Understanding Today’s ETL Pricing Landscape: Column vs Row Approaches

Most data teams don’t switch ETL platforms because they’re happy. They stay because switching feels risky, time-consuming, or impossible to get signed off. But the real risk is in doing nothing—especially when it comes to how you’re being charged.

The data landscape has transformed dramatically over the past decade, yet ETL pricing models have remained curiously static. The column vs row debate in ETL pricing represents a fundamental shift in how modern data teams should approach cost optimisation.

The truth? How you pay for ETL could be costing you significantly more than necessary.

Row-based pricing made sense when ETL was new and complex. But today, ETL is a commodity—a standardised, repeatable function delivered by dozens of vendors. Yet pricing models haven’t evolved with the technology, leaving many data teams paying premium prices for what should be a utility service.

 

The Current State of ETL Pricing Models

Let’s examine the common pricing models in the market today and what they mean for your data strategy:

 

Row-Based Pricing: The Legacy Tax on Growth

How it works: You pay for every row processed, regardless of whether that data is new, valuable, or even used.

Who uses it: Fivetran, Stitch, Hevo Data, Portable, Integrate.io

Merits: Scales with data activity; relatively transparent for row-centric use cases; easy to understand for finance teams.

Weaknesses: This model actively punishes growth. As your data volumes increase—even if your usage patterns stay the same—your costs spiral upward. The row-based approach becomes especially expensive at high volumes and is not ideal for wide tables or large datasets.

 

Volume-Based Pricing: The Data-Size Escalator

How it works: You pay based on the total volume (usually in GB) of data processed.

Who uses it: Airbyte Cloud, Estuary (combined with connector-based)

Merits: More consistent than row-based for wide tables; aligns somewhat with infrastructure costs; typically more predictable than row-based models.

Weaknesses: Still scales directly with data growth; penalises data-heavy operations; offers no incentive for efficient pipeline design or column-oriented optimisation.

 

Credit-Based: The Opaque Calculator

How it works: Users buy credits, which are consumed based on actions (e.g., pipeline runs, data processed, connector use).

Who uses it: Matillion, Rivery

Merits: Flexible; can align with true usage patterns; often covers multiple types of resources under one system.

Weaknesses: Credit consumption can be opaque or confusing; difficult to estimate future spend accurately; requires constant tracking of credit usage; can lead to unexpected shortfalls.

 

User/Subscription-Based: The Team Size Multiplier

How it works: Charges based on the number of users or a fixed subscription tier.

Who uses it: Talend, Informatica, Coalesce

Merits: Simple to understand; often includes unlimited data; predictable billing.

Weaknesses: Rarely aligns with actual data usage or processing needs; can be prohibitively expensive for large teams with relatively light usage; discourages democratising data access across organisations.

 

The Column vs Row Debate in ETL Processing

At the heart of the ETL pricing discussion is a fundamental technical difference in how data is processed: row-by-row or column-by-column.

Traditional ETL approaches process data row-by-row, which means every field in every row is handled regardless of whether all that data is actually needed. This aligns with traditional row-based pricing models but creates inherent inefficiencies.

Column-oriented processing, by contrast, only handles the specific data elements needed for a particular operation. This approach is inherently more efficient for many analytical workloads, especially when only certain columns from a wide table are required.

 

Performance-Based Pricing: The Evolution Towards Efficiency

While the industry clings to outdated pricing models, performance-based pricing represents a fundamental shift that better aligns with column-oriented processing approaches.

How it works: You pay for the actual infrastructure resources used (CPU, memory, IO)—not arbitrary metrics like row counts, data volume, or abstract “credits.” This model inherently rewards column-oriented approaches that only process the data you actually need.

Who uses it: Matatika

Merits:

• Costs align with true resource usage and actual value delivered
• Rewards pipeline efficiency and column-oriented optimisation rather than penalising it
• Predictable as you scale without sudden cost jumps
• Avoids penalising business growth
• Encourages optimisation and efficient design

Weaknesses:

• May require monitoring of resource usage (though this leads to better practices)
• Unfamiliar to teams used to row/volume pricing models

 

 

Frequently Asked Questions About ETL Pricing Models

What’s the fundamental difference between row-based and column-based processing?

Row-based processing handles every field in every row, even if you only need a few columns for your analysis. Column-based approaches only process the specific columns needed, which is significantly more efficient for many analytical workloads. This technical difference directly impacts cost when paired with the right pricing model.

How do different pricing models scale as my data grows?

Row-based pricing scales linearly with data growth—if your data doubles, your costs double, regardless of computational needs. Credit-based pricing typically accelerates as your needs grow, burning through credits faster. User-based pricing remains flat but starts high. Performance-based pricing scales with computational efficiency, not data volume, meaning costs grow much more slowly than your data.

What hidden costs should I look for in my current ETL pricing?

Beyond the obvious row/volume charges, watch for:

• Idle infrastructure costs: On average, 20-30% of pipelines are inactive but still incurring costs.
• Inefficient processing: Full-table reloads when incremental updates would do (up to 90% more compute).
• Duplicate processing: The same data processed multiple times across different pipelines.
• Overage charges: Unexpected costs when exceeding tier limits.

Why does row-based pricing punish growth?

As your business grows, your data naturally grows—often faster than your actual analytics needs. Row-based pricing directly ties cost to data volume rather than value delivered. This creates a direct disincentive to collect, store, and analyse more data, precisely when you need those insights most. It’s like paying for every ingredient in your kitchen rather than the meals you actually cook.

How does performance-based pricing avoid punishing growth?

Performance-based pricing charges based on actual computational resources used, not arbitrary row counts. Well-designed pipelines using column-oriented principles can handle significantly more data with only modest resource increases. This creates an expanding efficiency gap as you scale—what might be a minor cost difference initially can become enormous as your data volumes grow.

 

The Future of ETL Pricing: Moving Beyond Row-Based Models

For most data teams, the journey to better ETL pricing starts with understanding what you’re currently paying for. A detailed cost audit often reveals significant opportunities for optimisation—even before considering a platform switch.

With the ETL landscape evolving, the column vs row debate is increasingly relevant. Performance-based pricing that rewards column-oriented efficiency represents the future—a model that aligns costs with actual value, rewards efficiency, and scales sensibly with your business.

For organisations looking to future-proof their data strategy, the choice between column vs row approaches to ETL pricing isn’t just about cost—it’s about aligning technical incentives with business goals and ensuring that your data infrastructure can scale sustainably as your needs evolve.

 

From Confusing Pricing to Clear Cost Control

If you’re tired of ETL costs that don’t align with actual value, it’s time to understand what you’re really paying for. Most teams discover significant savings opportunities just by auditing their current setup against modern pricing approaches.

 

Book a free ETL renewal planning session

We’ll help you benchmark your current row-based pricing against column-oriented alternatives, identify hidden cost drivers, and show you exactly how performance-based pricing would impact your specific data volumes and usage patterns.

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