Frequently Asked Questions

Question 1

Is OpenObserve a good ClickHouse alternative for observability?

Accepted Answer

Yes, if your goal is observability rather than general-purpose analytics. ClickHouse is an excellent OLAP database, but using it for logs and traces means building and operating your own stack: schema design, ingestion pipelines, a UI (Grafana or HyperDX), and alerting. OpenObserve is a purpose-built observability platform with logs, metrics, traces, dashboards, alerts, and pipelines included. If you need a general analytics database for product or business data, ClickHouse remains a great choice; many teams use both.

Question 2

How does OpenObserve pricing compare to ClickHouse Cloud?

Accepted Answer

ClickHouse Cloud is usage-based: compute billed per unit-hour, storage per compressed TB per month, plus separate data transfer charges and per-GB fees for managed ingestion (ClickPipes). Costs vary with query load and are hard to forecast, and they don't include the engineering time spent maintaining the observability layer on top. OpenObserve uses simple ingest-based pricing, and self-hosted OpenObserve is free; storage on S3 or other object storage keeps long retention cheap.

Question 3

How much effort is migrating from ClickHouse to OpenObserve?

Accepted Answer

Usually less than you'd expect, because most ClickHouse observability stacks already use the OpenTelemetry Collector. Migration is largely a collector reconfiguration: add OpenObserve's OTLP endpoint as an exporter, dual-ship for a few weeks, rebuild key dashboards and alerts, then cut over. Historical data can stay queryable in ClickHouse until its retention expires; most teams don't backfill. Simple setups migrate in days to weeks; heavily customized stacks take longer.

Question 4

Can I keep using SQL like I do in ClickHouse?

Accepted Answer

Yes. OpenObserve is SQL-native for querying logs and traces, so your team's SQL skills carry over directly; most queries translate with minor dialect changes. You also get PromQL for metrics and VRL-based functions for data transformation, without needing to design tables, partition keys, or materialized views first.

Question 5

Is OpenObserve open source and self-hostable like ClickHouse?

Accepted Answer

Yes. OpenObserve is open source and can be self-hosted as a single binary for small setups or as a highly available cluster on Kubernetes via Helm. Data is stored in open Apache Parquet format on object storage (S3, GCS, Azure Blob, MinIO), so there's no lock-in; your data stays readable by any Parquet-compatible tool.

Question 6

What about ClickStack and HyperDX, don't they make ClickHouse an observability platform?

Accepted Answer

ClickStack (ClickHouse + OpenTelemetry + HyperDX) narrows the gap, but it's still a stack of components you assemble, configure, and operate: database, collector, and UI each with their own scaling and upgrade story. OpenObserve delivers the same outcome as a single integrated platform: one deployment, one UI, built-in alerting and pipelines, and stateless nodes on object storage. Fewer moving parts means less engineering time spent on the tooling itself.

Question 7

Will query performance match ClickHouse?

Accepted Answer

ClickHouse is one of the fastest OLAP databases available, and for arbitrary analytical queries over huge datasets it's hard to beat. OpenObserve is optimized specifically for observability workloads, time-range scans, full-text log search, trace lookups, and dashboard aggregations, using columnar Parquet with partitioning and caching. For typical observability queries, performance is fast where it matters, without the schema tuning ClickHouse needs to get there.

Question 8

Is OpenObserve enterprise-ready?

Accepted Answer

Yes. OpenObserve is SOC2 Type II certified and ISO 27001 compliant. We process over 2 PB of data daily across thousands of deployments, including Fortune 100 enterprises. Enterprise features include RBAC, SSO, sensitive data redaction, and dedicated support.

Feature	ClickHouse	OpenObserve	Reference Links
Feature parity: logs, metrics, traces, dashboards, alerts, pipelines	Build it yourself on top (ClickStack, Grafana, custom pipelines)	✓	Logs Metrics Traces Dashboards Alerts Pipelines
Purpose-built observability UI	✗ Requires Grafana, HyperDX, or a custom frontend	✓ Log search, dashboards, and trace views built in	Learn more
Query language	SQL (ClickHouse dialect)	SQL + PromQL	Familiar SQL, plus PromQL for metrics
Schema management	Manual: table design, partition keys, ordering keys, materialized views	Automatic schema on ingest: no table design	Learn more
Alerting	✗ External tooling required	✓ Scheduled and real-time alerts built in	Learn more
Ingestion	OTel Collector exporter plugins + insert/batching tuning	Native OTLP, plus Fluent Bit, Vector, and 30+ integrations	Learn more
Data transformation pipelines	Materialized views and custom insert logic	✓ Built-in pipelines with VRL functions	Learn more
Storage & retention	Columnar MergeTree; retention via TTLs you configure and monitor	Parquet on object storage: long retention without budget blowouts	Learn more
Operations at scale	Shards, replicas, and Keeper to manage (or usage-based Cloud)	Stateless nodes: scale without rebalancing	Learn more
Open Source	✓	✓	-
IAM & SSO	Database-level users and roles	✓ SAML, OIDC, LDAP, role-based access	Learn more

OpenObserve vs ClickHouse

Why teams switch from ClickHouse

No DIY Stack to Build

No Schema Engineering

Predictable Ingest Pricing

Logs, Metrics, Traces Unified

OpenTelemetry-Native

Minimal Operational Overhead

See how OpenObserve replaces your ClickHouse stack

Feature comparison

Migrating from ClickHouse

Repoint your OpenTelemetry Collector

Recreate dashboards and migrate alerts

Complete cutover and retire the DIY stack