# OpenObserve vs Elasticsearch > OpenObserve vs Elasticsearch: 140x lower storage cost on S3, no shard or JVM tuning, single binary, OTel-native. Open source ELK alternative. Try free. Source: https://openobserve.ai/elasticsearch-alternative/ Competitor: Elasticsearch --- 140x lower storage cost on plain object storage. No shards, no JVM tuning. A single binary instead of a cluster. See why teams are replacing the ELK stack. - **140x** Lower storage costs compared to Elasticsearch - **21,000+** Github Stars - **9,000+** Companies trust us Teams cut their Elasticsearch storage costs up to 140x. [See your ingest-based pricing →](/pricing/) *Based on OpenObserve's published log-storage benchmark versus Elasticsearch. Actual savings depend on data shape, replication factor, and retention; Elastic Cloud additionally bills ingest and retention per GB.* - [Request Demo](/demo/) - [Start Free](https://cloud.openobserve.ai/web/login) ## Why teams switch from Elasticsearch The many reasons that teams are leaving the ELK stack behind ### 140x Lower Storage Cost Columnar Parquet on S3 instead of replicated indices on hot SSDs. Keep months of logs, not days. ### No Index or Shard Management No shard sizing, no rebalancing, no red clusters after a node restart. Stateless nodes, data on object storage. ### No JVM Heap Tuning Written in Rust: no garbage-collection pauses, no OutOfMemory crashes, no heap-size guesswork. ### One Platform, Not a Stack Logs, metrics, traces, dashboards, alerts, and pipelines built in. No assembling Elasticsearch + Logstash + Kibana + APM Server. ### OpenTelemetry-Native, No Lock-in First-class OTLP ingestion plus Elasticsearch-compatible APIs. Data stored in open Apache Parquet; switch anytime. ### Single Binary to Petabyte Scale Start with one binary on a laptop, grow to an HA cluster via Helm. No master, data, and ingest node choreography. ## See how OpenObserve replaces Elasticsearch Get a personalized walkthrough and see how much you'd save moving off Elasticsearch clusters and Elastic Cloud's per-GB ingest and retention billing. - 30-minute personalized walkthrough - No credit card required - See your real migration path from the ELK stack ## Feature comparison Modern, full-stack observability | Feature | Elasticsearch | OpenObserve | Reference Links | | --- | --- | --- | --- | | Feature parity: logs, metrics, traces, dashboards, alerts, pipelines | ✓ (assembled from Elasticsearch, Kibana, Logstash, APM) | ✓ Built into one platform | [Logs](/docs/user-guide/data-exploration/logs/#overview), [Metrics](/docs/user-guide/data-exploration/metrics/), [Traces](/opentelemetry/), [Dashboards](/docs/user-guide/analytics/dashboards/dashboards-in-openobserve/), [Alerts](/docs/user-guide/analytics/alerts/), [Pipelines](/docs/user-guide/data-processing/pipelines/pipelines/) | | Storage backend | Local disk indices with replicas; hot/warm/cold tiering to manage | S3 / GCS / Azure Blob / MinIO: compressed columnar Parquet | [Learn more about Storage backend](/docs/administration/maintenance/storage-management/) | | 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](/blog/replace-elasticsearch-log-management/) | | Index & shard management | Required: shard sizing, ILM policies, rebalancing, rollover | None: no shards, no ILM, no rebalancing | [Architecture](/docs/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](/docs/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 about Data retention](/docs/administration/maintenance/storage-management/) | | 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](/docs/user-guide/account-administration/identity-and-access-management/) | ## Migrating from Elasticsearch Moving off ELK is a pipeline cutover, not a data migration. Redirect new data and let old indices age out. ### Dual-ship from your existing collectors Deploy OpenObserve alongside Elasticsearch and send data to both. Point Filebeat, Fluent Bit, Logstash, or the OpenTelemetry Collector at OpenObserve; its Elasticsearch-compatible API means most agents only need a new output endpoint. ### Recreate dashboards and migrate alerts Translate your key Kibana queries from Query DSL/KQL to plain SQL. Rebuild critical dashboards in OpenObserve and configure alerts with equal or better granularity. Parsing and enrichment move from Logstash to built-in pipelines. ### Cut over and retire the cluster Gradually shift production workloads, starting with non-critical services, and validate results side by side. Once retention windows lapse, decommission the Elasticsearch data nodes, and the shard, ILM, and JVM upkeep with them. > OpenObserve is super fast, definitely very lightweight, and you can get started with an initial POC in two to three minutes to be honest. > - Ajith Natarajan, Lead Software Engineer, Radius.ai - [Talk to Our Migration Team](#demo-form) - [Read Migration Stories](/customer-stories/) ## Frequently Asked Questions Common questions about switching from Elasticsearch to OpenObserve ### Is OpenObserve a good Elasticsearch alternative? 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. ### How much cheaper is OpenObserve than Elasticsearch? 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. ### How hard is it to migrate from the ELK stack? 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. ### Do I lose full-text search if I leave Elasticsearch? 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. ### Do I still need to manage shards, mappings, or the JVM? 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. ### Is OpenObserve open source and self-hostable? 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. ### Is OpenObserve enterprise-ready? Yes. OpenObserve is SOC2 Type II certified and ISO 27001 compliant. We serve thousands of deployments, including Fortune 100 enterprises; our largest single customer ingests more than 4 PB of data per day, with individual clusters sustaining over 1 PB/day. Enterprise features include RBAC, SSO, sensitive data redaction, and dedicated support. ## Ready to See the Difference? Get a personalized demo based on your current Elasticsearch usage ## OpenObserve: the open-source Elasticsearch alternative An open-source, SQL and OpenTelemetry-native observability platform with 140x lower storage costs than Elasticsearch: no shards, no JVM, no cluster babysitting. Radius.ai got started with a working POC in minutes, not months. Also evaluating other tools? See how OpenObserve compares to [Splunk](/splunk-alternative/), [Logz.io](/logz-io-alternative/), [ClickHouse](/clickhouse-alternative/). - 140x lower storage cost vs. Elasticsearch - No index, shard, or JVM management: single binary - S3-backed, self-hosted or cloud: your data, your control