Architectural Guidance
Loki or ClickHouse? Choosing the Right Log Engine
One size does not fit all. We help you choose between the metadata-indexed efficiency of Grafana Loki and the raw analytical power of ClickHouse.
| Feature | Grafana Loki | ClickHouse |
|---|---|---|
| Best Use Case | Cloud-native log aggregation & troubleshooting. | Large-scale analytics, security forensics, & tracing. |
| Indexing Strategy | Metadata/Labels only (like Prometheus). | Columnar indexing (Full SQL capabilities). |
| Storage Cost | Ultra Low (S3/GCS optimized) | Low (Excellent compression, higher CPU needs) |
| Query Language | LogQL (Functional) | SQL (Relational) |
Our Selection Framework
How we determine the best fit for your infrastructure during our audit.
Step 1: Audit Patterns
Do you search by TraceID (Loki) or aggregate over billions of events (ClickHouse)?
Step 2: Scale Sizing
We calculate the TCO based on your daily TB ingestion and retention policy.
Step 3: Integration
We map your OTel Collector pipelines to the chosen destination engine.
The Hybrid Approach
For many enterprise clients, we implement both.
- Loki handles ephemeral application logs for dev/staging (7-day retention).
- ClickHouse stores long-term traces and audit logs for compliance and forensics.
// OTel Routing Logic
exporters:
loki:
endpoint: "http://loki:3100"
clickhouse:
endpoint: "tcp://clickhouse:9000"
service:
pipelines:
logs/dev:
exporters: [loki]
logs/audit:
exporters: [clickhouse]Unsure which engine to pick?
Our experts will analyze your log signatures and data volume to build the most cost-effective architecture for your needs.