Last December, a friend running an e-commerce SaaS company called me up to complain. His team had been using Fivetran for about six months to pull data from a dozen sources: Shopify orders, Google Ads campaigns, Stripe payments, Zendesk tickets. Everything worked fine until March 2025, when Fivetran changed its billing structure. Instead of account-level Monthly Active Rows (MAR) discounts, they started charging per connector. His bill jumped 60% overnight.
“Same data volume, same pipelines, nothing changed on our end. The bill just doubled,” he said. His tone was calm, but I knew he was already shopping around for alternatives.
This isn’t an isolated case. Browse r/dataengineering on Reddit and you’ll find a steady stream of posts asking “Fivetran is killing our budget, what now?” On G2 reviews, “opaque pricing” and “unexpected charges” show up constantly. In January 2026, Fivetran added three new rules: a $5 minimum per connector, deletions now count toward MAR, and historical mode row updates are billable. For many mid-sized teams, that was the final straw.
Why Fivetran Got So Expensive
Let’s be clear upfront: Fivetran is a good product. Their connector library has over 750 integrations, schema handling is solid, and incremental sync reliability is industry-leading. Many teams pay the bill because it saves engineering hours they’d otherwise spend maintaining pipelines.
But the pricing model got increasingly complicated between 2025 and 2026. Calculating MAR per connector means if you have 12 SaaS data sources, you’re dealing with 12 separate pricing curves. Previously, all those sources rolled up into one total with volume discounts. Not anymore. Mid-sized teams running 5 to 15 connectors typically pay $4,000 to $8,000 per month. Enterprise deployments easily hit five figures.
For startups, it’s not that the money is unaffordable. It’s that it’s unpredictable. When data volume grows, costs grow with it, and CFOs can’t lock down a stable budget number. That’s when people start looking elsewhere.
The core needs driving the search usually fall into three buckets: save money, control costs, or own the infrastructure.
Airbyte: The Open Source First Choice
If you’ve spent any time in data engineering circles, you know Airbyte. Founded in 2020, it has over 20,000 GitHub stars and a Slack community of 27,000 members. The pitch is simple: self-host it and it’s free forever.
Airbyte’s open-source Core version runs on Docker or Kubernetes. You get access to 600+ connectors with no row limits and no connector caps. The trade-off is operations. You handle servers, backups, upgrades, and monitoring yourself.
For teams with DevOps chops, this is almost free money. A data consultant I know helps clients cut costs by migrating off Fivetran to self-hosted Airbyte. He says a 4-core, 16GB cloud instance costing about $30 to $40 per month can handle 20 to 30 connectors without breaking a sweat. Compared to Fivetran’s multi-thousand-dollar bills, the savings are orders of magnitude.
There are downsides. Community-maintained connectors vary in quality. Some niche sources might not see an update for six months. Official connectors are more stable but cover fewer data sources. Self-hosting also means you’re responsible for version upgrades and database migrations, which typically eats two to eight engineer-hours per month.
If you don’t want to run it yourself, Airbyte Cloud offers managed hosting. As of 2026, pricing is volume-based. The Standard plan starts at $10 per month, and Plus starts at $500 per month. Compared to Fivetran, you’ll usually pay significantly less for the same data volume. One benchmark test showed syncing 1 million PostgreSQL rows cost about $500 on Fivetran versus $15 on Airbyte Cloud. The gap is enormous.
Who it fits: Teams with engineering capacity who want to minimize costs should self-host. Teams that want simplicity but have tight budgets should go Cloud. Companies with data sovereignty requirements (data can’t leave certain jurisdictions) need self-hosting.
Hevo Data: For Teams That Don’t Want to Touch Code
If Airbyte is built for engineers, Hevo Data is built for “we only have one data analyst” companies.
Hevo is a Bangalore-based company focused on no-code data pipelines. The interface is clean, drag-and-drop configuration, and 150+ connectors cover the mainstream SaaS tools. Billing is event-based. Every record that flows through a pipeline counts as one event. This is way more intuitive than Fivetran’s MAR concept.
The free tier gives you 1 million events per month, enough for small teams to validate a proof of concept. The Starter plan costs about $239 per month on annual billing (5 million events). Professional is $750 per month annually and scales from 20 million to 100 million events. Compared to Fivetran at similar volumes, you typically save 30% to 50%.
Hevo’s strength is speed to value. A non-technical marketer can read the docs and have Google Ads data flowing into BigQuery within 30 minutes. It also includes basic data transformation features, so simple field mapping and filtering don’t require spinning up dbt.
The limitations are real, though. Connector count is much smaller than Fivetran or Airbyte (150 versus 750 versus 600). CDC latency bottoms out at 30 minutes, while Fivetran can hit 5 minutes. There’s no self-hosting option, so data has to flow through their cloud. For teams in regulated industries like finance or healthcare, HIPAA compliance requires the Business Critical plan, and pricing becomes opaque at that tier.
Who it fits: Small SaaS teams, e-commerce operators, marketing departments with fewer than a dozen data sources, no dedicated data engineer, and budgets in the few-hundred-dollars-per-month range.
Matillion: When Moving Data Isn’t Enough
The first three tools (Fivetran, Airbyte, Hevo) are fundamentally about moving data from point A to point B. Once it lands, you still need separate tools for cleaning, transforming, and modeling, typically something like dbt. Matillion is different. It bundles extraction, loading, and transformation together.
Matillion’s Data Productivity Cloud is a cloud-native ELT platform that does transformations directly in your warehouse. Instead of pulling data out, processing it, and pushing it back in, it pushes transformation logic into Snowflake, BigQuery, or Databricks compute engines. This design leverages the warehouse’s own processing power, which scales well for large datasets.
It offers a visual pipeline designer with low-code drag-and-drop operations, plus support for SQL and Python. The 150+ prebuilt connectors, along with a REST API generic connector, cover most use cases.
But Matillion is expensive. There’s no free tier, no public pricing page, and all quotes come through sales. Third-party estimates from July 2026 suggest small teams pay $20,000 to $35,000 annually, and enterprise contracts run $100,000 to $300,000+. The median contract size is about $139,000 per year. And that’s just for the Matillion platform. Your warehouse compute costs are separate.
That’s why Matillion typically shows up on shortlists for mid-to-large enterprises. If your team is already running Snowflake or Databricks and executing heavy daily transformation workloads, Matillion can consolidate scattered scripts into visual workflows. But if you just want to sync a few SaaS sources into a warehouse for dashboards, it’s overkill.
Who it fits: Data teams with 5+ people, Snowflake/BigQuery/Databricks as the core warehouse, complex transformation logic, and comfortable budgets at mid-to-large enterprises.
Rivery: Flexible But You Need to Do the Math
Rivery is an Israeli-built SaaS ELT tool that was acquired by Boomi in late 2024. Post-acquisition, the product continues to operate independently. The billing unit changed from “credits” to “BDU” (Boomi Data Units), but the logic stayed the same.
The pricing model is credit-consumption-based. The Base plan charges $0.90 per BDU, with unlimited connections and unlimited source-target combinations. This sounds flexible, but how much you actually spend depends on pipeline types and execution frequency. Database CDC sync, API calls, Python transformations, and orchestration tasks all consume different amounts of BDUs. If you have many API sources running on high-frequency syncs, costs can climb faster than expected.
Rivery’s advantage is workflow orchestration capability. It’s not just about moving data. You can add logic steps, version control, and CI/CD integration into pipelines. For teams needing complex data orchestration, it offers a layer beyond pure ELT tools.
But it has similar issues to Matillion. Advanced features like SSO, PrivateLink, and VPN are locked behind higher-tier plans. Python execution is separately billed. Sub-15-minute API syncs cost extra. The Base plan caps sync frequency at 60 minutes.
Who it fits: Teams that need flexible orchestration, have diverse data source types, and are willing to invest time understanding credit consumption patterns. Best for mid-sized technical teams.
Side-by-Side Comparison: See the Differences at a Glance
| Dimension | Fivetran | Airbyte (self-hosted) | Airbyte Cloud | Hevo Data | Matillion | Rivery |
|---|---|---|---|---|---|---|
| Billing model | MAR per row | Free | Volume-based | Event count | Credit/task time | BDU credits |
| Starting monthly cost | ~$500+ | $0 (ops on you) | $10 | $0 (free tier) | ~$1000 | Usage-based |
| Connector count | 750+ | 600+ | 600+ | 150+ | 150+ | 200+ |
| Self-hosting | No | Yes | No | No | No | No |
| Built-in transformation | Limited (dbt integration) | None (external) | None (external) | Basic | Strong (core feature) | Moderate |
| Fastest sync | 5 minutes | Config-dependent | Hourly/daily | 30 minutes | Schedule-dependent | 5-60 minutes |
| Best fit scale | Mid-to-large | Small to large | Small to mid | Small | Mid-to-large | Mid-sized |
This table gives you a rough direction, but real selection depends on your specific situation. What data sources do you have? How fast do syncs need to run? Does your team have engineering capacity? What’s your budget ceiling?
How to Choose: Three Typical Scenarios
Scenario one: Five-person startup, monthly budget under $500
You have three to five SaaS data sources and want to pipe everything into BigQuery for dashboards. Someone on the team knows SQL, but there’s no dedicated DevOps person.
Go with Hevo Data’s Starter plan or Airbyte Cloud Standard. Hevo is faster to set up. Airbyte Cloud is cheaper. If your data volume is modest (under 5 million events), Hevo’s free tier might be enough.
Scenario two: Ten-person data team with DevOps capacity, goal is minimize costs
You already run a Kubernetes cluster. Engineers can handle operations. You’ve got a dozen data sources generating tens of millions of row changes per month.
Self-host Airbyte. Software costs are basically zero. The thousands you save will more than cover a solid server and occasional maintenance hours. If certain connectors aren’t stable enough, you can write your own using Airbyte’s CDK.
Scenario three: Enterprise data platform centered on Snowflake with complex transformations
You’re not just moving data. You need extensive modeling, cleaning, and aggregation inside the warehouse. Team size is 10+ with dedicated data engineers and analytics engineers.
Matillion is worth serious evaluation. Its visual transformation designer lowers collaboration friction, and warehouse-native execution ensures performance. The cost is high, but for a team at this scale, the engineering time saved might justify it. Alternatively, consider the classic combo of Fivetran for extraction plus dbt for transformation, but that means two systems and two bills.
Don’t Forget Migration Costs
One thing many people overlook: migrating from Fivetran to any alternative has a cost.
Typical migration timelines run two to eight weeks. You need to reconfigure connectors, validate data consistency, and adjust downstream dependencies like dashboards, models, and alerts. If you’re running dozens of pipelines, this isn’t a weekend project.
So when making the decision, don’t just compare monthly bill differences. Factor in the engineering time for migration and calculate how long it takes to break even. Generally speaking, if the alternative saves you $1,000 to $2,000+ per month, you’ll recover the investment in three to six months. If the savings are smaller, it might not be worth the hassle.
There’s also a middle-ground strategy: build new pipelines on the alternative tool and gradually migrate old ones off Fivetran. This minimizes risk but means maintaining two systems for a while.
The Bottom Line
Choosing a data integration tool has never been about finding “the best” tool in absolute terms. It’s a balancing act between team capabilities, budget constraints, data scale, and compliance requirements. Fivetran remains the option with the most connectors and the least operational overhead. It’s just that the bills have gotten harder to swallow.
The good news is that alternatives in 2026 are mature enough to compete seriously. Whether you want open-source freedom, low-cost managed hosting, or an all-in-one platform, there are solid options available. The key is figuring out what problem you’re actually trying to solve, then matching it to the right tool.
Stay updated with our latest AI insights
