Observability Cost Calculator
Estimate total monthly observability spend across logs, metrics and traces, with a per-vendor pricing model comparison.
Inputs
Monthly log ingestion volume.
Vendor price per GB of log ingestion/retention.
Number of active time series being tracked.
Vendor price per 1,000 active series per month.
Monthly trace data volume after sampling.
Vendor price per GB of trace data.
Total Monthly Cost
$2,050.00
Projected Annual Cost
$24,600.00
Logs Cost
$1,000.00
Metrics Cost
$600.00
Traces Cost
$450.00
Logs Share of Spend
48.8%
Metrics Share of Spend
29.3%
Traces Share of Spend
22.0%
Step by step
Logs cost: volume × price/GB
2000 × $0.5
= $1000.00
Metrics cost: series ÷ 1000 × price
50,000 ÷ 1000 × $12
= $600.00
Traces cost: volume × price/GB
300 × $1.5
= $450.00
Total monthly: logs + metrics + traces
$1000.00 + $600.00 + $450.00
= $2050.00
How it works
Total observability spend is the sum of what each pillar costs under your vendor's pricing model: monthly = logs_cost + metrics_cost + traces_cost, where each is volume × unit price under that vendor's specific pricing dimension (per-GB for logs/traces, per-series for metrics). Because vendors price these very differently — some charge per-GB across all three pillars, others charge per-host or per-series for metrics — plugging in your specific vendor's rates lets you compare total cost of ownership across providers on equal footing.
Formula
monthly_cost = logs_GB_per_month × cost_per_GB_logs + metrics_series × cost_per_series + traces_GB_per_month × cost_per_GB_traces
- logs_GB_per_month
- Monthly log volume (GB)
- cost_per_GB_logs
- Cost per GB of log storage/ingestion
- metrics_series
- Active metric time series
- cost_per_series
- Cost per metric series
- traces_GB_per_month
- Monthly trace volume (GB)
- cost_per_GB_traces
- Cost per GB of trace storage
Frequently Asked Questions
Why do metrics get priced per-series instead of per-GB?
Metrics pricing is usually driven by cardinality (how many unique time series exist) rather than raw data volume, since each series requires ongoing storage and indexing regardless of how many data points it contains — high-cardinality labels (e.g. per-user or per-request-ID tags) can explode series count and cost.
How can I reduce observability costs without losing visibility?
Lower log verbosity in production (shift to sampling or structured summaries), reduce trace sampling rate, drop or aggregate high-cardinality metric labels, and set tighter retention windows on less critical data.
Which pillar usually dominates observability spend?
It varies by architecture, but logs are frequently the largest cost driver for verbose applications, while metrics cardinality dominates for microservice architectures with many labeled dimensions (per-pod, per-endpoint, per-customer).
Should I compare vendors using this same formula?
Yes — running the same volume/cardinality inputs through each vendor's actual per-unit pricing surfaces true cost differences, since list prices per GB or per series can vary by more than 2-3× between vendors for comparable data.
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