GreptimeTeam/greptimedb

The open-source observability database. One columnar engine for metrics, logs, and traces, on object storage.

What it solves

GreptimeDB is an open-source observability database designed to unify metrics, logs, and traces into a single columnar engine. It eliminates the need to run multiple separate backends (like Prometheus for metrics, Loki for logs, and Tempo for traces) and allows users to correlate these different signals using SQL without moving data between databases.

How it works

It uses a columnar storage engine built on object storage (S3, GCS, Azure Blob), with memory and local-disk caches to keep frequently accessed data close to the compute layer. The system disaggregates compute and storage, allowing them to scale independently. It supports a unified table model (tags, timestamp, and fields) and provides multiple ingestion protocols including OpenTelemetry, Prometheus Remote Write, and Loki Push.

Who it’s for

It is built for developers and platform engineers who have outgrown the cardinality or retention limits of Prometheus, are hitting performance bottlenecks with Loki, or want a single, SQL-capable backend for their observability telemetry, including telemetry for GenAI and AI agents.

Highlights

  • Unified Engine: Stores and queries metrics, logs, and traces in one place.
  • Object Storage Native: Primary data is stored on object storage for long-term retention andn cost-efficiency.
  • Multi-Protocol Support: Ingests data via OTLP, Prometheus, Loki, and Elasticsearch Bulk.
  • SQL & PromQL: Supports standard SQL for complex correlations and PromQL for metrics.
  • AI Agent Ready: Specifically optimized for LLM agents performing root cause analysis, reducing wrong diagnoses and token costs.

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