OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

Monitoring and optimizing application performance is important for software developers and enterprises at large. The more applications that an enterprise deploys, the more data that exists for collecting and analyzing. Yet, this data isn’t worth much without the right tools for monitoring, optimizing, storing and—crucially—putting the data into context.

Organizations can make the most of application data by deploying monitoring and observability solutions that help improve application health by identifying issues before they arise, flagging bottlenecks, distributing network traffic and more. These features help reduce application downtime, provide more reliable application performance and improve user experience.

OpenTelemetry and Prometheus are both open-source projects under the Cloud Native Computing Foundation (CNCF) that offer observability tools for application monitoring. Different types of data and operations require distinct solutions that depend on an organization’s goals and application specifications. Understanding the key differences between platforms like OpenTelemetry and Prometheus and what each solution offers, is important before you choose one for implementation.

It is also valuable to note that OpenTelemetry and Prometheus integrate and can work together as a powerful duo for monitoring applications. OpenTelemetry and Prometheus enable the collection and transformation of metrics, which allows DevOps and IT teams to generate and act on performance insights.

What is OpenTelemetry?

OpenTelemetry or OTel, is a platform that is designed to create a centralized location for generating, collecting, exporting and managing telemetry data, including logs, metrics and traces. OTel was born from the merger of OpenCensus and OpenTracing with the goal of providing APIs, SDKs, libraries and integrations that standardize the collection of disparate data. With OTel, the wanted monitoring outputs can be built into your code to simplify data processing and make sure that data is exported to the appropriate back end.

Analyzing telemetry data is key in understanding system performance and health. This type of optimized observability allows organizations to troubleshoot faster, increase system reliability, address latency issues and reduce application downtime.

Here’s a quick break down the key aspects of the OpenTelemetry ecosystem:

APIs: OpenTelemetry APIs (application programming interfaces) universally translate programming languages. This capability enables the APIs to collect telemetry data. These APIs play a key role in standardizing the collection of OpenTelemetry metrics.

SDKs: Software development kits are tools for building software. They include the framework, code libraries and debuggers that are the building blocks of software development. OTel SDKs implement OpenTelemetry APIs and offer the tools that are needed to generate and collect telemetry data.

OpenTelemetry collector: The OTel collector receives, processes and exports, telemetry data. OTel collectors can be configured to filter specific data types to the designated back end.  

Instrumentation library: OTel provides an instrumentation model that runs on all platforms. The instrumentation libraries make it possible for OTel to integrate with any programming language.

Benefits of OpenTelemetry

The OpenTelemetry protocol (OTLP) simplifies observability by collecting telemetry data, like metrics, logs and traces, without changing code or metadata.

Metrics: Metrics define a high-level overview of system performance and health. Developers, IT and business management teams determine what metrics are most useful to track to maintain a level of application performance that meets business objectives. Metrics vary depending on the data that a team deems important and can include network traffic, latency and CPU storage. Metrics can also be used to track patterns and trends in application performance.

Logs: Logs are a record of events that occur within a software or application component. Logs can be created around specific aspects of a component that DevOps teams want to monitor. They serve as historical data that can present general performance information, show when set thresholds are surpassed, or display errors. Logs help monitor the overall health of an application ecosystem.

Traces: Traces offer a more zoomed out view of application performance than logs and help with optimization. They are also more focused than logs and follow the end-to-end journey of a single request as it moves through the application stack. Traces allow developers to find the exact moment errors or bottlenecks occur, how long they last and how they affect the user journey. This information helps manage microservices and improve overall application performance.

OTel can take these three different types of telemetry data and export them to various back ends, including Prometheus. This capability prevents vendor or back-end lock-in and allows developers to choose their preferred analysis tools. OpenTelemetry supports a range of integrations with other platforms, including Prometheus, which provide greater opportunities for observability. OTel supports Java, Python, JavaScript and Go, making it an increasingly flexible solution. It also allows developers and IT teams to monitor performance from any web browser or location.

The greatest strengths of OpenTelemetry come from its ability to consistently collect and export data across many applications and its standardization of the collection process. OTel is a powerful tool for observability into distributed systems and microservices.

What is Prometheus?

Prometheus is a toolkit for monitoring and alerting that was created to collect and organize application metrics. The Prometheus server was originally developed at SoundCloud before it became an open-source tool.

Prometheus is a time-series database for end-to-end monitoring of time-series data. Time-series metrics are a collection of data that is taken at regular intervals such as monthly sales data, or daily application traffic. Clear visibility into this type of data offers insights into patterns, trends and predictions for business planning. Once integrated with a host, Prometheus gathers application metrics that are related to dedicated functions that DevOps teams want to monitor.

Prometheus metrics provide data points that consist of the metric name, label, timestamp and value by using a query language called PromQL. PromQL allows developers and IT departments to aggregate data metrics and turn them into histograms–graphs and dashboards for greater visualization. Prometheus can access data from enterprise databases or from exporters. Exporters are software that is related to applications that work to pull metrics from various apps and endpoints.

Prometheus collects four types of metrics:

Counters: Countersmeasure cumulative numerical values that only increase. Counters are used to measure completed tasks, the number of errors that occurred during a defined period, or the number of running processes or microservices.

Gauges: Gauges monitor numerical values that rise and fall depending on external factors. They can monitor CPU and memory usage, temperature, or the size of a queue.

Histograms: Histograms measure the duration of specified events such as request duration or response size. They then divide the range of these measurements into intervals that are called buckets and determine how many of these measurements fall into each respective bucket.

Summaries: Like histograms, summaries also measure request durations and response size, but also provide a total count of all observations and a total of all observed values.

Another valuable aspect of Prometheus is that it can create accessible dashboards and graphs based on the collected data.

Benefits of Prometheus

Prometheus enables real-time application monitoring that gives you accurate insights and facilitates quick troubleshooting. It also allows for the creation of thresholds that are related to specific functions. When these thresholds are met or surpassed, it triggers alerts that can reduce the time that it takes to resolve issues. Prometheus can handle and store large volumes of metrics data and make the data available for analytics teams as needed. It is not intended to be a long-term storage solution but a tool for storing data that is needed for immediate analysis. The standard window for data storage with Prometheus is between two hours and fifteen days.

Prometheus seamlessly integrates with Kubernetes, an open-source container orchestration platform for scheduling and automating the deployment, management and scaling of containerized applications. Kubernetes allows enterprises to build complex hybrid and multicloud environments that deploy a range of services and microservices. Integrating Prometheus with Kubernetes brings full-stack observability and oversight into these complex systems.

Prometheus is also compatible with Grafana, a powerful visualization tool that helps transform data into dashboards, charts, graphs and alerts. When paired with Prometheus, Grafana can take metrics and create clear visualizations. The compatibility between these two platforms makes complex data more accessible and sharable among different teams. 

Key differences between OpenTelemetry and Prometheus

Prometheus offers tools for metrics monitoring, storage and visualization, but does not track logs or support traces, which are used for root cause analysis. Overall, Prometheus has more limited use cases than OpenTelemetry.

OpenTelemetry can process and trace more complex metrics than Prometheus through programming language-agnostic integrations. OTel is highly scalable and has greater extensibility than Prometheus by offering automated instrumentation models. Unlike Prometheus, OpenTelemetry does not offer a storage solution and must be paired with a separate back-end system.

A quick breakdown:

• Prometheus can measure cumulative metrics, giving you a sum, while OpenTelemetry can represent metrics as deltas.
• Prometheus provides short-term data and metrics storage while OTel does not natively support storage but can be paired with a separate storage solution.
• OpenTelemetry collects metrics, logs and traces by using a consolidated API via push or pull, and translates them into a common language, which Prometheus cannot achieve. Prometheus gathers metrics by pulling data from hosts and is primarily concerned with collecting and storing time-series metrics.
• OTel is language agonistic and can translate metrics, giving developers more flexibility. Prometheus uses PromQL to aggregate data and metrics.
• Prometheus provides web visualization for monitoring metrics coupled with customizable alerts. OpenTelemetry must be integrated with separate tools for visualization.
• OTel allows metric values to be expressed as integers rather than floating-point numbers, which provide more accurate value representations and are easier to understand. Prometheus cannot express metrics as integers.

Your organization’s needs will dictate which of these solutions is right for you. If you need a more holistic understanding of your data, are working in complex environments with distributed systems, and want more flexibility, OpenTelemetry might be a more appropriate solution. This is also the case if you need to monitor logs and traces.

If you need to monitor individual systems or operations, and are looking for alerting, storage and visualization models, Prometheus might be the right option.

OpenTelemetry and Prometheus integration

The good news is that you don’t necessarily have to choose one or the other; OpenTelemetry and Prometheus are compatible platforms. OTel SDKs can collect metrics from Prometheus data models and Prometheus supports OpenTelemetry metrics. Using these platforms together gives you the best of both worlds and advanced monitoring options. For example:

• When coupled, OTel and Prometheus provide monitoring into complex systems with real-time insights into your application environments.
• You can pair OTel’s tracing and monitoring tools with Prometheus’ alerting capabilities.
• Prometheus can handle large volumes of data. This feature coupled with OTel’s ability to consolidate metrics, traces and logs into a single interface creates greater efficiency when scaling systems and applications.
• PromQL can analyze the data that is collected from OpenTelemetry’s data captures and use it to create visualization models.

In addition, OpenTelemetry and Prometheus integrate with IBM® Instana and IBM® Turbonomic to offer additional monitoring tools. With Instana’s powerful dependency map, upstream/downstream service correlation and full-stack visibility, OTel’s capabilities are optimized to make sure that all services are instrumented. Instana delivers the same great experience with OTel data as it provides for every other data source, giving you the context that you need to quickly find and fix application issues. With Turbonomic, you can use Prometheus’ data monitoring tools to automate resourcing decisions based on real-time data collection. These integrations are optimized ways to promote the health of your application ecosystem and improve overall performance.

Explore IBM Instana OpenTelemetry

Explore Prometheus integration with IBM Turbonomic