Estimating Resources

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When deploying **Managed ClickStack**, it is important to provision sufficient compute resources to handle both ingestion and query workloads. The estimates below provide a **baseline starting point** based on the volume of observability data you plan to ingest.

|  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Monthly ingest volume Recommended compute|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | < 10 TB / month 2 vCPU × 3 replicas|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | 10–50 TB / month 4 vCPU × 3 replicas|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | 50–100 TB / month 8 vCPU × 3 replicas|  |  |  |  | | --- | --- | --- | --- | | 100–500 TB / month 30 vCPU × 3 replicas|  |  | | --- | --- | | 1 PB+ / month 59 vCPU × 3 replicas | | | | | | | | | | | |

These recommendations are based on the following assumptions:

- Data volume refers to **uncompressed ingest volume** per month and applies to both logs and traces.
- Query patterns are typical for observability use cases, with most queries targeting **recent data**, usually the last 24 hours.
- Ingestion is relatively **uniform across the month**. If you expect bursty traffic or spikes, you should provision additional headroom.
- Storage is handled separately via ClickHouse Cloud object storage and isn't a limiting factor for retention. We assume data retained for longer periods is infrequently accessed.

More compute may be required for access patterns that regularly query longer time ranges, perform heavy aggregations, or support a high number of concurrent users.

Although two replicas can meet the CPU and memory requirements for a given ingestion throughput, we recommend using three replicas where possible to achieve the same total capacity and improve service redundancy.

Note

These values are **estimates only** and should be used as an initial baseline. Actual requirements depend on query complexity, concurrency, retention policies, and variance in ingestion throughput. Always monitor resource usage and scale as needed.

## Isolating observability workloads[​](#isolating-workloads "Direct link to Isolating observability workloads")

If you're adding ClickStack to an **existing ClickHouse Cloud service** that already supports other workloads, such as real-time application analytics, isolating observability traffic is strongly recommended.

Use [**Managed Warehouses**](/docs/cloud/reference/warehouses) to create a **child service** dedicated to ClickStack. This allows you to:

- Isolate ingest and query load from existing applications
- Scale observability workloads independently
- Prevent observability queries from impacting production analytics
- Share the same underlying datasets across services when needed

This approach ensures your existing workloads remain unaffected while allowing ClickStack to scale independently as observability data grows.

For larger deployments or custom sizing guidance, please contact support for a more precise estimate.

- [Isolating observability workloads](#isolating-workloads)

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