**Prometheus **

OpenTelemetry

Core features

Complete metrics platform with built-in time-series storage, PromQL query engine, and scraping architecture

Telemetry collection framework providing standardized APIs, SDKs, and a collector for unified metrics, traces, and logs

Data collection

Pull-based metrics collection, backend that scrapes endpoints from your applications, and exporters on a scheduled interval

Flexible telemetry collection that supports pushing, pulling, processing data, and exporting to multiple backends, including Prometheus

Implementation approach

Implementation decisions are mostly regarding built-in components: configuring scraping, storage, and querying within Prometheus itself

Implementation decisions span multiple systems: instrumentation setup, collector pipeline, and backend coordination

Data correlation

Manual correlation of metrics using labels and timestamps

Built-in correlation, with every trace span, including related metrics and logs with shared context

Ecosystem

An integrated ecosystem with established tooling and extensive community knowledge

A distributed ecosystem with growing vendor support requiring backend selection and component assembly

Resource requirements

Resource planning usually starts with a single statically linked binary and collector, and adds federation and remote storage complexity when scaling

Resource planning across multiple systems from the start, collectors, storage backends, and visualization with different scaling characteristics

SLO data quality

Strong foundation for infrastructure SLOs, business SLOs across multiple services, limited by metrics-only data model; requires greater instrumentation effort to get right.

Benefits for business SLOs from automatic trace correlation across multiple services and the ability to handle infrastructure SLOs equally well

Ecosystem

Prometheus benefits from years of community-built tooling around a single cohesive monitoring system with well-established patterns. Grafana dashboards, AlertManager configurations, and exporters like node_exporter for infrastructure metrics have extensive documentation. When you need to monitor MySQL, there’s a standard mysqld_exporter with proven dashboard templates and alerting rules that thousands of teams have used. Similar exporters exist for most applications and infrastructure.

OpenTelemetry**,** like Prometheus, also has strong community documentation and backing from major cloud providers (Google, Microsoft, AWS) and observability companies. These vendors offer separate backends, unlike Prometheus’s integrated approach. The OTLP standard allows collectors to ingest from legacy systems and export to multiple backends. However, assembling your stack requires researching compatibility and integration requirements for your specific combination.

The table below shows an example framework for such research:

Component	Key questions	Complexity factor
Trace backend	Can it handle trace volume? Does it support your sampling techniques?	Version compatibility and sampling configuration
Metrics storage	Does the setup support self-hosted solutions as well as platforms like Datadog?	Multi-backend routing and format difference
Log aggregator	Will log aggregation work across all services, and does it support the trace backend?	Context propagation and query capabilities
Visualization	Can the visualization layer connect all the backends, or do we need separate tools for each backend?	Data source configuration and data correlation capabilities