Parseable - AI-Native Unified Observability Platform

# Parseable Documentation This document contains [Parseable's official documentation and guides](https://www.parseable.com/docs) in a single-file easy-to-search form. If you find any issues, please report them [as a GitHub issue](https://github.com/parseablehq/parseable/issues). Contributions are very welcome in the form of [pull requests](https://github.com/parseablehq/parseable/pulls). If you are considering submitting a contribution to the documentation, please consult our [contributor guide](https://github.com/parseablehq/parseable/blob/main/CONTRIBUTING.md). Code repositories: * Parseable source code: [github.com/parseablehq/parseable](https://github.com/parseablehq/parseable) This content is designed to be easily copied and provided to Large Language Models (LLMs) for summarization and analysis. Use the copy button on the Parseable LLM Text page to easily transfer this content to your favorite AI assistant. Title: Introduction URL Source: https://www.parseable.com/docs/introduction Markdown Content: Parseable combines a purpose built OLAP, diskless database [Parseable DB](https://github.com/parseablehq/parseable) and Prism UI. These components are designed from first principles to work together, enabling efficient and fast ingestion, search, and correlation of MELT (Metrics, Events, Logs, and Traces) data.  ### **Key Features** * **Cost-Effective**: Efficient compute utilization, compression and utilizing object storage like S3 offers up to 70% cost reduction compared to Elasticsearch or up to 90% compared to DataDog. * **Performance**: With Rust based design, modern query techniques, and intelligent caching on SSDs / NVMe and memory, Parseable offers extremely fast query experience for end users. * **Resource Efficiency:** * Parseable consumes 50% less CPU and 80% less