Cloud ETL Tools Pricing Comparison: Fivetran vs Airbyte vs Confluent vs Streamkap

Your ETL bill should not require a spreadsheet to understand. And yet, for most data engineering teams, figuring out what a cloud ETL tool actually costs feels like solving a puzzle with missing pieces. One vendor charges per row, another per connector, a third per “compute unit” that nobody can clearly define, and somehow your actual invoice never matches the number from the pricing page.

This is not an accident. Complex pricing benefits the vendor because it makes comparison shopping harder and bill creep easier to hide. When every platform uses a different unit of measurement, running an honest cloud ETL tools pricing comparison becomes genuinely difficult.

We wrote this guide to fix that. Below, you will find a detailed, fair breakdown of how the leading ETL and data streaming platforms charge for their services, what the hidden costs look like in practice, and what total cost of ownership actually means when you factor in everything from infrastructure to engineering hours. We will cover Fivetran, Airbyte, Confluent, Debezium (the DIY route), and Streamkap.

Let’s make the numbers transparent.

Understanding ETL Pricing Models

Before diving into platform-specific pricing, it helps to understand the fundamentally different models vendors use. Each model has trade-offs, and the model itself often matters more than the sticker price because it determines how your costs scale.

Per-Row / Monthly Active Rows (MAR)

This model, popularized by Fivetran, charges based on the number of unique rows your pipelines touch in a given month. On the surface it sounds straightforward: move more rows, pay more. In practice, it creates several complications.

First, “active” is the operative word. If a row changes multiple times per month, it still counts as one MAR. But if your source tables have high cardinality with many unique rows that update infrequently, your MAR count can balloon even with modest data volumes. Second, different connector tiers multiply your MAR cost by different factors, so two connectors moving the same number of rows can have wildly different price tags.

The core problem: your bill is driven by how many distinct rows change, which is hard to predict and harder to control.

Per-Connector

Some platforms charge a flat or tiered fee per connector. This makes costs predictable when you have a fixed number of data sources, but it incentivizes consolidation over coverage. Need to add three new SaaS sources? That is three more line items. It also means you pay the same whether a connector moves 1GB or 1TB, which penalizes small pipelines and subsidizes large ones.

Per-Volume (GB Moved)

Volume-based pricing charges for the actual amount of data transferred through the platform. This model aligns cost directly with value: you pay for what you move. Data volumes tend to grow more predictably than row counts, making this the easiest model to forecast. It also eliminates the connector-tier complexity because a gigabyte is a gigabyte regardless of source.

Per-Compute-Unit

Platforms like Confluent use abstract compute units (CKUs, or Confluent Kafka Units) that bundle CPU, memory, and throughput. This model works well for general-purpose streaming where workloads vary, but it makes price comparison nearly impossible without benchmarking. What does one CKU actually buy you? The answer depends on your specific workload patterns.

Open-Source + Infrastructure

Tools like Debezium and self-hosted Airbyte have zero licensing costs. The catch is that “free software” is not the same as “free to run.” You still need compute infrastructure, storage, networking, monitoring, and—critically—engineering time to keep it all running. These costs are real, recurring, and often underestimated.

Platform-by-Platform Pricing Breakdown

Fivetran: MAR-Based Pricing

Fivetran is the most established name in managed ETL, and its pricing reflects both its maturity and its enterprise positioning.

How it works: Fivetran uses Monthly Active Rows (MAR) pricing, where costs scale based on the number of unique rows synced in a billing period. Connectors are tiered into free, standard, enterprise, and custom categories, with each tier carrying a different MAR multiplier.

Base costs: Fivetran’s Starter plan begins at around $1 per MAR credit per month. A mid-size deployment syncing 50 tables across standard connectors typically runs $2,000 to $5,000 per month. Enterprise connectors (Oracle, SAP, database CDC) consume more credits per MAR, pushing costs higher for those use cases.

What is included: Managed connectors, schema handling, monitoring dashboard, and basic transformations via dbt integration.

What is extra: Enterprise connectors cost more per MAR. Higher sync frequencies (below 60-minute intervals) require upgraded plans. Advanced features like private networking, column-level hashing, and governance capabilities sit behind higher-tier plans. Fivetran’s real-time CDC offerings (via HVR, which they acquired) carry premium pricing.

The real cost challenge: MAR-based pricing is difficult to predict because your bill is a function of data shape (how many unique rows change), not data volume. A table with 10 million rows where 500,000 change monthly costs less than a table with 5 million rows where all 5 million change. This makes budgeting a guessing game, especially as your data sources evolve. For a deeper analysis, see our Streamkap vs Fivetran comparison.

Airbyte: Open-Source vs Cloud Pricing

Airbyte occupies an interesting position because it offers both a free, self-hosted open-source edition and a paid cloud service. The choice between them dramatically changes your cost structure.

Airbyte Open-Source (Self-Hosted):

• License cost: Free
• Infrastructure cost: You need Kubernetes to run Airbyte at any meaningful scale. A production-grade Kubernetes cluster on AWS or GCP typically costs $500 to $2,000+ per month depending on workload. Add persistent storage, load balancers, and monitoring, and you are looking at $1,000 to $3,000 per month in infrastructure alone.
• Engineering cost: Plan for 10 to 20 hours per month of engineering time for maintenance, upgrades, troubleshooting connector issues, and monitoring. At $150 per hour fully loaded, that is $1,500 to $3,000 per month in people costs.
• What you miss: No SLA, no managed connector updates, no enterprise security features (SSO, RBAC), no official support beyond community forums.

Airbyte Cloud:

• Pricing model: Credit-based, where different connectors consume credits at different rates. Costs vary by connector, sync frequency, and data volume.
• Base costs: Usage-based pricing typically starts in the hundreds of dollars for small deployments and scales to $2,000 to $8,000+ per month for mid-size workloads.
• What is included: Managed infrastructure, connector maintenance, basic monitoring, and support.
• What is extra: Advanced features, priority support, and custom connectors come at additional cost.

The real cost challenge: The open-source option looks appealing until you factor in total cost. Between infrastructure and engineering time, self-hosted Airbyte typically costs $2,500 to $6,000+ per month—which may not be cheaper than cloud alternatives once you account for everything. Airbyte Cloud addresses this but introduces its own credit-based pricing complexity. For a detailed look, check our Streamkap vs Airbyte comparison.

Confluent: CKU-Based Pricing

Confluent Cloud is built for event streaming at scale, and its pricing reflects the complexity of running a full Kafka ecosystem.

How it works: Confluent charges based on Confluent Kafka Units (CKUs) for compute, plus separate charges for storage, connectors, ksqlDB, Schema Registry, and network egress. It is a multi-dimensional pricing model where the total bill is the sum of many moving parts.

Base costs: A single CKU in Confluent Cloud starts at around $1.50 per hour (approximately $1,100 per month). Most production workloads need at least 2 to 4 CKUs, putting the baseline at $2,200 to $4,400 per month for Kafka alone—before connectors, storage, or any processing.

What is included at base: Kafka brokers, basic monitoring, and multi-AZ availability.

What is extra—and this is a long list:

• Connectors: Fully managed Kafka Connect connectors are billed separately, typically per task per hour.
• ksqlDB: Stream processing is an additional charge based on CSUs (Confluent Streaming Units).
• Schema Registry: Charged per schema and per API call.
• Governance (Stream Governance): Data catalog, lineage, and quality features are a paid add-on.
• Network egress: Data transfer out of Confluent Cloud incurs per-GB charges that can be substantial at scale.
• Cluster linking and replication: Additional charges for multi-cluster setups.

The real cost challenge: Confluent’s modular pricing means your bill can have 6 to 10 separate line items. A mid-size CDC deployment (Kafka + connectors + basic processing + storage) typically runs $5,000 to $15,000+ per month. The platform is powerful, but it requires careful architectural planning to avoid bill shock. Our Streamkap vs Confluent comparison breaks this down further.

Debezium (DIY): Free Software, Not Free to Run

Debezium is the gold-standard open-source CDC engine. It is completely free to use, but running it in production is a different story.

License cost: $0. Debezium is Apache-licensed open-source software.

Infrastructure costs for a production deployment:

• Kafka cluster: Debezium requires Kafka for its change event log. Running Kafka in production (3 brokers minimum) costs $1,500 to $4,000+ per month on cloud infrastructure.
• Kafka Connect: Debezium runs as a Kafka Connect connector, requiring its own compute. Budget $300 to $800 per month.
• ZooKeeper or KRaft: Kafka coordination layer adds $200 to $500 per month.
• Monitoring and observability: Prometheus, Grafana, and alerting infrastructure add $200 to $500 per month.
• Storage: Kafka topic retention and log compaction require SSD storage, typically $200 to $1,000+ per month depending on retention policies.

Total infrastructure estimate: $2,400 to $6,800+ per month for a mid-size deployment.

Engineering costs:

• Initial setup: 40 to 100 hours of senior engineering time to design, deploy, and validate a production Debezium pipeline.
• Ongoing maintenance: 15 to 30 hours per month for monitoring, troubleshooting, upgrades, connector offset management, and schema change handling.
• At $150/hour fully loaded: That is $2,250 to $4,500 per month in ongoing people costs.

Total cost: A production Debezium deployment typically runs $4,650 to $11,300+ per month when you include both infrastructure and engineering time. You gain full control over every component, but you also own every operational burden. See our Streamkap vs Debezium comparison for more.

Streamkap: Volume-Based, All-Inclusive Pricing

Streamkap takes a fundamentally different approach to pricing. Instead of charging per row, per connector, or per compute unit, Streamkap charges based on the volume of data moved through the platform. No connector tiers, no add-on fees, no surprise line items.

Current pricing:

What is included at every tier:

• All connectors (no connector-tier pricing)
• Sub-second CDC latency
• Automatic schema evolution
• Monitoring and alerting
• IP safelist and SSH tunneling
• GDPR/CCPA compliance

Scale plan adds:

• Dedicated Kafka and Flink infrastructure
• Streaming transformations (SQL, Python, TypeScript)
• AWS PrivateLink
• SSO
• SOC 2 Type 2 compliance

Additional capacity: $1.50/GB beyond plan limits on the Starter plan. Free backfill included: 25GB on Starter, 1TB on Scale. Streaming transformations available at $250 per vCPU/month.

Why volume-based pricing works: Your data volume is the most predictable metric to forecast. Unlike MAR counts that fluctuate with data shape, or compute units that depend on workload patterns, gigabytes scale linearly. You can look at your current data sources, estimate monthly change volumes, and know with confidence what your Streamkap bill will be. No spreadsheets required.

Pricing Model Comparison Table

Here is a side-by-side view of how each platform charges, what you get at baseline, and where the extra costs live.

The Hidden Costs Nobody Talks About

Every pricing page shows you what a platform costs. None of them highlight what else you will end up paying for. These hidden costs can easily double your effective bill, and they tend to surprise teams 3 to 6 months after deployment.

Network Egress Charges

Cloud providers charge for data leaving their network. If your ETL tool runs in AWS and your data warehouse sits in GCP, you are paying egress fees on every byte transferred. At $0.08 to $0.12 per GB, a pipeline moving 500GB per month adds $40 to $60 in egress alone. That seems small, but multiply it across dozens of pipelines and it adds up fast.

Confluent Cloud is particularly affected by this because Kafka’s architecture involves multiple data transfers (producer to broker, broker to consumer, replication across brokers), each of which can trigger egress charges.

Governance and Compliance Add-Ons

Need data lineage? That is an add-on. Column-level security? Add-on. Audit logging beyond basics? Add-on. Many platforms gate compliance and governance features behind enterprise tiers that cost 2 to 3 times the standard plan.

For teams operating under HIPAA, SOC 2, or PCI DSS requirements, these are not optional features—they are table stakes. The gap between the “starting at” price and the price that includes everything you actually need can be massive.

Connector Upgrades and Premium Sources

Not all connectors are created equal. Database CDC connectors (Oracle, SQL Server, PostgreSQL) typically sit in a higher pricing tier than basic SaaS API connectors. On Fivetran, an enterprise connector can consume 10 to 25 times more MAR credits than a standard one for the same number of rows.

This means two pipelines that look identical on paper—same row count, same sync frequency—can cost dramatically different amounts depending on which connectors they use.

Overage Charges

Every tiered pricing model has a threshold, and crossing it triggers overage charges. These charges are almost always more expensive per unit than your base rate. On some platforms, overages can be 1.5 to 3 times the standard rate.

The worst part? Overages often hit without warning. A marketing campaign doubles your event volume for a week, or a product launch adds a burst of new rows, and suddenly your bill has a painful surprise at the end of the month.

Support Tiers

Basic support (community forums, documentation, email with 48-hour response times) is usually included. But if you need guaranteed response times, dedicated support engineers, or phone/video support for production incidents, that is a separate line item—often $500 to $5,000+ per month depending on the SLA.

When your production pipeline goes down at 2 AM, the difference between “community forum” and “24/7 on-call engineer” matters enormously.

Total Cost of Ownership Analysis

Let’s put real numbers to a real scenario. Consider a mid-size deployment with these characteristics:

• 50 tables across 3 source databases (PostgreSQL, MySQL, MongoDB)
• 100GB of monthly change volume (data actually moved)
• Sub-minute latency requirement for at least some pipelines
• Basic transformations needed (filtering, renaming, type casting)
• Compliance requirements: SOC 2, with data encryption in transit and at rest

A few things stand out in this comparison.

First, engineering time is the great equalizer. Platforms that require more hands-on management (Debezium, Confluent) accumulate significant people costs that often exceed the platform fee itself.

Second, “included” does not always mean “no additional cost.” Fivetran includes connectors but charges more for premium ones via MAR multipliers. Airbyte Cloud includes infrastructure but bakes that cost into per-credit pricing.

Third, the range between best-case and worst-case is enormous for some platforms. Confluent can cost $7,000 or $13,000+ for the same workload depending on how optimally you architect the deployment. Streamkap’s range is the tightest because volume-based pricing has fewer variables.

At the Scale tier, Streamkap delivers roughly 3x lower total cost of ownership compared to Fivetran for this representative workload, while also providing sub-second latency that Fivetran’s batch architecture cannot match at any price point.

Real-Time vs Batch: The Price of Latency

There is an important nuance that pure price comparisons miss: what you get for your money is fundamentally different depending on whether the platform delivers data in real time or in batches.

Batch ETL tools sync data on a schedule—every 5 minutes, every hour, or once a day. The data is always at least as stale as the sync interval. Streaming CDC platforms like Streamkap deliver changes within sub-second latency, creating a continuous, always-current view of your data.

This matters for pricing because:

1. Batch tools may look cheaper per row but they deliver less value per dollar. A dashboard refreshing hourly gives you 24 data points per day. A real-time stream gives you continuous currency.

2. Real-time CDC enables use cases that batch cannot serve at all. Fraud detection, live inventory management, real-time personalization, and operational alerting all require data freshness measured in seconds, not minutes or hours.

3. The cost of stale data is invisible but real. When your analytics are 6 hours behind production, decisions get made on outdated information. That missed fraud event, that oversold inventory, that customer who churned before you noticed—these have concrete dollar costs that never show up in your ETL bill.

When comparing a batch tool at $5,000 per month against a streaming platform at $1,800 per month, you are not just saving money. You are getting a fundamentally more capable data infrastructure for less.

For teams evaluating the architectural trade-offs, our platform overview covers how Streamkap’s streaming-first architecture delivers sub-second latency without the operational complexity of managing Kafka and Flink directly.

Customer Case Study: 3x Lower TCO in Practice

These numbers are not theoretical. SpotOn, a restaurant technology company processing millions of transactions, provides a concrete example of what the cost difference looks like in production.

Before Streamkap: SpotOn was using a batch-based data pipeline that could not keep up with their real-time operational requirements. Data latency, pipeline maintenance, and scaling costs were all growing problems.

After switching to Streamkap:

• 4x faster data delivery — from batch intervals to sub-second streaming CDC
• 66% cost savings — total platform costs dropped by two-thirds compared to their previous solution
• 3x lower total cost of ownership — when accounting for engineering time, infrastructure, and the platform fee combined
• Minutes to deploy — new pipelines went from weeks of engineering work to same-day deployment

The 66% cost savings came from three areas: eliminating infrastructure management overhead, reducing engineering hours spent on pipeline maintenance, and replacing per-connector premium pricing with volume-based billing.

This lines up with what we see across our customer base. Teams that switch to Streamkap from either DIY solutions or premium batch ETL tools typically see significant cost reductions while simultaneously upgrading from batch to real-time data delivery.

How to Choose: Decision Framework

Pricing is only one input to your platform decision. The right choice depends on your team’s size, technical depth, latency requirements, and growth trajectory. Here is a practical framework.

A few decision principles to guide your thinking:

If your primary use case is CDC (streaming database changes to warehouses, lakehouses, or operational systems), you should strongly consider a purpose-built CDC platform over a general-purpose streaming or batch tool. You will pay less, deploy faster, and spend less time on operations.

If you need general-purpose event streaming (microservices communication, event-driven architecture, complex event processing), Confluent or a managed Kafka service is more appropriate than a CDC-focused tool.

If batch latency is genuinely sufficient for all your use cases, batch tools will be cheaper and simpler. Just be honest about whether “hourly is fine” is actually true for every stakeholder. In our experience, teams that say they are fine with batch today often discover real-time requirements within 6 to 12 months.

If you are choosing between self-hosted open-source and a managed platform, do the honest TCO math. Include infrastructure, engineering time, opportunity cost of delayed features, and the risk cost of self-managed reliability. Free software is rarely the cheapest option.

Getting Started

If you have read this far, you are serious about understanding what your data infrastructure actually costs. Here is the direct comparison that matters most for teams evaluating their options today:

Streamkap’s Starter plan begins at $600 per month and includes all connectors, sub-second CDC latency, automatic schema evolution, and monitoring. No connector tiers, no MAR calculations, no hidden add-ons. The Scale plan at $1,800 per month adds dedicated infrastructure, streaming transformations, private networking, SSO, and SOC 2 compliance with a 99.9% SLA.

Both plans include a 30-day free trial with no credit card required. Most teams have their first production pipeline running within an hour.

For teams currently spending $5,000+ per month on Fivetran, Confluent, or a self-managed Debezium stack, switching to Streamkap typically means better latency, lower costs, and fewer engineering hours spent on pipeline plumbing.

View full pricing details or start your free trial to see the difference for yourself.