greptimedb
GreptimeDB is an open-source observability database that unifies metrics, logs, and traces into a single engine with SQL and PromQL query support, designed to replace Prometheus, Loki, and Elasticsearch. It uses columnar storage on object backends (S3, GCS, Azure) with tiered caching and claims up to 50× cost savings compared to traditional observability stacks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | GreptimeTeam/greptimedb |
| Owner | GreptimeTeam |
| Primary language | Rust |
| License | Apache-2.0 — OSI-approved |
| Stars | 6.5k |
| Forks | 503 |
| Open issues | 202 |
| Latest release | v1.1.2 (2026-07-02) |
| Last updated | 2026-07-08 |
| Source | https://github.com/GreptimeTeam/greptimedb |
What greptimedb is
Written in Rust, GreptimeDB runs in standalone or distributed modes (Frontend, Datanode, Metasrv, optional Flownode). It natively ingests OpenTelemetry, Prometheus remote write, and Jaeger; queries via SQL and PromQL; and separates compute from storage using object storage as primary with memory+disk caching. Columnar engine with fulltext, inverted, and skipping indexes targets both analytical and high-concurrency point queries.
Get the greptimedb source
Clone the repository and explore it locally.
git clone https://github.com/GreptimeTeam/greptimedb.gitcd greptimedb# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Distributed mode requires standing up Frontend (stateless, scale horizontally), Datanode (region engine, persists to object storage), Metasrv (metadata/routing), and optional Flownode (streaming); plan for etcd or RDS backing store for metadata.
- Object storage configuration (S3, GCS, Azure credentials, bucket/path policies) is mandatory in production; validate latency and cost assumptions for your cloud region and data access patterns.
- Migration strategy should account for signal-by-signal onboarding: ingest metrics first (via Prometheus remote write), then logs (via OTel/Loki adapters), then traces (via Jaeger); avoid big-bang cutover.
- Indexing strategy (fulltext, inverted, skipping indexes) requires tuning per table/column; defaults may not align with your cardinality profile; monitor index size and query performance during pilot.
- OpenTelemetry instrumentation is native but not automatic; agents (OTel SDKs, collectors) must be deployed separately; ensure SDK/collector versions are compatible with GreptimeDB ingest protocol version.
When to avoid it — and what to weigh
- Metrics-only or logs-only observability — If your stack is already optimized for single-signal observability (e.g., pure Prometheus), the unified approach adds no immediate benefit; consider migration complexity vs. incremental gains.
- Sub-millisecond SLO requirements on fresh data — While indexes are available, tiered caching (memory → disk → object storage) introduces latency variance; not suitable for ultra-low-latency telemetry ingest pipelines requiring deterministic <1ms response times.
- Zero operational tooling or vendor lock-in concerns — GreptimeDB is relatively young (created 2022, v1.1.2 current) with distributed mode requiring four components and metadata store (etcd/RDS); ops overhead is lower than Prometheus+Loki+ES but non-zero vs. fully managed SaaS.
- Extensive third-party integrations out-of-the-box — Ecosystem is narrower than Prometheus/Elasticsearch; while it supports OTel, Prometheus remote write, and Jaeger, integrations with custom agents, legacy log shippers, or niche APM tools require evaluation per use case.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license. Permits commercial use, modification, and distribution with minimal restrictions (retain license and copyright notices, list significant changes, provide license copy).
Apache-2.0 allows commercial deployment without license fees or vendor approval. However, this is the open-source project; Greptime (the company behind it) offers commercial support and likely a managed/SaaS offering. For production use, evaluate whether support SLAs, security certifications (SOC 2, ISO 27001), or uptime guarantees are required; requires vendor review. Self-hosted open-source carries no warranty or liability.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Apache-2.0 open-source does not guarantee security posture. Code is public and auditable (Rust reduces memory-safety bugs). No security audit, CVE history, or third-party attestation mentioned in provided data. Production deployments should: (1) isolate object storage credentials (IAM roles, temporary tokens); (2) enable TLS for gRPC/HTTP ingestion; (3) review Metasrv security (etcd/RDS access control); (4) audit multi-tenant isolation (if applicable). Requires security review before sensitive workloads.
Alternatives to consider
Prometheus + Grafana Loki + Elasticsearch
Best-of-breed per signal; mature ecosystems and large vendor support; significantly higher ops complexity (three stacks) and cost; no cross-signal queries in SQL; strong if you have existing deep expertise in each.
Grafana Mimir + Grafana Loki + Grafana Tempo
Unified UI via Grafana; high cardinality Prometheus alternative (Mimir); good logs/traces support; vendor-backed (Grafana Labs); less object-storage-native than GreptimeDB; can still require significant ops.
Managed SaaS (Datadog, Splunk, New Relic, Honeycomb)
Zero ops burden, integrated APM/SRE agents, built-in security/compliance certifications; no lock-in concerns (you own export); premium pricing; no direct SQL queries across signals; best for teams prioritizing time-to-value over cost.
Build on greptimedb with DEV.co software developers
GreptimeDB unifies metrics, logs, and traces in a single columnar engine. Start with a proof-of-concept using Docker, then migrate signals incrementally. For production deployments, engage Greptime for commercial support and managed SaaS options.
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greptimedb FAQ
Can I use GreptimeDB in standalone mode for production?
Do I have to migrate all three signal types (metrics, logs, traces) at once?
What is the cost model compared to Prometheus + Loki + ES?
Is GreptimeDB compliant with HIPAA, SOC 2, or PCI-DSS?
Software developers & web developers for hire
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Evaluate GreptimeDB for Your Observability Stack
GreptimeDB unifies metrics, logs, and traces in a single columnar engine. Start with a proof-of-concept using Docker, then migrate signals incrementally. For production deployments, engage Greptime for commercial support and managed SaaS options.