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  • Observability

ClickHouse vs Elastic

ClickHouse vs Elastic

ClickStack is a high-performance, open-source observability stack built on ClickHouse. It delivers high compression, lightning-fast queries and powerful aggregations across high cardinality logs, metrics, traces, session replays at petabyte scale.

Elastic, by contrast, is rooted in a full-text search engine, built on the belief that observability was just a search problem at a time when inverted indices were sufficient at smaller scales. Never designed for metrics, it cannot unify logs, metrics, and traces in a single system, leaving observability fragmented, costly, and slow.

Why ClickStack is better:

4x

Reduction in costs

10x

Faster analytical queries

3x

Better compression

Read our comprehensive guide about migrating from Elastic to ClickStack.

Frustrated by slow queries, rising storage costs and an endless need to scale horizontally? You’re not alone.

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Performance at Scale

Elastic slows under heavy ingest and high-cardinality queries, while ClickHouse powers sub-second analytics even at petabyte scale.
Full data set aggregation for 1 billion JSON documents
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Lower Cost, Higher Efficiency

ClickHouse’s columnar storage and advanced compression cut storage needs by > 50%, reducing infrastructure costs dramatically and allowing for long term retention.
Storage required for 1 billion JSON documents
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Unified Observability

ClickStack runs logs, metrics, and traces in one engine alongside business and application data for unrivalled correlation. Elastic was never designed for analytical workloads leaving data fragmented.
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Open source and open standards

ClickStack is fully open source (MIT + Apache 2.0) and OpenTelemetry-native, ensuring interoperability and freedom from lock-in.

ClickStack compared to Elastic Observability

At a high level, Elastic and ClickStack share a familiar shape: both have a data collection layer (Beats and Logstash vs. OpenTelemetry), a storage engine (Elasticsearch vs. ClickHouse), and a UI (Kibana vs. HyperDX). But beneath these parallels, the architectures diverge.
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Elastic is a distributed search engine built around inverted indices and a shard-based architecture. While effective for full-text search, this design introduces high storage overhead, limited query parallelization, and heavy contention between ingest and query workloads.

ClickStack is powered by ClickHouse, a database built on a columnar, shared-nothing architecture optimized for analytics. It minimizes storage with advanced compression, parallelizes queries across all available cores, and separates storage from compute in the cloud to deliver fast, efficient observability at scale. Full SQL support unlocks deep data analysis.

ClickHouse

  • Yes

    Native object storage support

  • Yes

    Aggregate states for accuracy

  • Yes

    Columnar storage with high compression

  • Yes

    ClickPipes for streaming ingest

  • Yes

    JSON with type fidelity

  • Yes

    1,000+ QPS per node

  • Yes

    Full parallelization within & across shards

  • Yes

    Full join support

  • Yes

    Materialized views (incremental & refreshable)

  • Yes

    Full SQL support

  • Yes

    Natural language search via HyperDX

  • Yes

    Self-managed & cloud

  • Yes

    Decoupled compute/storage (ClickHouse Cloud)

  • Yes

    Stateless compute nodes (ClickHouse Cloud)

  • Yes

    Real-time ingest supported

  • Yes

    Async inserts for small batches

  • Yes

    Inverted index support

  • Yes

    REST API support

  • Yes

    Incremental materialized views

Elastic

  • No

    Paid, Elastic Cloud Serverless only

  • No

    Terms aggregations are estimates

  • Intermediate

    Doc values provide columnar storage, not compressed

  • Intermediate

    Requires Logstash/third parties, no hosted ingestion

  • Intermediate

    First event field determines type

  • Intermediate

    Requires horizontal scaling/replicas

  • Intermediate

    Limited; relies only on shards

  • Intermediate

    LOOKUP JOIN only

  • Intermediate

    Limited types; requires full scans

  • Intermediate

    Limited syntax coverage

  • Yes

    Natural language search supported

  • Yes

    Self-managed & Elastic Cloud

  • Yes

    Elastic Cloud Serverless

  • Yes

    Elastic Cloud Serverless

  • Yes

    Real-time ingest supported

  • Yes

    Inserts supported

  • Yes

    Inverted index supported

  • Yes

    REST API supported

  • Yes

    Ingest pipelines

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Lower costs

10x cost savings thanks to high compression and resource efficiency
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Simpler at scale

Homogenous architecture and vertical scaling simplifies and reduces nodes
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Built for high cardinality analytics

Column orientation designed for high cardinality queries
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Open and interoperable

Supports open standards like OpenTelemetry and integrates directly with systems and formats such as Postgres, Kafka, Parquet, and Iceberg.

Migrate your workload from Elastic today

Cut costs, boost performance, and unlock observability at scale with ClickHouse.
We’ll get you started on a 30 day trial and $300 credits to spend at your own pace.

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