ETL Cost in 2025: Real Numbers, Hidden Fees & Smarter Strategies

Data teams are facing an increasing challenge as ETL costs become less predictable. Vendors across the industry have significantly raised their prices, forcing organizations to reassess their data strategies. 

Take Fivetran, for example, a predictable pricing model based on row volume will no longer offer discounts after March 2025, leaving teams with large connector volumes facing higher costs. Similarly, Airbyte has shifted to capacity-based pricing, moving from the traditional Monthly Active Rows (MAR) model to billing based on data worker time. While this may benefit some, it also creates complexities for scaling teams who must now navigate less transparent costs.

Talend presents a more extreme case, with price increases forcing many long-time customers to reconsider their data architecture. dbt Cloud, once a more affordable option, now charges consumption-based per user annually, in addition to usage-based fees. Legacy users may have benefited from discounted pricing, but new users are feeling the impact of these changes. Pricing models for ETL tools are increasingly complex, with many teams facing unpredictable costs due to connection-level fees, capacity usage, and data volume fluctuations

In this article, we’ll explore how these factors contribute to rising costs and offer strategies for mitigating them, ensuring that you can continue to scale efficiently without the financial surprises.

What ETL Really Costs Today? 

ETL pricing has evolved considerably, with a variety of pricing models now available to meet the diverse needs of data teams. Each pricing model brings its own advantages and challenges, depending on the size and requirements of the business.

Let’s explore some of the most common models you’ll encounter today, along with examples of how they work and which types of businesses are best suited for each.

1. Pay-as-you-go: Flexible, Scalable, but Hard to Predict

The Pay-as-you-go (PAYG) model is similar to utility billing—customers pay based on actual usage during a billing period. This model offers flexibility and scalability, making it a popular choice for businesses with varying data needs. However, it can also lead to unpredictable costs, as your monthly bill depends on the amount of data processed, which can fluctuate.

For instance, Fivetran charges customers based on the data volume processed, typically calculated by the number of rows transferred. If your data pipeline processes millions of rows, your costs will scale accordingly. This makes PAYG ideal for businesses that need flexibility, but can also lead to unpredictable expenses as your data requirements grow.

Best Suited:  Startups, small businesses, or companies with seasonal data demands that may vary month to month. It’s perfect if you need flexibility without long-term commitments, but it can become costly as your data usage increases.

2. Subscription-Based Pricing: Predictable, but Limited by Caps/Features

The subscription-based model (also known as tiered pricing) is one of the most popular choices due to its predictability. You pay a fixed monthly or annual fee based on the features and resources available in your chosen tier. As your business grows, you may need to move to higher tiers that provide additional connectors, more data processing capacity, or enhanced features.

For example, Hevo offers pricing tiers starting at $249/month for the “Starter” plan, supporting basic connectors and up to 1 million rows of data. The “Pro” plan, starting at $999/month, includes more advanced features and additional connectors.

Best for: This model is ideal for medium-sized businesses or established enterprises with stable data needs, where predictable billing and resource planning are key. However, it may be restrictive if your business experiences unexpected spikes in data volume or needs features that are only available at higher tiers.

3. Usage-Based and Connector-Based Pricing: Pay for What You Use

The user-based pricing model charges based on the number of active users accessing the platform, while the connector-based pricing depends on the number of data sources or destinations connected. This approach allows businesses to control costs based on the usage or connectors, rather than overall data volume.

An example of user-based pricing is where dbt Cloud charges per user annually, depending on the plan and features, with additional usage-based fees for computing resources and advanced functionality, and Fivetran charges per connector. 

Best for: User-based pricing works well for larger enterprises with defined user roles or external consultants requiring access to the platform. Connector-based pricing is ideal for businesses that need to integrate multiple data sources but must manage the costs as the number of connectors increases.

4. Flat-Rate and Freemium Models: Simple, but Limited by Features

The flat-rate model offers a single, fixed price for the service, regardless of the features used or the amount of data processed. It provides simplicity and predictability but may not be cost-effective for businesses with lower data volumes or more complex needs. 

Skyvia offers a flat-rate pricing model starting at $79/month. This plan provides unlimited data integrations, unlimited connectors, and up to 5,000 data rows per month. Additional data processing may incur extra charges, but the core price remains predictable, regardless of how much data is processed within the plan’s limit.

The freemium model, on the other hand, provides basic access to the platform for free, with the option to upgrade as your business grows and requires more advanced features.

Best for: The flat-rate model is ideal for businesses with predictable, stable data needs that want to avoid surprise costs. It’s well-suited for companies that prioritize simplicity and do not experience significant fluctuations in data volume.

For instance, Airbyte offers a freemium pricing model, where the basic version is free to use, providing essential features with some limitations. The free plan allows users to integrate data from several sources and destinations, but it may have limitations on features like scheduling, data volume, or connectors. As the business grows or requires more advanced features, they can upgrade to a paid plan with additional connectors, higher data limits, and advanced support.

Best for: The freemium model is best for small businesses or startups testing out an ETL solution without making a major financial commitment. While it’s a great way to assess value, businesses will likely need to upgrade to a paid plan as they scale or require additional features.

Key Factors Impacting ETL Costs

When evaluating the costs of an ETL solution, it is essential to understand the underlying factors contributing to the overall price. While many focus on the visible subscription fees, the true cost of ETL solutions is influenced by several variables that can significantly impact your total expenditure. Below, we explore the primary cost drivers and why they matter.

1. Data Volume and Frequency: 

The amount of data being processed and the frequency at which it is transferred directly influence ETL costs. Many ETL providers charge based on the data volume; the more rows or data you process, the higher the cost. Frequent data loads in high-throughput environments will naturally lead to increased expenses. 

Understanding your data needs how much data you need to move and how often, is critical when assessing costs. For businesses with large datasets or those in dynamic industries where data volume fluctuates, these variables can lead to significant cost variation over time.

2. Number and Type of Connectors: 

Connectors are a pivotal element of any ETL solution, as they facilitate data transfer between systems. Prebuilt connectors that are included as part of standard offerings are typically covered in the base pricing. However, custom connectors designed to integrate niche or legacy systems can result in additional development costs or premium pricing.

As businesses evolve, the need for custom integrations increases, and this can escalate costs. It’s essential to weigh the long-term need for specialized connectors against the potential savings offered by prebuilt options.

3. Real-Time vs. Batch Ingestion:

The choice between real-time and batch ingestion has a substantial impact on both performance and cost. Real-time ingestion, which involves continuous data transfer, requires significantly more infrastructure and computational power. As such, it generally incurs higher costs than batch processing, where data is processed at scheduled intervals.

For organizations with less time-sensitive data requirements, batch processing offers a more cost-effective solution, while real-time processing is better suited for businesses that rely on immediate data for critical decision-making. Choosing the appropriate mode depends on your operational needs and budget.

4. Transformation and Pipeline Complexity:

The complexity of your data transformation processes adds to the cost of your ETL solution. More intricate data pipelines that include advanced transformation logic, multiple data processing steps, or conditional branching require greater computational resources and more sophisticated orchestration.

As the complexity of your pipelines increases, so too do the costs, especially when utilizing tools that charge for advanced features or higher compute usage. Businesses should carefully evaluate whether their data processing needs justify the investment in complex pipeline configurations.

5. Hosting Type: 

The deployment model, whether cloud-based or on-premises, has a significant impact on the cost structure of an ETL solution. Cloud-based ETL solutions are typically billed on a usage-based model, offering flexibility but also introducing variability in cost as your usage scales.

In contrast, on-premises ETL solutions typically require higher upfront capital investment, including infrastructure and licensing fees. Additionally, on-premises deployments often come with ongoing maintenance costs and the responsibility for scaling, updates, and security.

For businesses with fluctuating data needs or those looking to minimize capital expenditure, cloud-based solutions are often more cost-effective. However, on-premises solutions might make sense for organizations that need greater control over data security or compliance.

Tip: Read our ebook to understand which form of data storage is better for you. Download now

6. Support Tiers and Hidden Service Charges: 

Support services often represent a hidden cost in the total price of an ETL solution. While basic support may be included, businesses requiring advanced support such as dedicated assistance, faster response times, or custom SLAs, can expect to pay extra.

Additionally, many ETL solutions introduce hidden charges for services such as API quota extensions, pipeline monitoring, or connector upgrades. It is crucial to assess the full range of support services available and understand any potential charges beyond the base pricing.

7. Feature Set and Add-ons: 

Enterprise-grade features, such as enhanced security, audit trails, role-based access controls, and advanced monitoring, often come at a premium. As your data integration needs expand, you may require these additional features to support large teams or meet compliance standards.

While these features add substantial value, they also increase the overall cost. Businesses must carefully evaluate whether these advanced capabilities are necessary or if a more basic feature set will suffice for their current needs.

8. Scalability and Performance Guarantees:

ETL solutions that are designed to support high scalability and performance guarantees are often priced at a premium. Solutions that promise guaranteed SLAs and high uptime tend to involve higher costs to ensure that the infrastructure can handle growth, high concurrency, and critical workloads.

For businesses that anticipate significant growth in data volumes or require high availability for their data pipelines, scalability and performance guarantees are essential. However, these advanced capabilities come with a cost, and businesses must balance the need for performance with the budget available.

Real Cost Comparison: Top ETL Tools in 2025

Choosing the right ETL tool is crucial for optimizing your data integration processes and managing your budget effectively. With so many options available in 2025, understanding the true cost of each solution is essential for making the best choice based on your company’s specific needs. While the upfront subscription costs are often the focus, the overall cost of an ETL solution depends on various factors such as data volume, number of connectors, frequency of data loads, and the scale of your business. 

This guide provides a detailed comparison of the pricing structures of major ETL tools to help you make an informed decision.

ETL Tool	Starting Price	Free Tier Availability	Core Cost Factors	Best Fit For
Hevo	$249/month (Starter Plan)	No (Free trial available)	Users, Connectors (150+), Data Volume	Small to medium-sized businesses seeking a robust, all-in-one ETL solution with scalability and excellent customer support.
Fivetran	$500 /month (for 1M rows)	No	Users, Connectors (200+), Data Volume	Enterprises dealing with large-scale data and high-frequency data loads.
Airbyte	Free (Open-source); $1,000/month (Cloud version)	Yes (Open-source)	Users, Connectors (100+), Data Volume	Small to medium businesses needing a flexible, low-cost solution (open-source available).
Skyvia	$99/month (Pro Plan)	Yes (Limited features)	Users, Connectors (Unlimited on higher tiers), Data Volume	Small businesses and startups with low to moderate data processing needs.
Stitch	$100/month (up to 5M rows)	Yes (Limited features)	Users, Connectors (100+), Data Volume	Small to mid-sized businesses looking for an easy-to-use ETL tool.
Matillion	$2,000/month (Enterprise Plan)	No	Users, Connectors (100+), Data Volume	Large enterprises with complex, high-volume data integration and transformation needs.
Portable	$99/month (Starter Plan)	Yes (Basic integrations)	Users, Connectors (50+), Data Volume	Small to medium businesses looking for a cost-effective and simple ETL solution.

Which Tool is Best for SMBs vs. Enterprises?

• For SMBs: Hevo is ideal for small to medium-sized businesses that need an easy-to-use, reliable, and scalable ETL tool. It offers a low-risk entry point and excellent value, allowing businesses to grow without facing steep price increases or hidden fees. Additionally, Airbyte and Skyvia are cost-effective options for those looking for flexibility, though they may require more hands-on management.
  
• For Enterprises: Larger businesses with complex data pipelines and high-frequency data loads may lean towards Fivetran or Matillion, both of which are designed for large-scale environments and come with advanced features. 

However, Hevo strikes an attractive balance, offering robust capabilities without the significant price escalation seen with enterprise-grade tools.

How to Reduce ETL Costs Without Cutting Value

As businesses scale, the cost of ETL processes can quickly become a significant part of the data budget. Here are some actionable strategies to optimize your ETL processes and maintain high performance without increasing costs.

1. Batch Processing Strategies to Lower Compute Bills

While real-time data processing is valuable for certain use cases, batch processing remains a cost-effective method for many businesses. Batch processing optimizes resource usage because it allows you to run heavy data transformations during off-peak hours, using fewer resources.

This method works especially well for businesses that don’t require immediate insights from every data point and can afford to wait for periodic updates. Scheduling your ETL jobs during off-peak times can lead to substantial savings on cloud compute costs, especially if you’re using usage-based cloud services.

2. Using Data Lake Ingestion Where Appropriate

Data lakes offer an efficient way to store large volumes of raw data in a cost-effective manner. By ingesting data directly into a data lake (rather than processing it immediately into a structured database), businesses can save on processing costs, as they don’t need to perform costly transformations or structure data right away.

Data lakes are particularly beneficial when working with large, unstructured datasets. Instead of transforming data during the ETL process and incurring higher computational costs, the data can be ingested into a lake and transformed later, on-demand, or when required for analytics. This can help optimize your costs while maintaining flexibility in how data is stored and accessed.

3. Choosing Tools with Inclusive Support or Low/No Connector Fees

One of the hidden cost drivers in many ETL solutions is the connector fees for integrating data sources. Many tools charge extra for each additional connector or API call, which can accumulate quickly as your data needs grow. To mitigate this, consider tools that offer inclusive support for a wide range of pre-built connectors without charging extra fees for each one.

Look for ETL platforms that offer unlimited connectors or a flat fee for any number of integrations, ensuring that your costs stay predictable even as you add more data sources. Additionally, some tools include customer support in their subscription, reducing the need to pay extra for support services when issues arise.

4. Open-Source + Managed Hybrid Strategies

If you’re looking for maximum flexibility and cost savings, consider adopting an open-source ETL solution alongside a managed cloud service. This hybrid approach allows you to take advantage of the low upfront costs and flexibility of open-source tools, while also leveraging the reliability and scalability of managed cloud services for specific, high-demand parts of your data pipeline.

For example, Airbyte is an open-source ETL tool that can be customized and hosted on your own infrastructure, providing complete control over the process. When paired with a managed service for high-availability or critical workloads, this can create a cost-effective setup that scales as needed, without paying for features you don’t use.

5. Avoiding Vendor Lock-In and Overengineering

Vendor lock-in occurs when you become overly dependent on a single ETL provider, making it difficult and costly to switch solutions or migrate data to different systems. To avoid this, focus on open standards, interoperability, and modular solutions. Choosing tools that allow you to mix and match different components of your data pipeline, or that offer seamless integration with various services, reduces the risk of lock-in and ensures that you’re not overpaying for features you don’t need.

In addition, avoid overengineering your ETL pipelines by not building overly complex transformations or integrations that aren’t necessary for your business’s goals. Overengineering can lead to unnecessary costs for maintenance, scaling, and complexity. Instead, focus on keeping your ETL workflows simple and cost-efficient while maintaining the flexibility to scale as your data requirements grow.

How can Hevo Data be a cost-effective solution?

Hevo Data delivers on all fronts, offering an intuitive, all-in-one platform that simplifies data integration while keeping costs predictable. With transparent pricing, robust features, and comprehensive support, Hevo ensures businesses can scale seamlessly without hidden costs. Here’s how it stands out: 

• Complete and Scalable Solution: Hevo’s $239/month pricing includes everything businesses need—150+ pre-built connectors, unlimited data volume, and built-in data transformations, all under one umbrella. Unlike other tools that charge separately for premium features or scalability, Hevo provides an all-inclusive package without hidden costs, offering predictable pricing as your data needs grow.
  
• Customer Support: Hevo goes beyond basic features by offering exceptional customer support at no extra charge, ensuring businesses don’t need to worry about additional service costs as they scale.
  
• Ease of Use: Hevo provides a user-friendly platform that allows businesses to seamlessly integrate data without complex configurations. For small to medium-sized businesses, this ease of use, combined with a predictable, flat-rate price, makes it a highly cost-effective solution for long-term scalability.
  
• Robust Integrations and Automation: Hevo allows businesses to focus on their core operations by automating routine data processing tasks. The comprehensive data pipeline automation reduces the need for manual oversight, leading to long-term cost savings.

Sign up for a 14-day free trial and enhance your data pipelines within minutes!

FAQs