At a glance, Managed ClickStack and Elastic Cloud look familiar. Both provide an end-to-end observability stack with data collection, storage, and a UI for search, dashboards, and analysis. OpenTelemetry and Beats serve similar roles, HyperDX and Kibana feel conceptually alike, and both platforms aim to make large-scale observability data accessible.
The key difference lies in the underlying architecture. Elastic Cloud is built on Elasticsearch, a distributed search engine optimized around inverted indices and shard-based storage. This works well for text search, but comes with higher storage overhead, limited compression, and increasing contention between ingest and query workloads as data volumes and cardinality grow. Elastic Cloud’s serverless offerings improve operational simplicity and long-term retention through object storage, but the core search-centric architecture remains.
Managed ClickStack is built on ClickHouse Cloud, a column-oriented analytics engine designed for high-cardinality data and fast aggregation at scale. Columnar storage enables dramatically higher compression, while massively parallel query execution delivers faster aggregations and analytics across large datasets. In ClickHouse Cloud, separation of storage and compute allows low-cost object storage for long-term retention, independent scaling of ingest and query workloads, and predictable performance as data grows.
The result is a platform that feels familiar to users of Elastic, but delivers lower storage costs, faster analytical queries, and greater efficiency for real-time observability and long-term analysis at petabyte scale.
For more details on how ClickStack compares with the ELK Stack see our comparison guide.