# Every Log. Fully Indexed. Instantly Searchable. > Transform log data into insights with SQL queries, real-time analytics, and efficient storage. Supports JSON, cloud providers, and VRL parsing. Start free. Source: https://openobserve.ai/logs/ --- Every log field, fully indexed, forever queryable, powered by columnar Parquet storage and a Rust query engine, at a fraction of Elasticsearch’s cost. - [Start Free Cloud Trial](https://cloud.openobserve.ai/web/login/) - [Talk to a Human](/demo/) ### 140x More Storage Efficient Than Elasticsearch Columnar Parquet storage cuts log storage cost without cutting retention. ### Sub-Second Queries at Terabyte Scale A Rust query engine keeps searches fast as data grows. ### OTel-Native Logs Standardized collection with zero vendor lock-in. ## Every Log, Searchable the Moment It Lands Scale from gigabytes to petabytes without the bill scaling with it. ### Log Processing - **Automated Parsing** - Convert raw logs into structured data automatically with intelligent built-in parsing for common formats, including [JSON](https://openobserve.ai/blog/json-logging-guide-examples/), [CSV](https://openobserve.ai/blog/json-logging-guide-examples/), [Syslog](https://openobserve.ai/docs/ingestion/logs/syslog/), [CEF](https://openobserve.ai/blog/json-logging-guide-examples/), [nginx](https://openobserve.ai/docs/integration/servers/nginx/), and more. - **Flexible Transformation Pipeline** - Enrich, filter, and reshape logs during ingestion with [Vector Remap Language](https://openobserve.ai/docs/ingestion/logs/vector/), maintaining peak performance while handling any log format. [Learn More](https://openobserve.ai/blog/how-log-parsing-works-in-openobserve/) ### Optimized Storage - **Industry-Leading Compression** - Slash log storage costs by up to 140x compared to Elasticsearch with [columnar storage](https://openobserve.ai/docs/features/logs/) and [Parquet format](https://openobserve.ai/docs/features/logs/). - **Retention Management** - Configure custom log [retention policies](https://openobserve.ai/docs/user-guide/data-processing/streams/extended-retention/) per data source, keeping valuable logs accessible longer without escalating costs. [Learn More](https://openobserve.ai/docs/user-guide/data-processing/streams/extended-retention/) ### Real-Time Analytics - **Instant Ingest-to-Dashboard** - Get answers the moment logs arrive, with [sub-second results](https://openobserve.ai/docs/user-guide/account-administration/management/streaming-search/) even across petabytes of data. - **Log-Centric Visualizations** - Build customized logging [dashboards](https://openobserve.ai/docs/user-guide/analytics/dashboards/dashboards-in-openobserve/) in minutes with a drag-and-drop interface that updates live and shares with one click. [Learn More](https://openobserve.ai/docs/user-guide/analytics/dashboards/dashboards-in-openobserve/) ### Powerful Search - **Instantaneous Text Search** - Find specific log entries across massive datasets in seconds with intelligent [pattern matching](https://openobserve.ai/docs/reference/sql-functions/full-text-search/) that surfaces relevant results first. - **Simple, Yet Precise Queries** - Use [SQL](https://openobserve.ai/docs/reference/sql-functions/full-text-search/) to run precise queries and complex aggregations across all log sources. [Learn More](https://openobserve.ai/docs/reference/sql-functions/full-text-search/) ## Measured against industry leaders Same telemetry, same workloads, one platform. Every number is OpenObserve against a named vendor - not an industry average. 8x cost reduction means you can unify your observability into a single platform. 140x storage means longer retention doesn't necessarily mean expensive bills. 5x to 15x faster queries mean dashboards load in milliseconds, not minutes. See how much you would save switching today. - [See all comparisons](/comparison/) ## Teams trust OpenObserve with their logs ## Log FAQs ### What is the best self-hosted logging solution? OpenObserve is an open source (AGPL-3.0), self-hosted logging solution built for teams that want full control over their data without Elasticsearch-scale costs. It ships as a single binary or Helm chart and stores logs in Apache Parquet on object storage - S3, GCS, MinIO, or Azure Blob - cutting storage costs by up to 140x compared to Elasticsearch while keeping full-text search and SQL queries sub-second. Common formats such as JSON, Syslog, CEF, and nginx are parsed automatically on ingest, and VRL pipelines can enrich or filter logs in flight. Per-stream retention policies let you keep valuable logs longer without an escalating bill, and the same engine holds metrics and traces, so an investigation never requires a second tool. ### What is the best log management software for startups? OpenObserve fits startups because it’s open source, free to self-host, and free up to 50GB/day on Cloud, with no per-host or per-seat pricing to outgrow. A single binary deployment means no dedicated ops team is needed to run it in production. ### What is the cheapest way to store and search logs at scale? The cheapest way to store and search logs at scale is columnar storage on object storage like S3 instead of an indexed cluster like Elasticsearch. OpenObserve uses this approach with Apache Parquet, delivering up to 140x lower storage costs while still supporting full SQL search across every field. Object storage costs a fraction of the SSD-backed hot tiers an indexed cluster requires, and columnar compression shrinks what you store in the first place. Because queries stay sub-second even at terabyte-to-petabyte scale, you no longer have to choose between affordable retention and searchable history - configure retention per stream and keep months of logs queryable. Pricing follows ingestion volume, not hosts or seats, so cost stays predictable as infrastructure grows. ### How do I migrate logs off an expensive Elasticsearch cluster? Point your existing log shippers, Fluent Bit, Filebeat, Vector, or Logstash, at OpenObserve’s HTTP ingestion endpoint instead of Elasticsearch, no re-architecture required. Most teams run OpenObserve alongside their ELK stack during migration and cut over once dashboards and alerts are rebuilt. ### How does S3 object storage cut log storage costs? Object storage like S3 costs a fraction of the SSD-backed indexed storage that tools like Elasticsearch require, and OpenObserve uses S3, or any S3-compatible storage, as its primary data tier rather than a separate archive. Combined with Parquet’s columnar compression, this is how OpenObserve reaches up to 140x lower storage costs than Elasticsearch while keeping data queryable, not just archived. ### How is OpenObserve different from Elasticsearch or Splunk? The differences show up in cost, operations, and query language: - Up to 140x lower storage cost via columnar Parquet on object storage. - No shards, replicas, or heap sizes to tune, thanks to a single-binary deployment. - SQL and PromQL instead of a proprietary query language. Learn more about [log storage](https://openobserve.ai/docs/features/logs/). ## Explore guides, videos, and articles to help you get the most out of Logs. ### Log Ingestion Guide [Learn more](https://openobserve.ai/docs/ingestion/logs/) ### Log Exploration User Guide [Learn more](https://openobserve.ai/docs/user-guide/data-exploration/logs/logs/) ### Introduction to Highly Performant Logging with OpenObserve [Learn more](https://openobserve.ai/webinars-videos/highly-performant-logging-openobserve-introduction/) - [Explore All Blogs](/blog/) ## Ready to get started? Try OpenObserve today for more efficient and performant observability. - Get Started For Free - [Schedule Demo](/demo/)