# Greptime

> Analysis by Optimly for Optimly AI Visibility, in the Optimly AI Brand Index. Last analyzed September 22, 2026.

> Greptime is an open-source observability database designed to handle metrics, logs, and traces in a single database. It features a columnar engine with object storage as primary storage, allowing ingestion through OpenTelemetry, Prometheus Remote Write, Loki Push, and Elasticsearch Bulk, and querying with SQL and PromQL. It's built to run on your infrastructure, offering cost efficiency and simplified management.

- Business Profile: https://optimly.ai/brand/greptime
- Publisher: Optimly (https://optimly.ai)
- Dataset: Optimly AI Brand Index (https://optimly.ai/brand)
- Official website: https://greptime.com/
- Logo: https://logo.clearbit.com/greptime.com
- Slug: greptime
- Brand Authority Index tier: Emerging
- Category: Time Series Databases
- Last Analyzed: September 22, 2026

## Buyer Intent Signals
Problems: Managing disparate observability data (metrics, logs, traces) across multiple backends | High storage costs for observability data | Complexity of cross-signal investigation due to different query languages | Scaling real-time monitoring and historical analysis separately | High operational overhead from deploying and managing multiple observability components | Manual correlation of observability signals by engineers | Challenges with high-cardinality telemetry from agentic applications
Solutions: Unified observability data management | Cost-effective storage for logs, metrics, and traces using object storage and compression | Simplified observability architecture with a single database | Flexible data ingestion via open protocols (OpenTelemetry, Prometheus Remote Write, Loki Push, Elasticsearch Bulk) | Powerful querying with SQL and PromQL for all observability data types | Scalable deployment on user infrastructure (from single node to Kubernetes clusters) | Automated retention, downsampling, and continuous aggregation of data | Support for mission-critical systems monitoring and analytics | Workload isolation and independent read capacity for enterprise needs
Comparisons: Comparing observability databases for metrics, logs, and traces | Evaluating solutions for reducing observability data storage costs | Seeking open-source observability database options | Assessing unified observability platforms | Considering migration from existing log/metrics management systems (e.g., Loki) | Evaluating database performance for high-cardinality telemetry and agentic applications | Analyzing options for scalable observability infrastructure running on owned environments