Frequently Asked Questions

Question 1

Is OpenObserve a good Elasticsearch alternative?

Accepted Answer

For observability, logs, metrics, and traces, yes. OpenObserve was built specifically as an ELK replacement: it ships logs to compressed columnar Parquet on object storage (about 140x lower storage cost in our benchmark), needs no index, shard, or JVM management, and includes dashboards, alerts, and pipelines out of the box. If you use Elasticsearch as a general-purpose search engine or document store for your product, that is Elasticsearch's home turf; keep it there and move only your observability workloads.

Question 2

How much cheaper is OpenObserve than Elasticsearch?

Accepted Answer

Two savings compound. Storage: Elasticsearch indices are often as large as the raw data and then replicated across hot nodes, while OpenObserve compresses data into Parquet on S3: roughly 140x lower storage cost in our published benchmark. Operations: no dedicated cluster to size, tier, and babysit. If you are on Elastic Cloud, you are also billed per GB for ingest and retention (measured on uncompressed data), which grows linearly with volume. Actual savings depend on your data shape, replication factor, and retention.

Question 3

How hard is it to migrate from the ELK stack?

Accepted Answer

Easier than most migrations, because it is a pipeline cutover rather than a data migration. OpenObserve exposes an Elasticsearch-compatible API, so Filebeat, Fluent Bit, Logstash, and the OpenTelemetry Collector can dual-ship with an output-endpoint change. Run both platforms in parallel for a few weeks, rebuild your critical Kibana dashboards in SQL, then let old indices age out. Simple setups cut over in days; larger estates with heavy Logstash logic typically take a few weeks to a couple of months.

Question 4

Do I lose full-text search if I leave Elasticsearch?

Accepted Answer

Elasticsearch's inverted index is best-in-class for general-purpose document search, and we won't pretend otherwise. OpenObserve provides full-text search designed for observability workloads, finding errors, request IDs, and patterns across huge log volumes, plus SQL for structured analysis. For log exploration and troubleshooting, teams rarely miss anything. For powering product search features, Elasticsearch remains the right tool.

Question 5

Do I still need to manage shards, mappings, or the JVM?

Accepted Answer

No. There are no shards to size, no ILM policies to write, no index mappings to fight, and no JVM heap to tune. OpenObserve is a single Rust binary (or a stateless HA cluster via Helm) with data on object storage, so a node restart doesn't trigger shard-recovery storms and there are no garbage-collection pauses or OutOfMemory crashes to chase.

Question 6

Is OpenObserve open source and self-hostable?

Accepted Answer

Yes. The core platform is open source on GitHub (19k+ stars) and you can self-host it anywhere, a laptop, a VM, or Kubernetes, with your own S3-compatible bucket, so your data stays under your control. A managed cloud offering and enterprise features (advanced RBAC, SSO, support) are available when you want them.

Question 7

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	Elasticsearch	OpenObserve	Reference Links
Feature parity: logs, metrics, traces, dashboards, alerts, pipelines	✓ (assembled from Elasticsearch, Kibana, Logstash, APM)	✓ Built into one platform	Logs Metrics Traces Dashboards Alerts Pipelines
Storage backend	Local disk indices with replicas; hot/warm/cold tiering to manage	S3 / GCS / Azure Blob / MinIO: compressed columnar Parquet	Learn more
Storage cost for logs	Indices often as large as raw data, then replicated	~140x lower in our benchmark, thanks to columnar compression on object storage	How we replace Elasticsearch
Index & shard management	Required: shard sizing, ILM policies, rebalancing, rollover	None: no shards, no ILM, no rebalancing	Architecture
JVM / runtime tuning	JVM heap sizing, GC pauses, memory-pressure firefighting	None: Rust binary, no JVM, no garbage collector	-
Query language	Query DSL (JSON), KQL, ES\|QL	SQL + PromQL	Used universally with no learning curve
Deployment	Multi-node cluster with master/data/ingest roles	Single binary, Docker, or HA cluster via Helm in minutes	Quickstart
Schema handling	Index mappings; mapping conflicts and field explosions	Schema-on-ingest with automatic evolution	-
Data retention	Longer retention means more hot/warm nodes or frozen-tier setup	Object storage makes long retention affordable by default	Learn more
Full-text search on documents	✓ Best-in-class inverted index	✓ Full-text search tuned for observability workloads	-
Open Source	✓ (AGPL option since 2024; some features need paid tiers)	✓ Core platform open source on GitHub	-
IAM & SSO	SAML/OIDC require paid Platinum+ subscription	✓ SAML, OIDC, LDAP, role-based access	Identity and access management

OpenObserve vs Elasticsearch

Why teams switch from Elasticsearch

140x Lower Storage Cost

No Index or Shard Management

No JVM Heap Tuning

One Platform, Not a Stack

OpenTelemetry-Native, No Lock-in

Single Binary to Petabyte Scale

See how OpenObserve replaces Elasticsearch

Feature comparison

Migrating from Elasticsearch

Dual-ship from your existing collectors

Recreate dashboards and migrate alerts

Cut over and retire the cluster