Fivetran Pitfalls: What to Know Before You Commit

Fivetran is one of the most well-known names in data pipeline automation. Its appeal lies in automation, no-code setup, and managed connectors. For many teams, this makes it a natural first choice.

However, real-world use cases often reveal critical Fivetran challenges that impact cost, flexibility, and scalability. These are not minor annoyances. For growing businesses, they can be serious roadblocks.

This guide walks you through the major Fivetran pitfalls to look out for before adopting the tool for your team.

What Are the Major Pitfalls of Fivetran?

From cost spikes to governance gaps, these Fivetran challenges can affect your budget and analytics outcomes. Let’s understand how.

1. High and unpredictable costs

Fivetran bills you depending on Monthly Active Rows (MARs). This includes every row added or updated during a billing period. Before March 2025, MAR was calculated for your entire account, with discounts applied to all connectors. This kept costs relatively lower for teams with multiple data sources. 

Now, Fivetran’s pricing has shifted from account-level to per-connection billing, giving each connector its own spend curve. This means multiple mid-volume connections can each trigger higher base charges and rates.

Many users have seen bills jump by 50% or more. For teams with evolving data needs, this shift has made an already unpredictable pricing even harder to budget. This is why many users seek Fivetran alternatives.

However, some tools help you with this. The ETL Cost Monitor app for Snowflake helps you understand which connectors, schemas, or tables are driving costs. Want to try it? Get the app now.

2. Lack of pricing transparency

Fivetran’s pricing is not just unpredictable. It also lacks transparency. Features like transformations often bring hidden charges that can sneak up on you unexpectedly.

Some users report that the rows in the source system sometimes differ from the billed rows. A single nested record in your source can turn into multiple rows in your warehouse once Fivetran normalizes it. For example, a single Shopify order can be split into four separate rows, and you pay for all four.

This mismatch makes it harder to compare Fivetran pricing with other tools. This lack of transparency is frustrating for teams looking for a quick cost breakdown. Solutions like ETL Cost Monitor give visibility into ETL spend, help teams spot inefficiencies, and prevent surprise overruns.

Pricing transparency. Fivetran is very opaque about pricing. Daily summaries of row usage by table are provided, but we often incur large charges where Fivetran is unable to explain why those charges were incurred. The lack of pricing auditability is a big issue.

Eric A.

Chief Data Officer

3. Limited data transformation capabilities

Fivetran is primarily an ELT tool. It extracts and loads raw data into your warehouse, allowing transformation there. However, it does not support complex transformations before the load like traditional ETL tools.

For anything beyond that, you must use tools like dbt Core or dbt Cloud. This creates work and tool management overhead. Plus, using dbt tools to perform complex transformations requires expertise. Teams looking for non-technical options would find this a critical limitation.

But there is an easy solution. Want more control over your transformations? Try Hevo’s ETL as a Service now!

While Fivetran is efficient, it offers limited customization options, which can be a challenge when dealing with diverse data sources and complex transformations. In our case it was Intercom. The cost of using Fivetran may be a concern for smaller projects with a limited budget.

Pavel K.

4. Customer support and reliability problems

Support experiences vary greatly. Enterprise and Business Critical customers get 24/7 coverage and priority queues with a response time of up to 1 hour. However, other users might have to wait for extended hours or even days.

Fivetran’s SLA guarantees 99.9% uptime for Core Services on all paid plans, but this uptime applies to Data Delivery Services only on higher-tier plans. However, the SLA provides service credits only, and no fast-track fixes or compensation for downtime.

In practice, users have experienced sync delays, dropped rows, and schema drift issues. The limited technical depth in first-line support and outdated documentation further slows down resolution, posing risks for mission-critical pipelines.

5. Limited flexibility and control

Fivetran’s data pipeline automation comes at the cost of granular control over your data migration processes. Sync schedules offer limited customization beyond preset intervals. You cannot sync different tables at different frequencies within a single connector.

This means high-priority tables must wait for entire sync cycles to complete. This rigidity doesn’t match real-world business requirements.

Although Fivetran offers you an option to create custom connectors, it requires significant engineering work and familiarity with Python and the Connector SDK.

API request settings now offer some control, such as setting a maximum Zendesk API usage, but most rate limits remain fixed. Since schema transformations during sync are still minimal, even simple adjustments, like renaming fields or combining columns, require more effort.

Data delivery control. While we can control database and schema names from Fivetran, we cannot change the behavior of 1 Destination to 1 Database and 1 connector to 1 schema. This has shoehorned us into an unsustainable architecture as we grow.

Eric A.

Chief Data Officer

6. Migration and vendor lock-in challenges

Fivetran automatically adjusts schemas and renames fields for compatibility. While this reduces day-to-day maintenance, these changes are not fully configurable and can drift further from your source schema over time.

This ties your downstream systems, like reports, queries, and models, to Fivetran’s version of the schema rather than the source structure.

If you ever migrate to another tool or run a parallel pipeline, you may need to rewrite large portions of your analytics layer to match the new format. This adds weeks or months to a migration project.

For companies planning long-term data migration strategies, this dependency can become a cost and time sink.

7. Limited real-time capabilities

Fivetran is primarily built for batch processing. The default sync runs every six hours, but can be lowered to 5-minute intervals for most connectors. Enterprise and Business Critical customers can go as low as one minute for supported connectors.

Change Data Capture (CDC) is also available for specific database types, such as MySQL, PostgreSQL, and Oracle. However, it still follows a micro-batch model rather than true streaming.

Even when using streaming connectors like Kafka or Kinesis, Fivetran buffers the data, syncs it in batches, and loads it at fixed intervals, not continuously.

This makes real-time use cases like fraud detection or operational dashboards difficult to support, because downstream processes cannot react instantly when new data arrives.

8. Scale and performance issues

Fivetran’s batch-based approach is effective most of the time. However, it struggles as data volume grows. While it often functions as a data migration tool, large initial loads can take several days.

Connector speeds also vary widely. For example, APIs are well-tuned while niche sources are slower and prone to errors. You can’t prioritize critical connectors during peak times. Complex data types, such as nested JSON or large text fields, can cause failures or slowdowns, and limited visibility into bottlenecks makes troubleshooting complex.

9. Poor multi-tenant environment support

Fivetran does not offer built-in support for multi-tenant SaaS models. You must create a separate connector for each tenant, which replicates data into a separate schema or database. This inflates the per-connection costs.

To consolidate tenant data, set up a SQL VIEW in your destination warehouse. This VIEW uses a UNION query to combine identical tables from each schema into a logical dataset. This workaround allows cross-tenant queries but still requires manual setup and does not cut connector costs.

10. Data governance and semantic gaps

Fivetran’s governance capabilities might feel limited to some users. It doesn’t provide built-in data cataloging, lineage tracking, or quality checking during syncs. This is a significant gap for a modern data architecture where data integrity and discoverability are crucial. Without a unified system, errors or incomplete records can enter your warehouse unnoticed.

Similarly, there is no semantic layer for defining business metrics or KPIs, so you need extra tools to make data analytics-ready. Fivetran can push metadata to external cataloging or governance systems through its Platform Connector. However, these integrations require a separate setup.

11. Limited Observability and Pipeline Visibility

Fivetran provides basic sync status dashboards, but deep pipeline observability is limited. You can see whether a sync succeeded or failed, but diagnosing 

why it failed or catching data quality issues before they reach downstream consumers is difficult. There are no native anomaly detection alerts, no row-level audit trails, and no batch-level data checks. Teams often discover data issues only after downstream dashboards or reports surface errors, by which point business decisions may already have been made on incorrect data.

For lean data teams operating in AI-driven environments where silent errors are no longer acceptable, the absence of proactive alerting and granular job-level logs is a serious liability.

12. High Total Cost of Ownership for Growing Teams

Beyond Fivetran’s direct subscription fees, the true cost of ownership is often significantly higher than anticipated. Engineering time spent building custom connectors, maintaining workarounds for multi-tenancy, integrating third-party transformation tools, managing migrations, and debugging opaque billing issues all add up.

Mid-market teams typically with lean data functions operating across data engineering, analytics, and data science, cannot afford to have their best people firefighting pipelines. As data volumes grow, Fivetran’s per-connection pricing model compounds the problem. The operational overhead, combined with escalating licensing costs, makes Fivetran a difficult tool to scale sustainably without a corresponding growth in budget and headcount.

How Does Hevo Solve These Challenges?

The market has long forced data teams into a trade-off: pay a premium for a reliable but opaque tool like Fivetran, or wrestle with unreliable, high-maintenance open-source solutions. Hevo Data is built to break that trade-off. 

With Hevo 2.0, the platform has been rebuilt from the ground up around three principles that directly address the pitfalls above: 

• Reliability: data pipelines that are set-and-forget, with guaranteed uptime and fault tolerance
• Transparency: deep visibility into every step of the pipeline with no black boxes
• Simplicity: an intuitive experience that empowers your entire team to build pipelines in minutes, not weeks

1. Predictable, Transparent Pricing

Hevo’s event-based pricing model eliminates the billing anxiety that Fivetran’s per-connection MAR model creates. There are no hidden fees, usage credits, or surprise overages. Teams can forecast data spend accurately as volumes scale, and the architecture is designed to be 40–50% lower in total cost of ownership compared to Fivetran. For a concrete benchmark: loading 201M rows from MySQL historically took Fivetran 39 hours and 49 minutes, compared to Hevo’s 1 hour and 5 minutes at 78% lower cost.

2. Built-In Observability and Proactive Alerting

Hevo addresses Fivetran’s observability gap with end-to-end pipeline visibility, granular job-level monitoring, and comprehensive logs that let you debug without contacting support. A classification engine provides proactive, pipeline-level alerts so your team catches anomalies before they reach downstream consumers, not after. Activity logs, session logs, and data lineage views give you complete confidence in what’s happening with your data at all times.

3. Flexible Transformations Without the Overhead

Unlike Fivetran’s ELT-only approach that pushes all transformation logic to dbt, Hevo supports powerful pre- and post-load transformations natively. Teams can filter, cleanse, and enrich data before it reaches the warehouse using a no-code interface, no Python SDK or specialist expertise required. For teams already using dbt, Hevo integrates seamlessly with existing dbt models.

4. Auto-Scaling Performance at Lower Cost

Hevo’s dedicated compute pipelines auto-scale up and down based on load, delivering 20x–40x faster data replication than its previous platform and consistently outperforming Fivetran across database sources like Postgres and MySQL in throughput benchmarks. This means growing data volumes are handled without downtime, manual tuning, or proportional cost increases.

5. Native Multi-Tenant Support and Governance

Hevo natively supports multi-tenant environments, allowing organizations to consolidate multiple tenants into a destination without the connector sprawl and inflated costs that Fivetran’s architecture forces. Automated schema management, advanced governance features, and 24/7 support from real data engineers ensure pipelines remain healthy and compliant as you scale.

In short, Hevo provides the infrastructure modern, mid-market data teams need: pipelines that simply work, costs that don’t surprise you, and full visibility at every stage, without requiring a growing headcount to maintain them.

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Additional resources

1. About Fivetran
2. Hevo vs Fivetran
3. Fivetran pitfalls

FAQs on Fivetran pitfalls