CDC Tools With the Most Predictable Pricing for High-Volume Data Pipelines

The most predictable pricing for CDC solutions comes from a billing unit your team can reasonably forecast. For instance:

• Volume-based: Estuary and Google Cloud Datastream
• Capacity-based: Airbyte Pro
• Compute-based: AWS DMS
• Event-based: Hevo
• MAR-based: Fivetran
• Multi-dimensional: Confluent Cloud
• Self-managed: Debezium

For high-volume pipelines, this can matter more than any quoted base cost. After all, costs can scale depending on data volume, the number of active rows, events, processing capacity, compute consumption, or even the infrastructure needed to run the pipeline.

Keep in mind, though, that pricing predictability doesn’t necessarily mean cheap pricing. It simply refers to the ability to predict how your costs will change as your workload grows.

Estuary is particularly interesting because its cloud pricing is based on data that’s been moved and connector task hours, with no MAR pricing and no requirement to run a separate Kafka stack for CDC.

What Makes CDC Pricing Predictable?

The simplest approach to pricing isn’t always the simplest to predict.

If a PostgreSQL database generates 500 GB of modifications this month, but next month, transaction volume doubles, Snowflake is added as another destination, and one large table needs to be backfilled, what will happen to the bill?

Well, that’s the million-dollar question. Literally.

With volume pricing, the first thing to consider is the number of bytes processed. MAR pricing is more closely related to the number of rows that have been changed. Event-based pricing focuses on inserts, updates, and deletes, whereas capacity-based pricing works differently because the cost stays the same until the system has reached its capacity.

Compute-based services add another variable. An increase in traffic can cause a serverless CDC service to go up, even when the average volume per month is relatively unchanged.

This gives us several ways to evaluate pricing predictability:

• How well does the spending align with the amount of data being ingested?
• What happens if row churn increases?
• Does adding a new destination create additional charges?
• Are initial loads and backfills priced separately?
• How are traffic spikes handled?
• Does this tool require Kafka, storage, monitoring, or other external infrastructure services?

Backfills are not uncommon occurrences, and as such, they deserve particular attention. Teams run them when they add destinations, fix transformations, recover from failures, and integrate new use cases. According to Estuary’s guide to evaluating CDC solutions for 2026 (How to Evaluate Change Data Capture (CDC) Solutions), they should also be tested along with traffic bursts, destination slowdowns, and backlog recovery.

How We Evaluated the Tools

As mentioned in the intro, price comparisons can be difficult when each vendor presents its pricing in a different way.

That’s why we looked at each vendor’s pricing model first and identified exactly what it meters, whether it’s the size of data, number of rows/events/connector operations, or infrastructural capacity.

Then, we focused on the technical architecture, common use cases, and discussion on platforms like Reddit. However, keep in mind that these discussions are anecdotal. They can help identify potential issues or unexpected charges reported by engineers, but they aren’t a substitute for official pricing information.

In short, these are the factors we took into account during our evaluation:

• Pricing models
• Price transparency
• Performance at heavy loads
• Backfilling management
• Cost of multiple destinations
• Infrastructure requirements

Note from the publisher: This piece was written by Estuary. We know more about Estuary than any other product. Information on other platforms comes from recent vendor documentation and public user feedback.

Quick Comparison of CDC Pricing Models

The table below highlights the pricing model for CDC used in each tool and the primary cost driver as the workload increases.

Tool	Pricing Model	Cost Driver	Useful for	Main Point to Consider
Estuary	Data volume and task hours	GB moved and connector runtime	Managed high-volume CDC	Billable data at both capture and materialization levels
Airbyte	Volume-based on Standard; capacity-based on Pro	Data volume or data workers	Teams looking for managed and capacity-based ingestion	Capacity must still be sized for concurrency.
Google Cloud Datastream	Processed GiB	Data processed by Datastream	GCP-native CDC	Bytes processed may differ significantly from raw source size.
AWS DMS	Replication compute	Instance hours or DCUs	AWS database replication	Service cost is based on capacity and usage time.
Hevo	Event/credit-based	Records inserted, updated, and deleted	Workloads with measurable event rates	High change rates increase event consumption.
Fivetran	Monthly Active Rows	Unique active rows per connection	Low-maintenance managed ingestion	Row churn and connection structure affect MAR.
Confluent Cloud	Multidimensional pricing	Compute, tasks, throughput, storage, networking	Architecture built around Kafka	Total price is a sum of several factors.
Debezium	Open source	Infrastructure and engineering	Teams with existing streaming infrastructure	A free license doesn’t mean free CDC service.

As you can see, you can’t compare these values using a single “$ per GB” column.

CDC Tools With Predictable Pricing at Scale

Since the above-mentioned tools have quite different pricing models, it is necessary to know what each tool measures and how the cost varies with the increase in CDC load.

1. Estuary

Best for: Managed CDC at high volumes where data volume is easier to predict than row-level activity.

Estuary’s pricing is fairly simple. It charges $0.50 per GB captured and $0.50 per GB delivered to each destination. So, when 1 GB enters Estuary and 1 GB is delivered to a destination, the data usage portion of the bill is $1.

Connector pricing is based on the number of active connector instances. The first six active connector instances cost around $100 per month each. Additional connector instances are $50 per month, prorated based on usage. Keep in mind that both captures and materializations count as connector instances. For BYOC and private cloud deployments, annual pricing plans and additional infrastructure costs apply.

In simple terms: data usage + connector instances usage

Both parts are relatively easy for a data engineer to monitor. When billable GBs double while the pipeline configuration stays the same, then the data usage cost also doubles. However, twice as many database updates don’t necessarily mean twice as many bytes. The relevant metric, therefore, is billable data volume, not the number of changes.

One architectural detail becomes important when pipelines branch out. Estuary separates data capture from delivery:

Capture > Collections > Materializations

A single capture occurs in collections, which in turn supply multiple destinations. Imagine PostgreSQL needs to stream into Snowflake, S3, and an operational database. There’s no need to pull from the source three times. A new materialization can simply reuse the data that Estuary has captured earlier. The additional destination will move data, but it doesn’t require another historical extraction from the production database.

This is significant for both pipeline management and cost. It also eliminates the need for Kafka, Kafka Connect, and connector management separately for teams that want managed CDC.

Real-world use cases from Estuary customers show how this works in practice. Xometry reports reducing data integration expenses by more than 60% with pay-as-you-go pricing. Glossier managed to cut their integration costs by 50%, with no unexpected fees. And Cosuno’s pipeline turned out to be 50% less expensive than its previous solution powered by Fivetran.

There are also Reddit discussions around rising Fivetran costs and Estuary as an alternative. Though these are useful for understanding how some data engineers view volume-based pricing, I wouldn’t treat reported savings on Reddit as reliable evidence.

Estuary has fewer connectors than some of its competitors, and Private/BYOC deployments are associated with additional business and technical concerns. It’s important to consider both the data sources and destinations when making these comparisons.

Not ideal for: Teams that require sources/destinations that are not part of the Estuary catalog of connectors, or teams that require general-purpose SaaS ELT more than CDC.

2. Airbyte

Best for: Teams that prefer either the pay-per-use or capacity-based pricing model.

Airbyte currently offers two main pricing models. The Standard plan is a fully managed, volume-based product. Airbyte Pro uses data workers in its capacity-based approach. One data worker can handle approximately three simultaneous syncs, depending on the pipelines being run.

The advantages of the latter are very easy to grasp. If your pipeline processes additional data for a limited period but stays within its existing capacity, you don’t incur extra charges per GB.

However, the problem starts when concurrency or complexity of your workload exceeds your capacity. At that point, you need to add more data workers.

The forecasting question is no longer, “What’s the volume of the data transfer?” but “What’s the processing capacity our pipelines will require?”

The Pro tariff is quote-based, so the pricing page explains the model using plenty of details rather than mere dollar amounts.

Airbyte also offers a self-hosted option..This changes the cost equation considerably because your engineering team is now in control of the platform.

In a recent discussion on r/dataengineering, some engineers described self-hosting as cheap and easy, while others reported issues with maintenance and reliability. Another user mentioned saving a significant amount of money annually with Airbyte self-hosted on Kubernetes.

These examples clearly demonstrate the actual trade-off: although self-hosting can reduce software costs, the operational work remains your team’s responsibility.

Not ideal for: Those who’d like little involvement in operations and don’t want to measure capacity sizing, self-hosting management, or reliability of the connector.

3. Google Cloud Datastream

Best for: GCP projects looking for managed CDC with volume-based pricing.

At a glance, Datastream looks like the simplest option. Google bills for GiBs processed. Volume tiers apply, so the price per GiB decreases as usage increases. Backfilling historical data is billed separately, with the first 500 GiB per month free, according to Google’s current pricing page.

There’s an important caveat, though. GiBs processed by Google aren’t always the same as the GiBs from the source database. As noted on Google’s documentation page, internally processed data may be up to 2 to 5 times larger than the actual data for many use cases.

That means per-GiB pricing is only as predictable as the definition of the GiB being measured.

While Datastream itself doesn’t charge anything beyond the processed volume of data, a full-fledged GCP pipeline might use a variety of additional services, such as Cloud Storage, BigQuery, Dataflow, networking, and more. So, even though Datastream’s own pricing meter is relatively simple, the total cost of a GCP-based CDC pipeline can involve several separate cost centers.

Not ideal for: Teams that use multi-cloud architecture and processes where increasing the number of bytes and GCP services makes cost harder to estimate.

4. AWS DMS

Best for: Migration and replication workloads on AWS.

AWS charges for replication capacity rather than rows or per-GB of changed data.

With provisioned DMS, users choose the replication instances and can incur additional charges for extra log storage should they opt for it.

DMS Serverless uses DCUs (DMS Capacity Units) as the unit of capacity measurement. 1 DCU corresponds to 2 GB of RAM, and AWS automatically scales the capacity of DMS Serverless, depending on the number of transactions performed.

This makes DMS Serverless more flexible but less predictable. AWS can add or decrease capacity as the workload changes, so bursts of replication activity can result in higher DCU usage. The good thing is that AWS allows users to set a maximum number of DCUs, which provides some control over costs.

This pricing model makes sense for teams that already run their infrastructure on AWS. However, if the question is something along the lines of “How many GB of CDC do we need?” translating that metric into the compute capacity DMS will need is far from straightforward.

Not ideal for: Companies that want a simple volume-based pricing strategy or a CDC solution independent of the AWS ecosystem.

5. Hevo

Best for: Companies with a predictable rate of database changes that can be estimated based on events.

Hevo operates on an event-driven model. According to the company’s documentation, billable actions include record insertions, modifications, and deletions made after creating the pipeline. Deleted source records are counted as updates for billing purposes.

This makes pricing closely tied to the amount of change activity. So, if your PostgreSQL database generates 20 million changed records each month, it should be relatively easy to make predictions for the following one.

However, the problem appears when the nature of the workload changes. A single bulk update for millions of records is going to generate a large number of events, even if the overall database size hasn’t changed much.

Here are two useful points to keep in mind about Hevo’s current consumption pricing at large volumes:

• Historical data loads are marked as free, and pricing is applied only to the changed data.
• The unit price goes down when the volume increases, and Hevo provides capacity commitments.

Hevo recommends running the workload for four or five days, calculating the average number of events per day, multiplying it by 30, and adding around 5-10% variance.

This highlights an important point about event-based pricing: it’s highly dependent on the stability of your event rate.

Not ideal for: Processes that experience fluctuating rates of events, large-scale changes, or sudden surges in inserts, updates, and deletions.

6. Fivetran

Best for: Teams that want managed data ingestion and can reliably predict Monthly Active Rows (MAR).

Fivetran uses MAR as its primary measure of connection usage.

Now, one of the most important words here is active. You’re not charged for the byte size of your table. Usage is actually determined based on the number of rows that become active through inserts, updates, and deletes. In other words, the same row modified multiple times during a month counts as one MAR charge.

Basically, higher synchronization frequency won’t affect the standard MAR as long as the same rows are being updated. Fivetran measures MAR usage based on unique row modifications and not on the number of sync operations.

However, keep in mind that History Mode uses a different calculation that takes the number of changes into account. In most cases, though, initial historical syncs are free from MAR charges, while the pricing policy introduced in March 2025 allows historical resyncs to be excluded from paid MAR under certain conditions.

So what makes MAR forecasts hard then?

One factor is row churn. If your workload touches a significantly larger number of distinct rows, MAR can increase even when the total volume of data changes very little.

Another thing is the connection structure. Fivetran now applies its usage curve at the connection level. Two connections that replicate the same source to two destinations will each have their own MAR.

The 2025 shift to connection-level tiering helps explain some of the community feedback around Fivetran pricing. One user in a recent r/dataengineering thread mentioned their monthly bill doubled after the changes in pricing. Another user explained their increase was small since the majority of connectors had processed over one million MAR in a month.

This explanation is much more informative than simply saying “Fivetran is expensive”. As you can see, it all depends on the account structure itself.

According to Fivetran, the 2025 pricing change was designed in such a way that accounts with a lot of similar connections would experience increases in price, whereas those that use one large connection would see reductions. Connections with an output of 1 to 1 million paid MAR are subject to a minimum connection fee as of January 2026. Inserts, updates, and deletes all contribute to paid MAR.

And there’s another way for larger customers to avoid unpredictability: Fivetran provides Enterprise License Agreements with a fixed annual fee in certain cases.

MAR is therefore not inherently unpredictable. It remains relatively predictable when row activity is stable.

Not ideal for: Systems with high row churn or independent connections where the use of MAR is more difficult to predict.

7. Confluent Cloud

Best for: Businesses that already have Kafka and event streaming on their platform.

Confluent approaches CDC from a stream-processing perspective. Usage-based fully managed connectors are primarily charged based on two measurements:

• Number of tasks performed per hour
• Data transfer throughput per GB

This applies to the Connect component alone, though. Kafka clusters can also incur compute, networking, and storage expenses, while other Confluent services such as Flink have their own usage metrics.

Hence, there is no single measurement that determines the cost, but that’s not necessarily a bad thing.

If Kafka already serves as the backbone for your company events, these new offerings might just be the infrastructure you were looking for. It’s just that budgeting requires more information than a simple line item or GB tracker.

Confluent has also focused on making this model more predictable. In August 2026, the company unveiled Connect Advantage bundles that allowed companies with consistent usage to make a minimum monthly commitment in exchange for lower-priced connectors and less variance in pricing. As you can see, predictable pricing isn’t synonymous with the pay-as-you-go model. Sometimes, predictability involves making a commitment.

Not ideal for: Those teams that only need CDC from databases to warehouses and require no broader use.

8. Debezium

Best for: Those teams that have been using Kafka or need total control over the CDC stack.

Debezium is unlike the other managed products listed here because it’s open source. As such, its licensing terms are very straightforward. It’s also free, but its ecosystem is not.

A typical Debezium implementation relies on Kafka Connect:

In its own documentation, Debezium identifies Kafka Connect as its most popular configuration. For a production-ready solution, however, you also need to account for Kafka brokers, Connect workers, storage, replication, monitoring, upgrades, scaling, and sink connectors.

Debezium can also run in alternative configurations, such as Debezium Engine, which does not require any Kafka or Kafka Connect. However, as stated in documentation, the embedded lightweight version is more fragile and the application needs to take care of the state management that Kafka would normally provide.

This trade-off can be observed in community discussions as well. Those engineers, who already work with Kafka, perceive Debezium as a good CDC solution. However, others remark that Kafka operations, scalability problems, and the learning curve may prove to be a challenge. As a r/dataengineering user put it: “Debezium itself is pretty easy to operate, but Kafka consumes the lion’s share of operation effort.” There was also a thread discussing how this combination could be difficult to implement if the team planned to scale fast.

So, it’s not:

Debezium = free.

The better analogy would be:

Debezium = free; Debezium license = free; production CDC infrastructure = your responsibility.

Not ideal for: Teams without Kafka expertise or experienced operations staff that want someone else to manage their CDC.

Pricing Behavior in High-Volume Pipelines

Let’s put this into practice. Imagine the following scenario:

• PostgreSQL as a source database
• CDC continuously
• 1 TB of changed data per month
• Billions of rows of changed data
• Snowflake as the first target
• Occasional multi-terabyte backfill of historical data
• Peak periods that reach three times the normal workload

Then the team decides to add S3 as an additional destination. The value of data han’t changed just because of the pricing model.

But pricing behavior could be drastically different.

Pricing Model	Data Volume Doubles	Row Churn Doubles	Second Destination	Large Backfill	Temporary Spike
Per-GB	Direct increase	Only if bytes increase	More delivery volume	More moved data	Usage rises with bytes
MAR	Depends on active rows	Direct impact if more unique rows change	Can add MAR when another connection is required	Often treated separately/free under current Fivetran rules	Depends on unique rows modified
Events	Depends on records	Direct impact	Platform-specific	Historical loading may be treated differently	Event usage rises
Capacity	May fit existing capacity	May fit until workers saturate	May require additional capacity	Can consume more capacity	May temporarily need more capacity
Compute	Usually requires more runtime/capacity	Usually requires more processing	Additional replication work	More compute time	Autoscaling can increase usage
Self-managed	Infrastructure-dependent	Infrastructure-dependent	Requires more throughput/storage/consumers	Compute + storage + engineering	Capacity planning becomes your job

That’s because the advertised starting price rarely tells the whole story. What matters is whether you can reliably forecast the metric the pricing model uses to calculate your bill.

CDC Hidden Costs to Consider

Platform pricing is only one part of CDC expenses.

Debezium is a good example. It doesn’t have a licensing fee, but can still require Kafka brokers, Kafka Connect workers, storage, network, upgrades, and engineering.

Cloud-native services incur similar costs. Datastream could be billed by processed GiB, but destination and transformation route may require the use of BigQuery, Cloud Storage, Dataflow, and networking. And even though DMS might not incur a separate charge for its data transfer between nodes within some AWS paths, it does feature costs related to replication instances, serverless capacity, storage, and AWS infrastructure.

Don’t forget about destination costs, either. Processing increased number of changes in Snowflake, BigQuery, or other warehouse systems can mean additional compute or storage costs, even when the CDC provider itself charges only a small amount for the additional data.

And finally, engineering time. Someone needs to track replication lag, fix connectors following source database upgrade, and manage expired PostgreSQL WAL, Kafka capacity, schema changes, or backfilling failure. While a managed platform would incur these costs in a more transparent way as software billing, self-managed approach will make them a part of cloud expenses and salary.

None of these options is less expensive.

A proper cost comparison should cover the entire pipeline.

How to Calculate CDC Costs Before Tool Selection

When evaluating CDC pricing, use your own workload to estimate what you’ll actually pay.

Here are 7 metrics to start with.

1. Measure Changed Bytes

Measure the amount of CDC data that’s produced daily or monthly from the source. For PostgreSQL, it would be best to measure the WAL throughput rate, which gives you a more accurate view of the actual volume of changes being made.

Don’t use database storage volume as an estimate. A 5TB database changing 20GB per day is fundamentally different from 500GB database that is continuously changing.

2. Measure Changed Rows or Events

For MAR- or event-based pricing, calculate number of inserts/updates/deletes.

Also, track number of changed unique rows, since that’s important when calculating Monthly Active Rows.

3. Distinguish Average Volume from Peak Volume

An average of 20 MB/s doesn’t tell you much if there are also peaks of 100 MB/s every Friday.

They both matter in capacity and serverless compute pricing, so track them both.

4. Calculate the Historical Load

Calculate how much data needs to be brought in before the CDC process starts.

Check how each vendor prices the initial load and subsequent backfill. Estuary, for example, provides the free version for first-time users and a 30-day Cloud trial so that you can test historical backfills without actual production costs.

5. Count Destinations

The architecture might begin as:

But after six months, it can look like this:

Find out how each additional consumer influences the pricing model.

6. Budget for Growth

Calculate the cost of:

• Current demand
• Twice current demand
• Five times current demand

Then throw in one particularly bad month with:

• Large amount of backfilling
• Traffic burst that’s three times higher than normal
• New destination
• Some days of destination downtime followed by backlog recovery

If it’s difficult to figure out how much that costs without the vendor’s spreadsheet, that’s useful information to have before signing a contract.

7. Run the Proof of Concept Under Production-Like conditions

Just because you’ve inserted one PostgreSQL row into a destination, doesn’t mean you’re done. You need to generate enough traffic to expose actual performance.

Run a backfill test while CDC is still going. Pause the destination. Backlog data, pause the destination, then resume it to see how quickly the system will catch up. Finally, add a second destination to see what will happen to your cost and performance.

Currently Estuary’s CDC test guide recommends similar tests since there may be price, latency, and recovery differences when you start operating outside of steady state.

What Pricing Model Will Suit Your Workload?

There’s no single pricing model that’s objectively the easiest to forecast. It all depends on the metric that’s predictable in your workload.

If you can predict data volume…

Choose a volume-based model. Estuary and Datastream are good options here, especially if you can roughly estimate how many GB or GiB you will process.

If you can predict processing capacity…

A capacity-based model will likely work better. Airbyte Pro bases its pricing on data workers, not GB spent.

If you can predict row or event activity…

MAR- and event-based pricing might suit you. Fivetran is a good option when you can estimate how many unique rows will change across your connections, and Hevo is a better fit when you can reliably predict your event count.

If your organization already uses cloud compute thinking…

Then DMS will be a great fit if your platform is powered by AWS.

Similarly, Datastream can fit into your workflow if your data stack already runs on GCP.

If Kafka is an already established strategic platform…

Confluent or Debezium can work very well.

If hundreds of applications depend on Kafka, then the infrastructure is not an additional cost created by CDC. But if you have a small analytics team of five people that only needs to move PostgreSQL data into Snowflake, the cost-benefit ratio changes completely.

If your goal is to have the CDC layer managed without using Kafka…

Well, this is where Estuary really shines.

Variable cost depends on data movement, connector execution is accounted for separately, and captured data can be used across multiple materializations without requiring the production database to be the source for every downstream replay.

Conclusion

CDC pricing becomes particularly difficult when the aspect you can forecast is different from the metric you pay for. For example, if you know your data volume but pay for active rows, you may be in for a surprise. If you’ve analyzed your transaction volume but you end up paying for compute, a traffic surge could increase your bill unexpectedly. And if you choose an open-source tool because the software is free, you’ll still need to account for infrastructure and engineering costs.

That’s why the starting price shouldn’t be the deciding factor.

Test what happens with double volume, an extra destination, a backfill, or a high-traffic week. Also, take networking, destination compute, and engineering time needed to operate your system into account.

Fivetran can be predictable with stable MAR, and Hevo can be a good choice with consistent event rates. Airbyte Pro is the way to go if your processing capacity doesn’t fluctuate, and DMS and Datastream are a solid fit when architecture in cloud platforms is predetermined. Finally Confluent and Debezium are suitable options where Kafka is part of the engineering model.

However, for organizations that prefer managed CDC at a high volume but don’t need to manage a streaming infrastructure, Estuary provides a simpler solution.

Capture the data once. Measure how much data moves. Then reuse the same captured flow anywhere else it needs to go.