couldn’t be more different, and these differences reveal something fundamental about how each company views customer relationships.

Snowflake’s approach: “Use our cloud, pay our way, trust our optimisation.”

Databricks’ approach: “Use your cloud, pay for what you consume, optimise as you see fit.”

Both claim fairness. Both promise value. But only one approach actually puts control in your hands. The question is: which pricing model reflects how data platforms should work in 2025 and what does this tell us about the broader shift towards transparent, value-led pricing across the entire data stack?

 

The Problem: Pricing Models That Serve Vendors, Not Customers

Before we dive into the Snowflake versus Databricks comparison, let’s acknowledge the elephant in the room: most data platform pricing is designed to benefit vendors, not customers.

Whether it’s compute credits that expire, row-based charges that punish growth, or complex formulae that make cost forecasting impossible, the pattern is clear:

• Pricing complexity obscures true costs and prevents accurate budgeting
• Vendor-controlled optimisation means you can’t truly control your spend
• Lock-in mechanisms ensure switching costs remain prohibitively high
• Billing models that benefit from inefficiency rather than reward best practices

The result? Data teams spend more time managing vendor relationships than optimising their data operations. Finance teams struggle to forecast costs. Engineering teams work around pricing constraints instead of focusing on performance.

Sound familiar? It should, because this is exactly how ETL vendors have operated for years.

 

Snowflake’s Approach: Optimisation as a Service

Snowflake’s pricing philosophy centres on simplicity through abstraction. You pay for compute credits, Snowflake handles the optimisation, and theoretically everyone wins.

What Snowflake Gets Right

Predictable unit pricing: Credits provide a consistent unit of measurement across different workloads and regions.

Automatic optimisation: Query performance improvements, storage compression, and compute scaling happen without manual intervention.

Usage-based scaling: You only pay for compute when queries are actually running.

Where Snowflake’s Model Shows Its Limits

Cloud lock-in: You must use Snowflake’s managed service on their chosen cloud regions. No option to run on your own infrastructure.

Credit complexity: Understanding what drives credit consumption requires deep platform knowledge that most teams don’t have.

Optimisation opacity: Snowflake controls performance tuning, which means you can’t optimise for your specific cost constraints.

Renewal leverage: Annual credit commitments create pressure to forecast usage accurately or risk overpaying for unused credits.

The Snowflake Philosophy: Trust Us, We’ll Handle It

Snowflake’s approach reflects a particular view of customer relationships: “We know how to run data platforms better than you do, so let us handle the complexity.”

This works brilliantly when your workloads fit Snowflake’s optimisation patterns. It becomes expensive when they don’t.

 

Databricks’ Approach: Cloud Choice and Consumption Control

Databricks takes a fundamentally different approach. Rather than abstracting away infrastructure choices, they embrace them.

What Databricks Gets Right

Cloud agnostic: Run on AWS, Azure, or GCP using your own accounts and infrastructure.

Transparent consumption: DBU (Databricks Unit) pricing maps directly to underlying compute resources.

Optimisation control: You can tune clusters, adjust auto-scaling, and optimise costs based on your specific requirements.

No infrastructure lock-in: Your data and compute stay in your cloud environment.

Where Databricks’ Model Creates Friction

Complexity burden: More control means more decisions – cluster sizing, auto-scaling policies, and cost optimisation become your responsibility.

Multi-cloud overhead: Managing Databricks across different clouds requires additional operational expertise.

DBU variations: Different workload types have different DBU rates, making cost forecasting more complex.

The Databricks Philosophy: You Know Your Business Best

Databricks’ approach reflects a different philosophy: “You understand your workloads and constraints better than we do, so we’ll give you the tools to optimise accordingly.”

This works excellently when you have the expertise to manage complexity. It becomes overwhelming when you don’t.

 

The Deeper Question: What Does “Fair” Actually Mean?

Both Snowflake and Databricks claim their pricing is fair, but they define fairness differently.

Snowflake’s definition: Fair means you get enterprise-grade performance without needing enterprise-grade expertise.

Databricks’ definition: Fair means you have control over your infrastructure and can optimise costs based on your specific needs.

Both definitions have merit. But they miss something crucial: truly fair pricing should be transparent, predictable, and aligned with the value you actually receive.

What Neither Approach Addresses

Growth penalties: Both platforms can become expensive as data volumes increase, especially if your growth patterns don’t match their optimisation assumptions.

Vendor dependency: Whether it’s Snowflake’s managed service or Databricks’ platform complexity, you’re still dependent on vendor-specific knowledge and tooling.

Stack fragmentation: Your warehouse costs are just one piece of your data spend, what about ETL, transformation, and orchestration?

 

What Smart Data Teams Do Differently

The most successful data teams we work with have learned something important: the warehouse pricing debate misses the bigger picture.

They Focus on Total Cost of Ownership

Smart teams don’t optimise warehouse costs in isolation. They look at their entire data stack:

• Engineering time spent on ETL maintenance diverts resources from high-value projects
• Vendor lock-in at the ETL layer creates downstream constraints
• Row-based ETL pricing penalises growth regardless of warehouse efficiency
• Hidden operational costs accumulate through manual processes and firefighting

They Choose Warehouse-Agnostic Infrastructure

Instead of debating Snowflake versus Databricks, leading teams build modular architectures that avoid vendor lock-in:

• Freedom to adapt when new requirements emerge without rebuilding your entire stack
• Ability to evaluate and adopt new technologies as they become available
• Strategic flexibility to negotiate better terms when you’re not locked into one platform

This is only possible when your ETL layer doesn’t force artificial constraints. With warehouse-agnostic ETL, platform choice becomes a tactical decision based on workload requirements, not vendor limitations.

They Demand Pricing Transparency

The best data teams insist on pricing models that are:

• Predictable: You can forecast costs based on business growth, not vendor algorithms
• Transparent: You understand exactly what drives costs and can optimise accordingly
• Aligned: Pricing rewards efficiency and best practices rather than penalising them

 

Supporting Insight: The True Cost of Platform Lock-In

Recent industry research reveals how pricing models impact long-term flexibility:

• 68% of data teams report feeling “trapped” by vendor pricing models that seemed fair initially
• Platform switching costs average 3-4x higher when ETL and warehouse are tightly coupled
• Teams with warehouse-agnostic ETL save an average of 45% on total data platform costs

This highlights why warehouse pricing optimisation is a tactical issue, but ETL pricing is a strategic one.

 

Matatika’s Approach: Performance-Based Fairness

At Matatika, we’ve watched the Snowflake versus Databricks pricing debate for years. Both companies have pushed the industry forward, but neither addresses the fundamental issue: pricing should align with the value you actually receive.

Performance-Based Pricing: A Better Definition of Fair

Our approach is simple: you pay for the infrastructure your pipelines actually use.

• No row-based charges that penalise growth
• No vendor-controlled optimisation that obscures costs
• No lock-in mechanisms that prevent platform choice
• No surprise bills when your business scales

This means:

• Predictable costs that scale with your infrastructure needs, not arbitrary metrics
• Full transparency into what drives costs and how to optimise them
• Platform freedom to use Snowflake, Databricks, or any combination that serves your business
• Growth alignment where efficiency improvements reduce costs instead of increasing them

Open Source Foundation, Commercial-Grade Support

Because Matatika is built on an open-source core, you get:

• Complete visibility into how your pipelines operate
• Freedom to customise and extend functionality as needed
• No vendor lock-in through proprietary formats or transformations
• Community innovation combined with enterprise-grade support

Warehouse Agnostic by Design

We work natively with Snowflake, Databricks, BigQuery, Redshift, and others because your ETL shouldn’t dictate your warehouse strategy.

This means you can:

• Use the best platform for each workload
• Negotiate better warehouse pricing when you’re not locked in
• Experiment with new technologies without rebuilding everything
• Make platform decisions based on technical merit, not ETL constraints

 

Frequently Asked Questions

Should I choose Snowflake or Databricks based on their pricing models?

Both have merit depending on your team’s expertise and workload patterns. Snowflake works well when you want managed optimisation, whilst Databricks excels when you need control and flexibility. The bigger question is: how do you maintain the freedom to use both strategically?

How does performance-based ETL pricing compare to warehouse pricing?

Performance-based pricing aligns costs with actual infrastructure usage rather than arbitrary metrics like rows processed. This typically results in 30-70% lower costs than traditional ETL vendors, often saving more than warehouse optimisation efforts.

Can I use the same ETL pipelines across Snowflake and Databricks?

With warehouse-agnostic ETL like Matatika, yes. You write transformations once and deploy them to any supported warehouse. This eliminates the need to choose sides in the Snowflake versus Databricks debate.

What happens if pricing models change after I’ve committed?

Vendor pricing changes are common, both Snowflake and Databricks have adjusted their models multiple times. With open-source, warehouse-agnostic ETL, you maintain flexibility to adapt without rebuilding your entire stack.

How do I evaluate the true fairness of a pricing model?

Ask three questions: Can I predict costs based on business growth? Do I understand what drives my bill? Does pricing reward efficiency or penalise it? If any answer is no, the model prioritises vendor interests over yours.

From Vendor-Centric to Value-Led Pricing

The Snowflake versus Databricks pricing debate reflects a broader shift in data platforms: from vendor-controlled models to customer-centric approaches.

But this shift is incomplete whilst ETL remains stuck in the row-based pricing era. True pricing fairness requires:

• Transparency across your entire data stack
• Predictability that aligns with business growth
• Freedom to choose the best tools for each workload
• Efficiency rewards that reduce costs as you optimise

 

Book your renewal planning session

Whether you’re committed to Snowflake, excited about Databricks, or using both, we’ll show you how performance-based ETL pricing changes the economics of your entire data stack. We’ll review your current costs, demonstrate warehouse-agnostic flexibility, and give you a roadmap for truly fair data platform pricing.

Book Your Free Consultation →

#Blog