In the ever-evolving world of software development, observability enables software engineers to gain real-time insights into complex systems. OpenTelemetry and Prometheus are prominent Cloud Native Computing Foundation (CNCF) graduated projects but dissimilar observability tools for monitoring and debugging applications. This article examines the features, advantages, disadvantages, and major differentiators of OpenTelemetry and Prometheus. Software developers must understand the unique deliverables and use cases to decide which framework best suits their needs.

What is OpenTelemetry (OTel)?

OpenTelemetry is an open-source observability framework for instrumenting, collecting, and exporting telemetry data from software applications. It is a collection of specifications, SDKs, and libraries that help collect, transform, process, and export telemetry data. These telemetry data provide insights into application performance and health. OTel offers a vendor-agnostic model that provides libraries for various programming languages and frameworks and possibilities of exporting telemetry data to different vendor backends without changing the telemetry processors. Otel can be used with Jaeger, Grafana, Prometheus, DataDog, NewRelic, Last9, and numerous vendors, making it vendor-neutral.

OpenCensus, an open-source project offering libraries and tools for observability data collection, merged with OpenTracing, a standard for distributed tracing across diverse languages and systems, into OpenTelemetry. This unified project aims to standardize observability instrumentation and data collection. Gaining popularity, OpenTelemetry represents the future of observability, endorsed by numerous organizations and vendors.

Read more about what is OpenTelemetry.

How is OTel Built?

OpenTelemetry is built on standardized tools using a modular and extensible architecture. The following are some components of the OTel architecture.

APIs

OpenTelemetry provides language-specific APIs for popular programming languages like Java, JavaScript, Python, and Go. These APIs define developers' methods and interfaces to instrument their applications and generate telemetry data.

SDKs

Software Development Kits (SDKs) are implementation libraries built on OpenTelemetry APIs. They automatically capture telemetry data generated by instrumented applications and implement OTel APIs. The OpenTelemetry Collector receives processes, filters, and exports telemetry data in various formats.

Instrumentation Libraries

OTel offers a vendor-agnostic model of instrumenting applications that provides libraries that instrument popular frameworks and libraries. This eases the process of adding telemetry to applications built on various programming languages and frameworks, such as Spring and Express.js.

How Does OTel Work?

OTel enables you to add application instrumentation using the OpenTelemetry APIs and SDKs. This automatically directs system components to the specific logs, traces, or telemetry data you seek to gather, analyze and export. With OTel APIs, traces, logs, and metrics can be added to your code to ease data processing and export. Telemetry data processing involves filtering data for errors. Once this is done, the data is ready for export to a prespecified backend.

Features of OTel

OpenTelemetry offers a range of features, including the following.

Automatic Instrumentation

Automatic instrumentation allows software developers to initialize metrics, logs, and traces without tampering with application source codes.

Distributed Tracing

OpenTelemetry enables developers to trace transactions across different services within a distributed system. This makes it easy to understand front to back-end flow of requests and enables efficient error identification and resolution.

Metrics Collection

OpenTelemetry enables the collection and analysis of metrics from instrumented applications and represents the metrics in deltas rather than cumulatively. As such, it offers support for integer metric values, unlike Prometheus. Additionally, it allows you to attach extra metadata to histograms, enabling tracking of maximum and minimum values.

Read more about what is OpenTelemetry Collector.

Logging and Error Reporting

With OTel, you can log essential events and errors in your applications and export them to logging systems for further analysis.

Flexible Exporters

OpenTelemetry allows custom exporters to send telemetry data to different backend systems and observability platforms.

Why is OpenTelemetry Important?

OpenTelemetry plays a crucial role in modern observability practices by providing a standardized way of collecting and exporting telemetry data. Its ability to correlate metrics and traces simplifies troubleshooting and performance analysis.

Advantages of OpenTelemetry

The following are some essential benefits of OpenTelemetry.

1. OpenTelemetry’s standardized and easy-to-adopt approach to telemetry data collection makes for improved software observability.

2. OTel integrates seamlessly with various platforms and observability tools, including Prometheus.

3. OTel allows customization and extensibility by developing custom exporters, plugins, and instrumentation libraries. Its automated instrumentation libraries also reduce the effort and time required to instrument applications.

4. OpenTelemetry captures multiple layers of telemetry data, including traces, metrics, and logs.

5. OpenTelemetry supports programming languages, including Java, Python, JavaScript, and Go.

6. OpenTelemetry is backed by a solid and active community, including prominent industry experts. This ensures ongoing development, support, and incorporation of the latest advancements in observability practices.

Disadvantages of OpenTelemetry

1. OpenTelemetry's advanced features, such as its integrability, context propagation, distributed tracing, and custom exporters, make it a delicate-to-handle observability tool. As OTel enables the incorporation of its instrumentation code into the code of the application being monitored, in violation of the separation of concerns principle, it may require additional learning and expertise.

2. As an open-source tool, OpenTelemetry may require continuous maintenance and upgrades as new versions are released.

3. OTel's data collection and transmission processes consume system resources like CPU, memory, and network bandwidth. This may require additional resources, increase overhead, and impact performance.

What is Prometheus?

Prometheus is an open-source monitoring and alerting toolkit developed at SoundCloud designed to collect, process, and visualize metrics from various applications, using a flexible query language called PromQ to gain insights into application health and performance.

Read more about what is Prometheus.

How is Prometheus Built?

Prometheus is built as a standalone platform. Here are some critical components of the Prometheus architecture.

Programming Language

Prometheus is primarily written in the Go programming language (Golang), which balances performance and development productivity well.

Time-Series Database

Prometheus uses a custom-built time-series database (TSDB) to store and query collected metrics. The TSDB is optimized for fast and efficient time-series data ingestion, storage, and retrieval.

Pull-Based Data Model

Prometheus adopts a pull-based model, periodically scraping metrics from instrumented targets. It supports various scraping protocols, including HTTP, HTTPS, and DNS.

PromQL

Prometheus’ flexible query language, PromQL, supports various functions, such as creating custom aggregation functions, for manipulating and querying time-series data. PromQL also facilitates complex queries, such as filtering metrics by labels and performing mathematical operations.

Monitoring and Alerting

Prometheus utilizes a web-based graphical visualization dashboard called the Prometheus Expression Browser for monitoring and troubleshooting. It also provides built-in alerting capabilities, allowing users to define alert rules based on specific thresholds. Prometheus can send alerts via various notification channels, such as email or Slack.

Exporters

Prometheus supports a rich ecosystem of exporters that collect application-specific metrics and export them in a format that Prometheus can scrape.

How Does Prometheus Work?

When Prometheus is configured with targets, such as application servers, databases, or exporters, it periodically scrapes metrics from the configured targets using various protocols. By default, it scrapes metrics every 15 seconds, but this interval can be re-configured. During the scraping process, Prometheus collects application-specific, system-level, or custom-defined metric data, such as CPU usage, memory usage, request latency, or any other relevant metric, from the targets. It then stores the collected metrics in its time-series database, which organizes them based on unique metric names, labels, and timestamps.

Afterward, you can define and analyze complex queries with PromQL, apply functions and aggregations, and filter collected data to retrieve the desired information. Prometheus responds to these queries with the requested metrics and sends alerts of events to your notification channels based on your predefined threshold.

Features of Prometheus

Prometheus offers the following features.

Service Discovery and Target Management

Prometheus offers service discovery mechanisms to automatically discover and monitor new instances of services as they come online. It can integrate with service discovery systems like Kubernetes, Consul, and EC2.

Robust Querying

With PromQL, you can retrieve and analyze metrics, including functions, aggregations, and operators, using a flexible syntax. PromQL supports a range of operations for manipulating and querying time-series data, allowing software developers to create custom dashboards and alerts.

Alerting and Notification

Prometheus has a built-in alerting system that allows you to define alert rules based on specific conditions or thresholds. Prometheus generates and sends alerts via various notification channels when an alert condition is met.

Data Visualization

The Prometheus Expression Browser allows users to visualize metrics, create graphs, and explore data. The interface provides interactive features for zooming, panning, and applying various graphical options.

Hierarchical Federation

Prometheus servers are generally capable of monitoring large numbers of software components. But to make observability more cost-effective, Prometheus offers a hierarchical federation feature that allows software developers to configure a single high-level Prometheus server to collect metrics from multiple low-level servers.

Integration

Prometheus integrates with various tools and systems, such as alert managers, visualization platforms, and time-series databases.

Why is Prometheus Important?

Prometheus is critical to software developers monitoring application events in real time. It is essential to embed Prometheus during software development. Doing this will help you understand how different data types and software infrastructures intersect and interact without needing individual examination of each architecture.

The end goal of monitoring and collecting frontend user-facing and backend performance data is to cut application errors, improve latency and boost the client base. End-users expect applications to work swiftly, correctly, and efficiently. When applications perform inefficiently, users often migrate to alternative platforms, leading to revenue losses for corporate organizations.

Observability tools like Prometheus are deployed to study functional metrics that reveal backend system functions such as error rates and latency to avoid this. These data are then mapped to individual infrastructures to solve software issues.

Apart from its general functions as an observability platform, Prometheus has two distinct features: it provides short-term storage and a visualization layer. These distinguishing features are advantages that OTel does not offer.

Advantages of Prometheus

Six key benefits of Prometheus are itemized below.

1. Prometheus is easy to set up and configure, requiring minimal overhead.

2. PromQL allows developers to perform complex queries, aggregations, and calculations on collected metric data.

3. Prometheus' built-in alerting system allows you to define alert rules and receive notifications when the set rules and thresholds are met.

4. Prometheus’ federation allows for easy horizontal scalability as monitoring needs grow.

5. It has a vibrant ecosystem with many exporters and integrations.

6. Prometheus allows for flexible and efficient storage, retrieval, and analysis of time-series data.

Disadvantages of Prometheus

Below are some critical drawbacks of Prometheus.

1. Prometheus relies on scraping metrics data from targets, which may introduce long-term delays and scalability challenges, especially in heavily loaded or distributed environments.

2. Prometheus focuses primarily on metrics collection and does not provide native support for distributed tracing.

3. While Prometheus is built to handle large-scale deployments, it may face challenges with long-term data storage. Though it retains data for a configurable retention period, users need to consider external solutions for historical data storage, making it a resource-intensive monitoring solution.

4. Prometheus focuses primarily on metrics-based monitoring, so users have to select other tools for traces and logs.

OTel vs. Prometheus High-Level Comparison

The table below presents some crucial differences between OTel and Prometheus.

Differentiators	OpenTelemetry	Prometheus
Storage	Has no storage solution. Offers exporters that can be deployed to send metrics to preconfigured backend systems.	Has a short-term storage solution.
Data Collection Model	Adopts a push-based model and instrumentation standard, allowing applications and services to actively send telemetry data to collectors or exporters using various SDKs.	Uses a pull-based model where it actively scrapes metrics from instrumented targets at specified intervals.
Metric Types	Supports various types of telemetry data, including metrics, traces and logs.	Supports only metric types like counters, gauges and histograms. Can only monitor logs and traces if exporter servers are configured.
Data Protocol and Format	Uses a standard and vendor-agnostic protocol called OpenTelemetry Protocol (OTLP) for transmitting telemetry data.	Uses its own text-based data format for storing and transmitting metric data.
Standardization	It is a community-driven project with expert contributors from various organizations, making it a highly standardized observability platform.	While widely used, it is primarily maintained by the Prometheus community.
Extensibility	Offers SDKs and auto-instrumentation capabilities for multiple programming languages and frameworks, making it highly extensible.	Has limited extensibility.

Metrics in OpenTelemetry vs. Prometheus

The semantic convention for metrics in OpenTelemetrythe (OTLP metrics) does not align with Prometheus' native metrics naming convention. This means that metrics in OpenTelemetry and Prometheus do not have the same format and specification.

Check out following video to understand differences between OpenTelemetry Metrics and Prometheus

To address this disparity, there is a module in otel-collector-contrib that offers centralized functions that facilitate the conversion of OpenTelemetry metrics into metrics compliant with Prometheus.

Recently, Prometheus has also started adding support for ingesting OpenTelemetry metrics natively.

Here is a comparison of metrics in Prometheus and OpenTelemetry.

Metric Feature	OpenTelemetry	Prometheus
Metric Types	Supports time-series metrics but provides broader support for other types of telemetry data, making OTel a more comprehensive tool.	Focuses primarily on time-series metrics.
Data Format	Adopts an OpenMetrics protocol.	Uses its own text-based data format for metric data storage and transmission.
Data Schema	Allows for richer and more customizable schemas and provides the ability to attach additional contextual information or metadata such as labels to metrics.	Has a predefined data schema for metrics, including metric names, labels and values, ensuring consistency in metrics querying and organization.
Semantic Conventions	Defines semantic conventions as guidelines for representing metrics in a standardized way, based on best practices, ensuring consistency in naming and metadata across different systems and services.	Does not enforce specific semantic conventions but relies on developers to define their own naming conventions.

Summary

Choosing the proper observability framework for your specific application and infrastructure needs is critical. While OpenTelemetry and Prometheus are potent tools, OpenTelemetry provides a more comprehensive approach to observability across multiple platforms and languages, supporting metrics and distributed tracing. An in-depth comprehension of the highlighted capabilities and differences will allow you to make informed decisions and leverage the proper framework for your observability needs.