Sequin delivers industry‑leading change‑data‑capture (CDC) performance, sustaining 50k ops/s (or up to 40 MB/s) at a 55 ms average end‑to‑end latency. That makes Sequin the fastest Postgres CDC solution available today.

This performance is achieved with Sequin streaming changes from a single Postgres replication slot to Kafka. If configured with multiple replication slots, Sequin can stream low latency change-data-capture from very high throughput Postgres databases without falling behind. In the sections below, we document how Sequin performs on different instance sizes. We also compare Sequin to different Debezium runtimes (Debezium has several runtimes.)

Sequin performance

Single‑slot peak throughput & latency

Sequin streams from a single Postgres replication slot to Kafka at:

50k ops/s or 40 MB/s (whichever comes first), sustained
55ms average latency
253ms 99th‑percentile latency

If you need more headroom, you can enable additional slots; Sequin scales linearly without falling behind.

Sequin on different instance sizes

Here’s a sampling of throughput Sequin can handle on different instance types:

AWS Graviton EC2	vCPU / GiB	Avg row size	Sustained throughput	Bandwidth
c8g.4xlarge	16 / 32	100B	60k ops/s	5.53 MB/s
c8g.4xlarge	16 / 32	200B	58k ops/s	10.64 MB/s
c8g.4xlarge	16 / 32	400B	52k ops/s	19.92 MB/s
c8g.4xlarge	16 / 32	1.6kb	23k ops/s	36.06 MB/s
c8g.2xlarge	8 / 16	200B	34k ops/s	6.32 MB/s
c8g.xlarge	4 / 8	200B	20k ops/s	3.81 MB/s

Debezium on MSK Connect (AWS)

Best if: you already run Amazon MSK and want a managed Kafka Connect runtime. The highest throughput we were able to consistently achieve with Debezium deployed on AWS MSK Connect was 6k ops/sec.

Metric	8 MCUs (max available on AWS MSK Connect)
Sustained throughput	6k ops/s
Average latency	≈ 258ms
99th‑percentile latency	≈ 499ms

At this throughput, Sequin in a comparable setup has a latency that is significantly lower:

Scaling limits

8 MCUs (8 vCPU / 32 GiB RAM) is the hard ceiling MSK Connect exposes for a single connector.
The connector is single‑threaded for snapshotting and heavily synchronized during streaming, so adding more MCUs has diminishing returns past ~4 MCUs.

Debezium Server (stand‑alone)

Best if: you need a minimal footprint and do not want to run full Kafka Connect.

Hardware	Row size	Debezium Server	Sequin
16 vCPU / 32 GiB	200 B	20k ops/s (4 MB/s)	58k ops/s (10.64 MB/s)
16 vCPU / 32 GiB	400 B	20.3k ops/s (7.73 MB/s)	52k ops/s (19.92 MB/s)
8 vCPU / 16 GiB	100 B	20k ops/s (2 MB/s)	30k ops/s (3 MB/s)
4 vCPU / 8 GiB	100 B	20k ops/s (2 MB/s)	29k ops/s (2.9 MB/s)

Debezium on Confluent Cloud

(Coming soon)

Comparative benchmarks coming soon. Upvote the issue if you want to see them.

Debezium on self‑hosted Kafka Connect

(Coming soon)

Comparative benchmarks coming soon. Upvote the issue if you want to see them.

Benchmark methodology

All of our benchmarks are open source and available on GitHub.

Our benchmarks are conducted in a production-like environment. Sequin and Debezium are compared head-to-head capturing changes from AWS RDS and delivering to AWS MSK Kafka. Load is applied to a single Postgres table using workload_generator.py deployed to a dedicated EC2 instance. Throughput and end-to-end latency are measured with a Kafka consumer deployed to a separate EC2 instance. The stats are calculated as:

Throughput: the number of records delivered to Kafka per second.
Latency: the time between a change occuring in Postgres (updated_at timestamp) and it’s delivery to AWS MSK Kafka (Kafka creation timestamp).

Workload

workload_generator.py applies a mixed workload of INSERT, UPDATE, and DELETE operations to the benchmark_records table.

The benchmark_records Postgres table has the following schema:

                                           Table "public.benchmark_records"
     Column      |            Type             | Collation | Nullable |                    Default
-----------------+-----------------------------+-----------+----------+-----------------------------------------------
 id              | integer                     |           | not null | nextval('benchmark_records_id_seq'::regclass)
 string_field    | text                        |           |          |
 numeric_field   | numeric                     |           |          |
 timestamp_field | timestamp with time zone    |           |          |
 json_field      | jsonb                       |           |          |
 inserted_at     | timestamp without time zone |           |          | now()
 updated_at      | timestamp without time zone |           |          | now()

Stats collection

Similarly, the cdc_stats.py script is deployed to a separate EC2 instance and reads from AWS MSK Kafka. Stats are bucketed and saved to a CSV file for analysis.

Infrastructure

Sequin, Debezium, and the rest of the infrastructure are deployed to AWS in the following configuration:

AWS RDS Postgres db.m5.8xlarge instance (32 vCPUs, 128GB RAM)
AWS MSK Kafka provisioned with 4 brokers
AWS EC2 instances with Sequin running in Docker.

Summary

Tool / Deployment	Sustained throughput	Avg latency	99p latency
Sequin	>50 k ops / s	55 ms	253 ms
Debezium · MSK	6 k ops / s	258 ms	499 ms
Debezium · Server	23 k ops / s	210 ms	440 ms
Fivetran	5+ minutes	-	-	-
Airbyte	1+ hours	-	-	-

Sequin sustains 6 × – 8 × more throughput than Debezium’s on MSK, and over 2x the throughput of Debezium Server.

Next steps

Ready to see Sequin’s performance for yourself?

What is Sequin?

Learn about Sequin’s architecture and how it works.

Quickstart with Kafka

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Compare CDC Tools

See how Sequin stacks up against other CDC solutions.

Deploy to Production

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Get started

Sink setup guides

How-to guides

Concepts

Reference

Database setup guides

Tutorials

Performance

Sequin performance

Single‑slot peak throughput & latency

Sequin on different instance sizes

Debezium on MSK Connect (AWS)

Scaling limits

Debezium Server (stand‑alone)

Debezium on Confluent Cloud

Debezium on self‑hosted Kafka Connect

Benchmark methodology

Workload

Stats collection

Infrastructure

Summary

Next steps

What is Sequin?

Quickstart with Kafka

Compare CDC Tools

Deploy to Production

​Sequin performance

​Single‑slot peak throughput & latency

​Sequin on different instance sizes

​Debezium on MSK Connect (AWS)

​Scaling limits

​Debezium Server (stand‑alone)

​Debezium on Confluent Cloud

​Debezium on self‑hosted Kafka Connect

​Benchmark methodology

​Workload

​Stats collection

​Infrastructure

​Summary

​Next steps

What is Sequin?

Quickstart with Kafka

Compare CDC Tools

Deploy to Production

Sequin performance

Single‑slot peak throughput & latency

Sequin on different instance sizes

Debezium on MSK Connect (AWS)

Scaling limits

Debezium Server (stand‑alone)

Debezium on Confluent Cloud

Debezium on self‑hosted Kafka Connect

Benchmark methodology

Workload

Stats collection

Infrastructure

Summary

Next steps