Sizing Calculator

The APM Sizing Calculator helps you estimate the resource consumption of Application Monitoring before you deploy — or as you plan to scale. It models the three storage systems APM uses (span storage in OpenSearch, service map in OpenSearch, and RED metrics in Prometheus) based on your workload parameters.

Open the APM Sizing Calculator →

What it estimates

The calculator takes six inputs and produces storage, document count, and ingest rate estimates for each APM subsystem:

Span Storage (OpenSearch)

Spans are the raw building blocks of traces. Each span document is indexed into otel-v1-apm-span-* indices in OpenSearch.

Output	Formula
Spans / month	traces_per_month × avg_spans_per_trace
Retained spans	spans_per_month × (retention_days ÷ 30)
Storage	retained_spans × avg_span_size × 2.0 (the 2.0× factor accounts for OpenSearch index overhead — field mappings, inverted indices, doc values)
Ingest rate	spans_per_month ÷ 30 ÷ 86400 (spans/sec)

Service Map (OpenSearch)

Data Prepper’s service_map_stateful processor emits a document for every observed directed edge (service A → service B) every 180 seconds. These are indexed into otel-v2-apm-service-map-*.

Output	Formula
Directed edges	services × (services - 1) (worst-case: every service calls every other)
Docs / month	edges × (86400 ÷ 180) × 30
Storage	docs × 104 bytes × 2.0 (104 bytes is the measured average service map document size)

RED Metrics (Prometheus)

Data Prepper computes Rate, Error, and Duration metrics per service-operation pair and pushes them to Prometheus via OTLP. Each operation generates 16 time series: request_count, error_count, fault_count, plus ~12 histogram buckets with _sum and _count.

Output	Formula
Active series	services × ops_per_service × 16
Samples / month	series × (30 × 24 × 3600 ÷ 60) (one sample per series per minute)
Storage	samples × 2 bytes (Prometheus TSDB compression)

Totals

The calculator sums OpenSearch storage (spans + service map) and Prometheus storage separately, and computes a combined ingest rate in documents/sec.

Inputs

Parameter	Default	Range	Description
Traces (requests) / month	1.25M	100K – 1B	Total traces (requests) your applications generate per month
Avg span payload size	0.5 KB	0.1 – 50 KB	Average size of a single span document before indexing
Avg spans per trace	8	1 – 200	How many spans make up one trace on average
Number of services	10	1 – 1000	Distinct instrumented services in your environment
Avg operations per service	5	1 – 100	Distinct operation names (endpoints/handlers) per service
Retention period	15 days	1 – 365 days	How long span data is retained in OpenSearch

Key assumptions

• Index overhead 2.0× — OpenSearch stores field mappings, inverted indices, doc values, and segment metadata alongside raw documents. The 2× multiplier is a conservative estimate for span-shaped documents with many string attributes.
• Service map window 180s — Data Prepper’s service_map_stateful processor uses a default window of 180 seconds. One document per directed edge per window.
• Service map doc size 104 bytes — measured average from production otel-v2-apm-service-map-* indices.
• RED series = 16 per operation — includes request_count, error_count, fault_count, duration histogram buckets (~12), _sum, and _count.
• Prometheus compression ~2 bytes/sample — TSDB block compression ratio for typical time-series workloads.
• Worst-case service map — the calculator assumes every service communicates with every other service. Real topologies are typically sparser, so actual service map storage will be lower.

Tips for right-sizing

1. Start with spans/month — this is the dominant cost driver. Check your current OTel Collector metrics (otelcol_exporter_sent_spans) or Data Prepper metrics to get a baseline.
2. Measure your actual span size — run GET otel-v1-apm-span-*/_stats and divide store.size_in_bytes by docs.count for your real average.
3. Retention drives linear storage growth — doubling retention doubles span storage. Consider Index State Management (ISM) policies for automated rollover and deletion.
4. Service map is usually small — even with 100 services, service map storage is typically under 1 GB. Focus optimization efforts on span storage.
5. RED metrics are lightweight — Prometheus storage for APM metrics is typically a small fraction of total resource usage unless you have thousands of services with many operations each.