Quick answer: Estuary is the better tool for real-time ETL when a consumer needs database changes in seconds: it streams log-based CDC continuously, sub-100 ms in streaming mode per its figure, with a batch schedule you set per destination. Fivetran's managed product is scheduled ELT with a one-minute floor on its two top plans. Pick Fivetran when every consumer can wait for the next sync and your sources are long-tail SaaS.
The phrase "real-time" means something different at every tier of every pricing page, and the two tools in this comparison define it about as differently as two vendors can.
Fivetran's real-time is a sync frequency: the fastest cadence its managed product publishes is one minute, available on two plans, excluded for its Lite connectors and a long list of advertising sources. Estuary's real-time is a mode: change events flow from a database log to a destination as they happen, and "batch" is a schedule you attach to a destination that does not need them that fast.
A team comparing the two usually has one question underneath the search query: whether its slowest acceptable latency is minutes or seconds, and whether it wants to pay for the difference by the row or by the gigabyte.
What Fivetran does better is conceded early, because the catalog argument is not close, and the rest of the words go to the axis the title names. The sibling piece on Airbyte covers the open-core comparison; the decision guide on batch versus real-time explains how to sort your own consumers before you read either.
Estuary
Estuary is a managed data-movement platform that unifies log-based change data capture, streaming, and batch in one system. A capture reads a source and writes documents into a collection, which lives as JSON files in your own S3, GCS, or Azure bucket once you configure a storage mapping. A materialization reads that collection and writes it to a destination at whatever latency you set: as fast as possible, or on a schedule. The pricing model is per gigabyte plus per connector instance, with a free tier of ten gigabytes a month and two connector instances, and a Cloud tier at fifty cents per gigabyte plus one hundred dollars per connector instance per month for the first six and fifty for each after that. The CDC connectors for PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB are log-based and, per the documentation, in-house and purpose-built; MySQL captures are watermarkless by default, so the connector needs no write access to the source.
Two facts frame the comparison. Estuary's latency claim is sub-100 milliseconds in streaming mode, the vendor's own figure with no published methodology, and the same pipeline delivers on a batch schedule to a destination that prefers it, at no extra charge per the pricing page. And Estuary is not open source: the runtime is under the Business Source License 1.1 with an Apache 2.0 change date in 2029, while its connectors are Apache 2.0 or MIT. The vendor's own word is open-core.
Honest take: The case for Estuary here is narrow and real. When a fraud model, a support tool, or an agent needs a database change within seconds, Fivetran's one-minute floor is a ceiling, and a continuous capture with a per-destination schedule is what you want.
Fivetran
Fivetran's managed platform is commercial and proprietary: you operate nothing, and the bill is measured in Monthly Active Rows, the number of distinct rows synced per connection in a calendar month, counted once however often a row changes. A Free plan covers 500,000 MAR; Standard, Enterprise, and Business Critical are priced through an estimator, and every paid-plan connection between one and one million MAR carries a five-dollar base charge, per Fivetran's pricing guide. Fivetran also documents two ways to bring it inside your network: a Hybrid Deployment agent on Enterprise and Business Critical, and HVR 6, which its documentation describes as a self-hosted version of Fivetran.
The connector count is where Fivetran's site disagrees with itself, with 700-plus on the pricing page and 750-plus on the connectors page, and either number dwarfs the field. Since the merger with dbt Labs closed on 1 June 2026, transformations and the dbt ecosystem sit under the same roof, and activations, the reverse-ETL product, are on the same bill. For databases, Fivetran reads logs: PostgreSQL logical replication, the MySQL binary log, SQL Server CDC, MongoDB change streams, and on-premises HVA agents for Oracle, SQL Server, Db2 for i, and SAP ERP. What the managed product does not offer is streaming delivery. Syncs run on a schedule from one minute to twenty-four hours, and the one-minute option needs an Enterprise or Business Critical plan. The exception is HVR 6, whose documentation describes a capture process that continuously acquires changes at the source; it is licensed software you install and operate, which is a real path to low latency and also the reason it sits outside a comparison of managed services. The managed product is built for the warehouse-first team that measures freshness in minutes and counts its sources in the hundreds.
What "real-time" costs on each
| Fivetran | Estuary | |
|---|---|---|
| Fastest delivery | 1-minute scheduled sync, Enterprise and Business Critical only | Sub-100 ms in streaming mode, per Estuary |
| Default database cadence | Every 15 minutes | Capture is continuous; warehouse materializations default to a 30-minute sync you can set to zero or any schedule |
| Pricing unit | Monthly Active Rows per connection, plus a $5 base per paid-plan connection under 1M MAR | Gigabytes sourced, transformed, and delivered, plus $100 per connector instance |
| Free tier | 500,000 MAR | 10 GB per month, 2 connector instances |
| Durable change log | None; changes are processed in transit and land in your destination | Your own cloud bucket, once a storage mapping is configured |
| Deployment inside your network | Hybrid agent (Enterprise+), HVR 6 self-hosted | Private or BYOC data plane (Enterprise, annual contract) |
The table hides a subtlety about speed on both sides. Fivetran's own documentation for its Oracle agent says that choosing a one-minute sync frequency does not guarantee that the sync completes within one minute; a one-minute schedule is an interval between starts, and a large table or a slow API can stretch the gap. Estuary's streaming mode has no interval: the connector tails the log continuously, and a materialization set to sync as fast as possible commits transactions as they arrive. But its warehouse materializations default to a 30-minute sync schedule, per the documentation, so a team that never changes that default has bought a slower warehouse than Fivetran's fifteen minutes. The difference between the two, for a consumer that needs seconds, is that one can be set to zero and the other cannot.
Where the bill diverges
Fivetran counts a row once per month no matter how many times it changes. Estuary meters the bytes that move. Those two rules point in opposite directions depending on the shape of your data. A table of a million customer records, each updated dozens of times a day, is a million MAR on Fivetran and many gigabytes of change events on Estuary; here the per-row model favours Fivetran. A wide event table with hundreds of millions of new rows a month is hundreds of millions of MAR on Fivetran and, at fifty cents a gigabyte, a bill you can compute from the row size on Estuary; here the per-gigabyte model favours Estuary. Estuary's compare pages describe its cost as two to five times lower than the alternatives, and that figure is the vendor's own, with no methodology published, so treat it as a claim to check against your own tables rather than a fact.
The per-connector-instance fee cuts the other way. Twenty small SaaS sources on Estuary cost twenty instances a month before a byte moves, at one hundred dollars each for the first six and fifty thereafter. Twenty small SaaS sources on Fivetran are twenty five-dollar base charges plus their MAR. For a long tail of low-volume connectors, Fivetran is cheaper, and the honest recommendation is to run those there.
Which tool fits which consumer
Sort the things that read your data by how long they can wait. Nightly reports, the finance close, and model training sets tolerate hours; a scheduled sync serves them well on either tool, and Fivetran's catalog breadth and dbt integration make it the default for a warehouse-first team. A support console that shows an order the moment it is placed, a fraud check, a search index, or an agent that acts on a customer record cannot wait for the next sync, and those consumers are the reason to look at Estuary at all. Teams with both kinds of consumer tend to want one pipeline, capturing each change once into their own bucket and delivering it fast to the consumers that need speed and on a schedule to the ones that do not. Estuary's deployment options matter here too: a Private or BYOC data plane keeps that bucket and the processing inside your cloud, though both are Enterprise, annual-contract features.
Frequently asked questions
As a head of data on Fivetran Standard, what does moving to Estuary actually change?
Standard syncs every fifteen minutes. Moving buys you continuous capture from your databases, a schedule you can set per destination rather than per plan, and a change log that lives in your own bucket so a new destination backfills from it without re-reading the source. It costs you Fivetran's long-tail catalog, its dbt-native transformations, and the once-per-month row counting that makes high-churn tables cheap, plus a migration: a fresh snapshot of each moved table, a second schema in the warehouse while both run, and dbt sources repointed. Estuary's catalog covers the mainstream SaaS sources, Salesforce, HubSpot, and NetSuite among them; the gap is the long tail, and the practical path many teams take is to move the database sources that feed operational consumers and leave the rest where they are.
As the analyst who owns the data bill, is MAR or per-gigabyte cheaper for us?
It depends on the shape of your largest tables, not on the vendors. Count the distinct rows that change per month and multiply by the bytes each change carries. Tables with many updates to few rows favour Fivetran's model; tables with many new rows favour Estuary's. Add Estuary's per-instance fee for every connector you would run and Fivetran's five-dollar base charge per paid-plan connection. Fivetran publishes a pricing estimator and worked list-price examples rather than a per-MAR rate; Estuary publishes its per-gigabyte rate and a calculator on its pricing page. Volume discounts on either side are a sales conversation, so run the arithmetic on your own numbers.
As a data engineer, can we keep Fivetran for SaaS sources and add Estuary for CDC?
Yes, and it is a common arrangement. Fivetran keeps the connectors Estuary does not have and the transformation layer your analysts already use. Estuary takes the databases whose changes have operational consumers, delivers them in streaming mode where needed, and materializes to the same warehouse on a schedule for analytics. The two tools do not conflict at the destination; they land in different schemas. What you should avoid is capturing the same database twice, once per tool, because that means two replication slots, twice the decoding load on the primary, and a bill on both sides.
Which one should you pick
Pick Estuary when at least one consumer needs a database change in seconds, when you want the change log in your own bucket, or when the same capture must feed a fast consumer and a scheduled one without two pipelines; its product overview is the place to start. If you have both kinds of consumer, that last case is yours, and it is the one in which Estuary is the better tool.
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