Parseable is an open source, unified infrastructure observability platform built in Rust on a data lake architecture, made for platform, SRE, and infrastructure teams that need to track logs, metrics, traces, and events across their applications, agents, and systems.

What it is

Parseable is a columnar data lake platform purpose built for observability, written in Rust and licensed under AGPL-3.0. It keeps stateless compute over object storage as the backing store, so storage and compute scale independently and a team does not pay for one to grow the other. Alerting, dashboards, anomaly detection, APM, PromQL, and SQL all ship inside a single binary rather than as separate services to stitch together. It sits in the cloud-native observability ecosystem, alongside the log-storage, metrics, events, and agent-observability concerns its topic list names.

The problem it addresses is the assembled observability stack: a log store here, a metrics store there, a collector layer, a dashboard layer, and a query language that only covers part of the data. Parseable ingests through popular logging agents, OpenTelemetry, Kafka, eBPF, and other integrations, and the README states it can serve as a drop-in replacement for an existing OpenTelemetry Collector setup. It tracks logs, metrics, traces, and events across apps, agents, and systems, and the project claims storage cost reductions of up to 90 percent through columnar telemetry compression.

Key capabilities

• Ingestion through a single endpoint, POST http://localhost:8000/api/v1/ingest, which selects the target stream with a header such as X-P-Stream: demo and authenticates with Basic auth.
• Native OpenTelemetry support, usable without custom modifications or plugins, and positioned as a drop-in replacement for an existing OpenTelemetry Collector setup.
• Data intake from popular logging agents, Kafka, eBPF, and other integrations.
• Query in both PromQL and SQL, with alerting, dashboards, anomaly detection, and APM available from the same binary.
• Agent-ready observability, covering both observing AI agents and using LLMs to analyze telemetry data.
• A single binary with a UI served at http://localhost:8000, reachable on first run with the default credentials admin / admin.
• Attested builds produced with attest-build-provenance, verifiable with gh attestation verify PATH/TO/YOUR/PARSEABLE/ARTIFACT-BINARY -R parseablehq/parseable.

Who uses it and how

• Teams consolidating logs, metrics, and traces onto object storage, using independent scaling of storage and compute to keep cost tied to the dimension that actually grows.
• Platform and SRE groups that already run an OpenTelemetry Collector and want to replace that setup with one binary that also provides query, dashboards, and alerting.
• Production self-hosters, who are directed to the self-hosted installation guide for best practices and hardening rather than to the laptop quickstart.
• Teams evaluating the hosted path, available as Parseable cloud at app.parseable.com with a 14-day free trial and no credit card required.
• Groups that need to observe their own AI agents or feed telemetry data to LLMs for analysis.

Getting started

Run the binary on a laptop with curl -fsSL https://logg.ing/install | bash, or on Windows with powershell -c "irm https://logg.ing/install-windows | iex", then ingest data and open http://localhost:8000. Production deployments follow the self-hosted installation guide, and a hosted cloud option is offered as an alternative.

How it compares

The provided facts name no set of paid products that this project replaces, so the comparison rests on the similar tools that are named. The topic list files it as a grafana-alternative, and the README positions it as a drop-in replacement for an existing OpenTelemetry Collector setup, which places it against the collector-plus-dashboard combination rather than against a single product. Its distinguishing properties in those facts are the AGPL-3.0 licence, self-hosting from one binary, data kept in the operator's own object storage, and a cost model tied to storage rather than to a per-seat or per-volume subscription.

When to use it — and when not

A self-hoster takes on running the binary and supplying the object storage that backs the data lake, and is expected to follow the installation guide for production hardening rather than the quickstart defaults, which include the admin / admin credentials. Anyone who cannot accept the AGPL-3.0 licence, or who wants a fully managed service with no storage layer of their own, should look at the hosted cloud offering instead. The provided README excerpt is quickstart and feature-highlight oriented and does not enumerate the full operational dependencies of a production deployment, so those details should be confirmed in the docs before committing; the repository itself shows 2,458 stars, 172 forks, and 20 open issues, with the last push on 2026-09-17.

curl -fsSL https://logg.ing/install | bash

powershell -c "irm https://logg.ing/install-windows | iex"

curl --location --request POST 'http://localhost:8000/api/v1/ingest' \
--header 'X-P-Stream: demo' \
--header 'Authorization: Basic YWRtaW46YWRtaW4=' \
--header 'Content-Type: application/json' \
--data-raw '[
    {
        "id": "434a5f5e-2f5f-11ed-a261-0242ac120002",
        "datetime": "24/Jun/2022:14:12:15 +0000",
        "host": "153.10.110.81"
    }
]'

gh attestation verify PATH/TO/YOUR/PARSEABLE/ARTIFACT-BINARY -R parseablehq/parseable