AutoMQ vs StreamHouse

AutoMQ is Kafka-compatible streaming software that runs Kafka on cloud object storage. It supports S3, Google Cloud Storage, Azure Blob Storage, MinIO, and Ceph, and can be deployed on premises or in private and public clouds by binary, Helm, or Docker Compose. AutoMQ states sub-10ms P99 latency and automatic scaling with traffic, including partition rebalancing across brokers. Table Topic can materialize Kafka topics as Iceberg or Delta Lake tables without ETL pipelines, for direct querying with Spark, Trino, or Flink. Managed Connector runs Kafka Connect workers in a customer’s Kubernetes environment, with logs and monitoring for throughput, task health, failures, and anomalies. The platform lists stream processing, schema registry, exactly-once delivery, a maximum 90-day retention period, and 300 connectors. Supported client languages include Java, C/C++, Go, Python, and NodeJS. Pro costs $300 a month; Enterprise pricing is by contact. There is no free plan, but a 30-day free trial requires no credit card.

StreamHouse is an open-source, self-hostable system combining event streaming, processing, schema management, and observability. Developers can ingest events through REST, gRPC, or the Kafka protocol, then use its SQL engine to query streams and transform events in pipelines. Pipelines can continuously send SQL-transformed results to PostgreSQL and be managed through the CLI or an SDK. StreamHouse stores data in S3 and describes that storage as S3-native. Its schema registry supports JSON Schema, Avro, and Protobuf with compatibility checking. Listed sinks include PostgreSQL, S3, and Elasticsearch. Monitoring includes Prometheus metrics, Grafana dashboards, and real-time WebSocket metrics. Organization-scoped isolation and permissioned API keys provide access controls. Python and TypeScript SDKs are documented, while CLI installation is available through Homebrew or a shell installer. The project uses Apache License 2.0, and its stateless agents can scale like web servers.