Key TakeawaysThe first step, if you are looking to migrate from Fivetran to another ETL tool, is to document your existing Fivetran setup. This includes mapping your sources, destinations, tables, schemas, transformation logic, and other configurations.
- Once that is done, make sure the ETL tool you are moving to supports the sources and destinations you are currently using.
- Once you have confirmed that, start testing the new platform. Create the required sources and connectors in the new tool.
- Once you have confirmed that everything is working as expected, start migrating the connectors gradually. Start with the less critical connectors and make sure there are no errors before moving on to the more critical ones.
Once all the connectors have been migrated, run both platforms in parallel for some time to verify that the data is accurate and consistent. If everything is working as expected, you can then cut off Fivetran and complete the migration to the new ETL tool.
Moving off a core ingestion tool like Fivetran can feel like open-heart surgery on your data pipelines and downstream reporting. Pipelines can break, timelines can stretch, and the reports your business relies on can go stale.
Still, many teams are making the move. In recent conversations with our sales team, companies looking to leave Fivetran keep giving the same reason: unpredictable MAR pricing.
The hard part is that most Fivetran users are mid-market or enterprise companies with dozens of connectors and established pipelines. The platform you move to has to support every one of those connectors. It also has to replicate the configurations, transformation logic, and schema handling you already have, so the data reaching your destination stays consistent with the data already there.
This guide lays out the steps to make that switch without downtime, based on what we’ve learned helping teams move off Fivetran.
Table of Contents
Why Are Teams Moving Away From Fivetran?
1. Pricing Unpredictability of Fivetran
Since Fivetran introduced its MAR-based pricing model in March 2025, estimating the monthly spend based on data volume usage has become more difficult.
Even though Fivetran states that the effective cost per MAR reduces as data volume increases, when we tested this across different MAR volumes, we found that the overall cost continued to increase significantly as data volume increased.
To add to the bill, Fivetran has also introduced a $5 base fee for each connection. Since mid-market and enterprise companies usually run dozens of small connections, the costs quickly add up.
Transformations and activation are also billed separately, which, based on our analysis, can account for more than 70% of the overall cost in some scenarios.
With multiple pricing components involved, companies need to account for MAR usage, connections, transformations, and activation when estimating their monthly Fivetran spend.
2. Support Delays
Fivetran’s support is divided into different levels based on the plan.
The Free plan includes documentation and email support, with response times of up to 48 hours. The Standard plan reduces the response time to between 4 and 24 hours.
Both plans include Fivetran’s Core Service SLA. This assures the availability of the Fivetran platform, but does not guarantee that data from your connectors reaches the destination as intended.
Though Fivetran offers a Data Delivery SLA that guarantees 99.9% data delivery for eligible, properly configured connectors, along with a one-hour support response time, the catch is that both are reserved for its higher-tier plans.
Multiple customer conversations show how difficult it can be to manage pipeline issues on lower support tiers. In some cases, customers have had to build an additional pipeline to keep their data up to date while the original issue remained unresolved.
3. Limited Visibility Into Pipeline Issues
Troubleshooting a pipeline issue can become difficult when you have limited visibility into what is happening across the source, connector, transformations, and destination.
Issues such as schema changes, authentication failures, source API problems, or failed syncs can require teams to investigate different parts of the pipeline to identify the cause.
For companies managing a large number of connectors, this can increase the time required to identify and resolve pipeline issues, especially when combined with difficulty reaching the Fivetran support team.
Why is Hevodata the Best Fivetran Alternative?
Two of the major challenges we have seen from companies looking for an alternative to Fivetran are unpredictable pricing and limited support.
Hevo addresses these with fixed pricing for unlimited data volume, agentic monitoring, and 24/7 human support. Support is also available across all paid plans without requiring you to move to a higher tier to access support-related features.
The next challenge is safely migrating your existing Fivetran pipelines to the new ETL platform. As discussed above, Hevo simplifies this through Fivetran-parity connectors, the option to continue from where your Fivetran pipeline was paused, and dedicated migration support.
Here is what you get when moving from Fivetran to Hevo:
The option to run three critical pipelines in parallel for free for the first three months, allowing you to validate the setup before completely moving away from Fivetran.
- Fixed pricing with unlimited data volume.
- Agentic monitoring that automatically raises 98% of support tickets when pipeline issues are identified, backed by 24/7 human support.
- No support-tier gating across paid plans.
- Fivetran-parity connectors that allow you to continue using your existing schema and transformation logic.
- Dedicated migration support to help with the migration end-to-end.
- The option to run three critical pipelines in parallel for free for the first three months, allowing you to validate the setup before completely moving away from Fivetran.
“It only took us three days to move from Fivetran, which we used for 5-6 months, to Hevo. It was as simple as reconfiguring the settings, and Hevo was all set to show its capabilities in action”
What Factors Affect Fivetran Migration Effort?
The effort required to migrate away from Fivetran depends on your existing data setup. A company with a few simple pipelines can migrate relatively quickly, while an environment with multiple connectors, transformations, and downstream dependencies will require more planning and testing.
1. Connector Coverage
Start by checking whether the new ETL platform supports all the sources and destinations you currently use with Fivetran.
For example, if you have 20 Fivetran connectors and the new platform supports all 20 natively, you can recreate those pipelines directly. If some connectors are not supported, you may need to use a REST API or build a custom connector, which adds more work to the migration.
The number of connectors also matters. Each connector needs to be configured, tested, and validated before it can replace the corresponding Fivetran pipeline.
2. Volume of Historical Data
The migration effort will depend on whether you plan to reload your historical data or continue from where the existing Fivetran pipeline stops.
A full historical backfill can take significantly more time for companies with large datasets. It can also increase the compute required at the destination, especially when loading large tables into a data warehouse such as Snowflake.
If the historical data already loaded by Fivetran can remain in the destination, the migration becomes simpler. You can pause the Fivetran pipeline at a defined point and configure the new pipeline to resume loading data from that point.
3. Schema and Table Mapping
Check how Fivetran currently creates schemas, tables, columns, and data types in your destination.
Your dashboards, reports, SQL queries, BI tools, and other downstream systems may already depend on this structure. Changing table names, column names, or data types during migration can require changes across these downstream systems.
If the new ETL platform can maintain the same output schema as Fivetran, much of this additional work can be avoided.
You should still verify the schema for each migrated connector. Small differences in data types, timestamps, naming conventions, or the handling of specific fields can affect downstream queries and reports.
4. Pipeline Configuration
Each Fivetran pipeline may have its own configuration that needs to be recreated in the new platform.
This can include sync frequency, selected tables and columns, filters, error handling, alerts, data masking, and other connector-specific settings.
A standard pipeline with minimal configuration will require less work to recreate. Companies with heavily customized pipelines will need to document and replicate these settings before switching.
5. Complexity of Transformations
If you are using Fivetran’s transformation capabilities or its dbt integration, document the existing transformation logic before starting the migration.
Simple pipelines that move data directly from the source to the destination require less migration work. Pipelines with multiple transformations will require additional time to recreate and test that logic in the new environment.
After recreating the transformations, compare the output from both pipelines to confirm that the transformed data reaching the destination remains consistent.
Why Is Migrating from Fivetran to Hevo Easier?
1. Full Connector Coverage from Day One
Hevo provides 150+ pre-built connectors covering the most critical data sources.
If a connector you need is not available, Hevo has a dedicated SWAT team that can build custom connectors. You can also use Connect AI, Hevo’s connector builder agent, to build REST API-based connectors that are ready to use within two hours.
2. Hevo replicates the exact schema of Fivetran
Hevo’s connectors allow you to continue using the same schema that Fivetran was producing in your destination.
Meaning, you can pause your Fivetran pipeline and configure Hevo to start loading data from that point. This allows you to skip reloading the historical data that Fivetran has already moved to your destination, reducing the overall complexity.
If you want to continue with your existing Fivetran schema and transformation logic, Hevo can maintain the same setup. No schema or table remapping is required, and you do not have to rewrite your existing transformations or dashboards.
This reduces the migration effort associated with historical data, schema mapping, transformations, and downstream dependencies.
3. White-Glove Onboarding With Hevo’s Migration Squad
Hevo provides a dedicated migration squad to support the migration end-to-end.
Our team works with you through the setup and migration process, reducing the amount of pipeline configuration you need to manage on your own.
How to Plan a Smooth Migration
Data migration without a plan means downtime, lost data, and broken dashboards. Here’s how you can plan a smooth migration from Fivetran.
1. Audit your current setup
Before you evaluate a new setup, understand the existing one. Start by listing every data source connected through Fivetran, including databases, SaaS apps, and custom integrations. Note dependencies such as dashboards, reports, or workflows that rely on this data.
Document any custom scripts or downstream applications that use Fivetran’s API for tasks like monitoring or orchestrating syncs. They might need to be rewritten to fit the new platform.
2. Set success criteria
Decide what your goals are with this migration. Keep your success criteria as detailed as possible. If your target is cost reduction, include precise dollar amounts and defined timelines for savings. Define measurable data quality goals to maintain stakeholder trust.
3. Choose the right alternative
Decide which data migration tool will replace Fivetran. Analyze your team’s technical skills, budget, and operational needs. Compare the total cost of ownership over several years for data pipeline automation, factoring in licensing, infrastructure, and staffing.
How to Design Your Migration Strategy
1. Phased migration strategy
Migrating everything at once is risky. Instead, start small with low-risk, non-critical connectors and test how the platform handles your data. Once you are confident that things are running smoothly, gradually migrate higher-priority sources.
2. Parallel validation framework
Use both Fivetran and your new tool simultaneously for a period. This process provides a clear comparison of the functions and also gives your employees time to understand and implement the tool.
How to Execute the Migration
Phase 1: Environment setup and testing
Create development and staging environments that replicate your production setup in Fivetran. Test the tool’s capability with data that has real-world complexity, including anomalies and edge cases. Automate data quality checks to compare source and destination systems.
Phase 2: Pilot migration
Start with two or three low-risk source connections in the new platform. Perform complete historical data imports and allow incremental syncs for up-to-date data. Validate data accuracy with row counts, checksums, and business rules.
Phase 3: Full migration rollout
After the pilot proves successful, start moving the rest of your connectors in batches, beginning with the most critical ones. Keep both systems running simultaneously to address issues without disrupting operations. Plan the final switch during a low-traffic maintenance window.
Post-Migration Optimization
1. Immediate validation
Run smoke tests across all downstream systems. Check that dashboards load properly and refresh on schedule. Ensure automated reports are still generated and API connections are delivering accurate data without interruptions.
2. Ongoing operations
Track latency, error rates, and data quality to address issues before they cause outages. Keep an eye on resource usage and review performance regularly to understand where you can cut costs without sacrificing speed or reliability.
3. Decommissioning Fivetran
Do not pull the plug on Fivetran immediately. Wait until the new system has run smoothly for at least two to four weeks. Archive your Fivetran configurations and related documentation. Phase out your connectors gradually to provide a safety net.
Why Consider Hevo Data as Your Fivetran Alternative?
If you’re migrating from Fivetran in search of better prices, flexible transformations, or real-time syncs, Hevo makes a strong case. When it comes to the Hevo vs Fivetran debate, a key differentiator is cost. With pricing starting at $239/month, Hevo uses a clear, event-based model that scales smoothly with your business.
Hevo’s no-code interface makes launching pipelines simple, with 150+ pre-built connectors ready to go. It supports both ETL and ELT solutions, giving you the freedom to transform data pre- and post-load. With 4.3+ ratings on G2, Gartner, and Capterra, Hevo backs its reputation with efficient error handling and automatic schema drift management.
FAQs
Q1. How do I migrate from Fivetran to another ETL tool?
Start by auditing your current setup by listing all sources. Set clear success criteria for cost, performance, and data quality. Choose the right alternative based on your team’s skills, budget, and feature requirements.
Then follow a phased migration, starting with low-risk connectors. Run both tools in parallel and slowly roll out your entire ecosystem in batches, before fully decommissioning Fivetran.
Q2. How long does it take to completely migrate from Fivetran?
Migration timelines typically range from 4 to 12 weeks, depending on data complexity and connector volume. In any case, once all your data has been migrated to the alternative tool, it’s a good idea to have Fivetran as a backup and ensure stable operations for at least 2 to 4 weeks.
Q3. What are the roadblocks to migrating from Fivetran?
Migrating from Fivetran has potential challenges, like data loss, schema mismatches, downtime, and failed API connections. You may also need to script certain customizations that previously relied on Fivetran’s API to work in the new tool. Without careful planning and thorough testing, these issues can disrupt your daily operations.
Q4. How can I avoid data loss or downtime during a Fivetran migration?
Run both Fivetran and the new ETL tool in parallel until all data is validated. Use incremental syncs, automate data quality checks, and monitor latency, error rates, and row counts. Plan final cutovers during low-traffic periods to minimize disruptions.
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