Artie vs. Confluent

Kafka isn't a database replication product. Artie is. And it runs on Kafkaso you don't have to.

Factor

Confluent

Artie

Built for

General-purpose Kafka streaming platform

Real-time database replication + event streaming

Architecture

Debezium → Kafka topics → Schema Registry → sink connector → warehouse (DIY composition)

Managed end-to-end CDC, Kafka-backed under the hood

Event streaming for app consumers

First-class — many producers, many consumers, durable replay, multi-region

DB-fed events API — sub-minute, schema-aware, no Kafka to operate

Producer model

Any system: apps, services, IoT, batch jobs via Kafka clients

Database-sourced via CDC + events API

Stream processing

Flink, ksqlDB, Kafka Streams

Not in scope — pair Artie with dbt / Snowflake / Databricks downstream

Replication latency

Seconds, with tuning of partitions, batch sizes, and sink flush intervals

Seconds, out of the box

Time to first event

Days to weeks (cluster, connectors, Schema Registry, sinks, merge)

Minutes

CDC approach

Debezium-based source connectors; customer composes the pipeline

Native, purpose-built for DB → warehouse

Pricing model

Multi-axis: cluster (eCKU/CKU) + storage + ingress/egress + per-task or per-throughput connectors + Schema Registry + Stream Governance

Growth: plans from $500/mo, based on monthly usage 
Enterprise: fixed, predictable pricing

Backfill cost

Counts toward connector throughput, Kafka storage, and egress

Free

Cost predictability

Hard to forecast; cross-AZ and cross-region egress especially compound

Predictable, contracted volume tier

Cost optimization controls

Topic retention, partition tuning, connector task scaling

Per-table replication frequency tuning (Eco Mode)

Schema evolution

Schema Registry compatibility rules; sink connector behavior varies

Auto-detected DDL, deletes, and type changes

Warehouse merge logic

Customer's responsibility (sink connector or downstream Spark/dbt job)

Built-in, optimized MERGE per destination

Backfill behavior

Debezium snapshot floods topics, all sinks re-process

Online, parallel with CDC, replica-aware

Failure recovery

Kafka offset replay; sink-side idempotency is your job

Kafka offset replay, exactly-once delivery end-to-end

Impact on source DB

Reads from primary or replica based on connector config

Replica-aware by default; primary supported when needed

Sharded / multi-tenant fan-in

Bespoke topic and connector design per shard

Many-to-one fan-in 1,000s of shards → unified schema

Observability

Cluster, connector, and topic metrics; per-table lag is a custom dashboard

Per-table lag, throughput, and alerting via Datadog/PagerDuty

PII controls

SMTs (Single Message Transforms) configured per connector

Column include/exclude/hashing on all plans

Deployment options

Confluent Cloud, Confluent Platform (self-hosted), or BYOC via private networking

Cloud or BYOC (your VPC) or air-gapped on-premises

Enterprise compliance

SOC 2 Type II, ISO 27001, PCI DSS, HIPAA on Confluent Cloud

SOC 2 Type II, HIPAA

Connector breadth

120+ Kafka connectors across many systems

Focused: 9+ database sources, 14+ destinations (incl. event streaming)

Where Artie wins

Vertically-integrated, not just a substrate

Confluent gives you Kafka and the connectors. You wire them up. Artie owns the source CDC, schema evolution, warehouse-optimized merge, and observability – same Kafka durability, none of the assembly.

Real-time data and event streaming, both done deeply

Sub-minute replication into Snowflake, BigQuery, Redshift, Databricks, and Iceberg. And when downstream consumers need a stream, Artie ships event streaming destinations without standing up a parallel Kafka platform.

Pricing without the multi-axis surprise

Use Artie beginning at $500/mo. Backfills are always free. No CKUs, no per-GB egress math, no per-task connector charges, no Schema Registry add-on.

Deploy where your data lives

Artie Cloud or BYOC in your VPC (Enterprise) – same product, same UX. SOC 2 Type II and column-level PII controls (include, exclude, hash) on every plan. HIPAA-ready for regulated industries.

Start for Free

Book a Demo

Switching from Confluent to Artie takes hours, not weeks

What stays the same

The cutover playbook

Your warehouse: Snowflake, BigQuery, Redshift, Databricks, Iceberg
Your destination schemas: same tables, same columns
Your dbt models, dashboards, and BI tools: keep working unchanged
Your existing Kafka use cases: keep them in Confluent. Artie only replaces the database-fed pipelines.

Run Artie in parallel with your Debezium / Kafka / sink-connector pipeline. Both write to the same warehouse without conflict.
Compare row counts, latency, and correctness side by side.
Cut over downstream views or dbt models to read from Artie tables.
Decommission the Debezium source connectors, CDC topics, sink connectors, and Schema Registry rules tied to that pipeline.

Frequently asked questions

Artie vs. Confluent

Kafka isn't a database replication product. Artie is. And it runs on Kafkaso you don't have to.

Where Artie wins

Vertically-integrated, not just a substrate

Real-time data and event streaming, both done deeply

Pricing without the multi-axis surprise

Deploy where your data lives

Switching from Confluent to Artie takes hours, not weeks

What stays the same

The cutover playbook

Is Artie a direct replacement for Confluent?

We use Kafka for our microservices. Can we keep that and just move the CDC pipeline to Artie?

Doesn't Artie use Kafka under the hood? What's actually different?

What's the migration risk?

Will switching tools break our dbt models, dashboards, or BI tools?

We're in a regulated industry. Does Artie support HIPAA or BYOC?

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