Open Source Confluent Cloud Alternatives

AutoMQ is a fork of Apache Kafka with the storage engine replaced by one built on object storage, turning the classic shared-nothing design into shared storage. It targets the two things that make Kafka painful in the cloud: brokers hold state, so scaling means moving data, and block storage plus cross-zone traffic plus over-provisioning make the bill grow faster than the workload.

Because brokers keep no durable state, they scale in and out within seconds, and a built-in auto-balancer moves partitions and network traffic between them with no manual reassignment. Data lives on S3 or any compatible store, MinIO included.

The properties that follow from that design are concrete.

• Zero cross-zone traffic: object storage carries replication, removing the transfer costs that dominate a typical cloud Kafka bill.
• Durability: zero RPO, recovery measured in seconds and eleven nines of durability, inherited from the object store.
• Auto scaling: cluster metrics drive capacity up and down, so you pay for the workload rather than the annual peak.
• Kafka compatible: existing clients, tools and protocols work unchanged, since only the storage layer differs.
• Metrics export: Prometheus and OpenTelemetry natively, by push or pull, instead of JMX.

A single-node Docker Compose file with MinIO gives you an evaluation cluster in minutes, and a three-node file exercises clustering. Production deployment is documented for Linux and Kubernetes, and the maintainers say plainly that the quick start is not production shaped.

Timeplus Proton is a SQL pipeline engine for stream processing, analytics, observability and AI workloads, shipped as one C++ binary. It removes the JVM, ZooKeeper and the dependencies a streaming stack usually drags behind it, which is the argument for reaching past Apache Flink or ksqlDB.

The engine is powered by ClickHouse, extending its historical storage and compute with streaming. Materialized views are maintained incrementally and stay queryable inside Proton, so the same system feeds dashboards and applications rather than shipping every result elsewhere first. Thousands of SQL functions come with the ClickHouse lineage, and performance leans on SIMD optimization in C++.

SQL is the interface for everything, including the edges of the system.

• Sources and sinks: Kafka, ClickHouse, MySQL, Postgres, MongoDB, S3, Iceberg and OpenSearch, declared as external streams and tables.
• Windows and joins: tumble, hop and session windows, watermarks, and joins across several streams.
• Change data capture: CDC and data revision processing as part of the pipeline.
• Alerting and tasks: defined in SQL instead of a separate scheduler.
• User defined functions: extend the engine in Python or JavaScript.

Installation is a shell one liner, a Homebrew formula on macOS or a Docker image, and the binary is under 500MB with no dependencies, small enough for an AWS t2.nano. Published figures claim 90 million events per second and 4 millisecond end to end latency on a MacBook Pro with an M2 Max. Access it through the proton SQL shell, Python, Java and Go SDKs, a REST API or BI plugins.

Postgres is good at holding data and answering questions about it. Getting changes out of it and into everything else is where teams end up writing something brittle by hand. Sequin reads changes from any Postgres database from version 14 onward and delivers them to sinks, backfilling existing rows and streaming new ones in real time.

It runs as a standalone Docker container next to the database rather than as an extension. You name the schemas and tables to stream, add optional filters and transforms, and point them at a sink; delivery retries with exponential backoff when a sink is unavailable. Configuration happens in a web console, in YAML paired with the CLI, or through a management API, and Terraform templates for AWS live in the repository.

The delivery guarantees are what separate it from a webhook loop written in an afternoon.

• Strict ordering: every change reaches its sink, in order, under a documented consistency model.
• Exactly-once processing: idempotency keys keep a consumer from acting on the same change twice.
• Filters and transforms: payloads are reshaped by functions written in Elixir before they leave.
• Routing: individual messages are directed to specific topics, endpoints, or indexes.
• Backfills: a sink can be refilled from any point, including partial replays of specific rows.
• Metrics: a Prometheus endpoint exposes what the pipeline is doing.

Sinks include Kafka, NATS, RabbitMQ, Redis, SQS, SNS, Kinesis, GCP Pub/Sub, Azure Event Hubs, Elasticsearch, Typesense, Meilisearch, and webhooks. Published benchmarks report sustained throughput above 50,000 operations per second at 55ms average latency.