CHALLENGER
Grafana Tempo
Traces only · requires Prometheus + Loki for full observability · TraceQL
GREPTIMEDB
GreptimeDB
Metrics, Logs & Traces in one database · SQL + PromQL · single deployment
Skip to content 
Traces solved. Metrics and logs?
Still two more systems to run.
Grafana Tempo is a distributed tracing backend developed by Grafana Labs, designed to store and query trace data at scale without requiring sampling. It uses object storage (S3, GCS, Azure) for cost-effective long-term retention and supports Jaeger, Zipkin, and OpenTelemetry protocols. Tempo is designed to work alongside Prometheus (metrics) and Loki (logs) — meaning a full observability stack requires operating three separate systems.
Feature comparison
| Feature/Aspect | GreptimeDB | Grafana Tempo |
|---|---|---|
| Data Model | Metrics, Logs & Traces in one database | Distributed Tracing Backend |
| Value Model | Multi-Value (supports complex data structures) | Trace spans with attributes |
| Multi-model Support | Metrics, Logs & Traces in one database | Traces only (requires separate systems for metrics/logs) |
| Query Languages | SQL & PromQL (dual interface) | TraceQL |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry Loki Push API Elasticsearch Bulk API HTTP API | Jaeger Zipkin OpenTelemetry OTLP |
| Data Retention | Flexible TTL policies with tiered storage | Object storage-based retention with compaction |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Service maps and span metrics generation |
| Deployment Complexity | Single system deployment | Multi-component deployment (Distributor, Ingester, Querier, Compactor) |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | Distributed tracing, request flow analysis, latency troubleshooting |
| Architecture | Cloud-native distributed with compute-storage disaggregation | Microservices architecture with object storage backend |
| Storage Format | Apache Parquet (columnar, compressed) | Parquet files in object storage |
| Storage Scalability | Object storage integration with unlimited capacity | Native object storage design for unlimited scale |
| High Availability | Native clustering with automatic failover | Stateless components with object storage persistence |
| License | Apache 2.0 | Apache 2.0 |
| Written Language | Rust (memory safety, performance) | Go (ecosystem compatibility) |
| Deployment Options | Single-node, cluster, Kubernetes-native, edge-to-cloud with unified API | Microservices mode, scalable mode with object storage |
| Operational Complexity | Single unified system with simplified Kubernetes operations | Requires coordination with Prometheus and Loki for full observability |
Agent RCA Bench. In 504 end-to-end root cause investigations — six models, 14 incidents, model and prompt held fixed, the complete interface bundle as the variable — querying Prometheus, Loki, and Tempo through their native APIs produced 105 correct diagnoses out of 168, against 130 through GreptimeDB, at about 48% less input read. Storage, query language, and tool design change together, so the comparison does not isolate any one of them. Diagnosis accuracy was not a pre-specified endpoint and is reported as descriptive; the pre-specified endpoints and their separate correction families are in the report. Scoring uses no LLM judge. Greptime sponsors and maintains the benchmark, and the code and artifacts reproduce the published results byte for byte. See the full report and the writeup.
All Comparisons
Side-by-side feature breakdowns for additional alternatives.
Stay in the loop
Join our community
Get the latest updates and discuss with other users.