Exporter Resource Calculator
Estimate CPU and memory overhead added by running Prometheus exporters on a host.
About Exporter Resource Calculator
Exporters (node_exporter, cAdvisor, application-specific exporters, sidecar exporters) are lightweight individually but add up at fleet scale: total_CPU = exporters × CPU_per_exporter, total_memory = exporters × memory_per_exporter. At ~50 millicores and ~30 MiB per instance, a fleet of a few hundred exporters is usually a rounding error against total cluster capacity, but fleets in the tens of thousands can represent a meaningful dedicated resource allocation worth tracking explicitly.
Formula
total_memory_MB = exporters × memory_per_exporter_MB; total_cpu_millicores = exporters × cpu_per_exporter_millicores
- exporters
- Number of exporter instances
- memory_per_exporter_MB
- Memory per exporter (MB)
- cpu_per_exporter_millicores
- CPU per exporter (millicores)
Worked example
With the default values below, Exporter Resource Calculator returns Total Exporter CPU Overhead of 12.50. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Exporter Instancesaccepts 1–1,000,000 instances
- 250 instances
- CPU per Exporteraccepts 1–10,000 millicores
- 50 millicores
- Memory per Exporteraccepts 1–100,000 MiB
- 30 MiB
Results
- Total Exporter CPU Overhead
- 12.50 cores
- Total Exporter Memory Overhead
- 7.32 GiB
- Total CPU
- 12,500 millicores
- Total Memory
- 7,500 MiB
Step by step
- 1Total CPU: exporters × CPU/exporter= 12500m (12.50 cores)
- 2Total memory: exporters × memory/exporter= 7500MiB (7.32GiB)
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why do exporter resource needs vary so much between types?
A simple exporter like node_exporter mostly reads and re-exposes kernel/proc data with minimal processing, while exporters that maintain internal caches, connect to a database, or compute derived metrics (e.g. blackbox_exporter running active probes) can use substantially more CPU and memory per instance.
Should exporters have resource limits set?
Yes — even lightweight exporters should have Kubernetes resource requests/limits set explicitly, both to guarantee scheduling and to catch a misbehaving exporter (e.g. one leaking memory or stuck in a scrape loop) before it impacts co-located workloads.
Does scrape frequency affect exporter resource usage?
Slightly — more frequent scrapes mean the exporter's HTTP handler and metric-collection logic run more often, but for most simple exporters this effect is small relative to their baseline footprint unless the exporter does expensive work per scrape (e.g. re-reading large files).
How does this compare to the cost of the Prometheus server itself?
Exporter overhead is distributed across your fleet (many small allocations), while Prometheus server resource needs are concentrated (fewer, larger allocations) — both should be tracked, but they represent different parts of your monitoring stack's total resource budget.