Fivetran Alternatives With Better Postgres Support: What Data Engineers Need to Know

If you are running a modern data stack, chances are high that PostgreSQL is your primary transactional database and Fivetran is moving that data into your warehouse. For a long time, that combination was the default choice. You connected the two, paid the bill, and got back to building.

That is no longer the case for a lot of teams.

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Data engineers are increasingly hitting a wall when scaling Fivetran with Postgres. Stuck replication slots , write-ahead log (WAL) bloat, and Fivetran's 2025 and 2026 pricing updates have turned a once-reliable setup into a source of constant firefighting. Teams are spending hours debugging broken pipelines and explaining unexpected bill spikes to their CFOs instead of shipping.

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If your Postgres replication is failing or costing too much, here is exactly what is going wrong and what a better setup looks like.

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Why Fivetran Struggles with Postgres

Fivetran is a large platform with hundreds of connectors, but its architecture is fundamentally batch-based. When you try to push high-volume Postgres Change Data Capture (CDC) through a batch-based ELT tool, the cracks start to show quickly.

1. Stuck Replication Slots and WAL Bloat

Fivetran uses Postgres logical replication slots to read the WAL . Because it pulls data in batches, typically every 15 minutes on Standard plans, rather than streaming continuously, the replication slot must hold onto the WAL until Fivetran successfully fetches it.

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If Fivetran hits a delay, a timeout, or a processing error, that slot gets stuck. The WAL cannot be truncated, so it bloats rapidly . On high-transaction databases, this can fill up your disk space and bring down your production database entirely. Recovering from a stuck slot is not a quick fix either. It often means pausing the connector, dropping the slot, waiting for a checkpoint, and triggering a full historical resync .

2. Unpredictable Costs from History Mode and Deletes

Fivetran prices based on Monthly Active Rows (MAR). In 2025, Fivetran moved to per-connector billing, which eliminated account-wide volume discounts. Since January 2026, deletes count as billable MAR, and if you use history mode to track row changes, every update to a row counts toward your paid MAR.

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For high-velocity Postgres tables, this causes bills to spike in ways that are very difficult to predict or control. A single high-churn table can quietly generate millions of additional MAR per month, and you often do not find out until the invoice arrives.

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3. Slow Sync Speeds Without an Expensive Upgrade

Fivetran's Standard tier caps you at 15-minute syncs. Getting down to 1-minute syncs requires upgrading to their Enterprise tier, which comes with a significant price jump. For teams that need near real-time operational analytics, a 15-minute batch delay simply does not cut it. And paying Enterprise rates just to get closer to real time, while still dealing with the WAL and cost issues above, is a hard case to make internally.

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4. The Support Gap

When things go wrong with Postgres replication, you need answers fast. Fivetran's support response times are a consistent complaint from data engineering teams. Debugging a stuck replication slot at 2am while your production pipeline is down is not the time to be waiting on a ticket queue.

What Better Postgres Support Actually Looks Like

The problems above are not inevitable. They are the result of a specific architectural approach, and a different approach solves them.

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Real-time CDC over batch. A platform that reads the Postgres WAL continuously, rather than in batch intervals, eliminates the replication slot bloat problem at the root. The WAL is consumed as it is written, so there is nothing to accumulate and no slot to get stuck.

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Parallel syncs. High-volume Postgres tables do not have to be a bottleneck. Parallel sync architecture processes multiple streams simultaneously, dramatically cutting sync times without adding pressure to your source database.

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Predictable pricing. Monthly Active Row billing should not be a surprise. A pricing model that does not penalise you for deletes, history mode, or having multiple connectors gives you the budget certainty that Fivetran's current model does not.

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Built-in observability. Schema changes in Postgres should not silently break downstream pipelines. Monitoring at the source, with alerts before bad data reaches the warehouse, is the difference between catching a problem in seconds and discovering it in a stakeholder meeting.

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Fast support. Five-minute support response times are not a marketing claim. They are the difference between a minor incident and a major outage.

Why Data Teams Are Switching to Matia

Matia was built by data engineers who were tired of exactly these problems. It is a unified DataOps platform that combines ETL, Reverse ETL, Observability, and Data Catalog in one place, with Postgres support that is built for speed and reliability from the ground up.

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For Postgres specifically, Matia delivers:

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• Parallel syncs that reduce replication time by up to 28x compared to batch-based tools
• Real-time CDC that eliminates WAL bloat and replication slot failures
• Source-level observability that catches schema changes and anomalies before they hit the warehouse, with instant Slack alerts
• Pricing built on Monthly Active Rows, without the per-connector penalties, delete charges, or history mode surprises that inflate Fivetran bills. Customers report up to a 61% reduction in total data stack spend.
• A dedicated Slack channel on every plan with 5-minute support response times

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Curious what this looks like on your own Postgres setup? Book a demo and we'll walk through your replication slots, sync times, and current MAR spend together.

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And if you are currently on Fivetran, the migration is not a months-long rebuild. Matia is fully backwards compatible with Fivetran. Pipelines and configs migrate in days, not months.

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Ramp reduced their sync time by more than 80% after switching. Lopay cut their data costs by 40%. Obligo reduced warehouse costs by 83%. These are not edge cases. They are what happens when you stop forcing Postgres through a tool that was not designed for it.

The Bottom Line

Debugging Postgres replication issues is exhausting. You should not have to spend your week babysitting replication slots or explaining to finance why your data bill doubled.

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If Fivetran is slowing you down, costing too much, or simply not keeping up with your Postgres workload, there is a better option. Matia gives you faster syncs, lower costs, and the kind of support that actually shows up when something breaks.

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Book a demo today to see how fast you can migrate your Postgres pipelines.

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