Opentelemetry vs Prometheus: Everything You Need to Know?

Observability has become a cornerstone for maintaining performance, availability, and reliability in cloud-native applications. As systems grow more complex, particularly with the adoption of microservices and distributed architectures, understanding how applications behave in real-time is crucial. 

Observability tools help developers and operations teams track system health, troubleshoot issues, and optimize performance.

Two of the most prominent tools in this space are OpenTelemetry and Prometheus. Both are open-source projects under the Cloud Native Computing Foundation (CNCF) and play significant roles in observability ecosystems. 

However, they serve different purposes, while Prometheus focuses on metrics collection and monitoring, OpenTelemetry offers a broader approach by handling metrics, traces, and logs.

This article examines the differences between OpenTelemetry vs Prometheus, exploring how they compare, integrate, and complement each other. We’ll also look into how they stack up against other tools like Grafana, Jaeger, OpenTracing, and ELK, guiding you to choose the right observability solution for your specific needs.

OpenTelemetry vs Prometheus: Comparison Table

Feature	OpenTelemetry	Prometheus
Primary Purpose	Telemetry data collection (metrics, traces, logs)	Metrics monitoring (time-series data collection, storage, querying)
Scope	Multi-signal: Metrics, Traces, Logs	Single-signal: Metrics only
Data Collection	Uses client libraries and automatic instrumentation	Uses exporters and target scraping
Data Types Supported	Metrics, Distributed Traces, Logs	Time-series Metrics
Storage & Querying	No native storage; exports data to various backends (e.g., Prometheus, Jaeger, ELK)	Includes time-series database and PromQL for querying
Visualization	No built-in visualization; integrates with Grafana, Jaeger, Kibana, etc.	Basic web UI; commonly used with Grafana for advanced dashboards
Query Language	Depends on backend (e.g., PromQL, Elasticsearch queries)	PromQL (Prometheus Query Language)
Interoperability	Can export data to Prometheus, Jaeger, ELK, etc.	Can scrape data from OpenTelemetry-instrumented applications
Scalability	Designed for large-scale, distributed systems	Scales well with small to medium clusters; needs Thanos/Cortex for large-scale
Vendor Neutrality	Vendor-agnostic; integrates with multiple backends	Primarily self-contained but can integrate with other tools
Use Case	Comprehensive observability (multi-signal correlation)	Metrics-focused monitoring
Ideal For	Complex microservices, polyglot architectures, and teams needing full observability	Infrastructure & application metrics monitoring, especially in Kubernetes
Learning Curve	Moderate to High (due to multi-signal scope and complex setups)	Low to Moderate (simpler, metrics-only focus)
Maturity & Stability	Rapidly growing ecosystem; still maturing in some areas	Highly mature and battle-tested
Popular Integrations	Jaeger, Prometheus, Grafana, ELK	Grafana, Thanos, Cortex
Best Suited For	Teams needing traces, logs, and metrics in a unified framework	Teams focused solely on metrics monitoring

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What is OpenTelemetry?

OpenTelemetry is an open-source observability framework designed to collect telemetry data, including metrics, traces, and logs, from applications. 

Managed by the Cloud Native Computing Foundation (CNCF), OpenTelemetry was formed by merging two previous projects: OpenCensus and OpenTracing. Its primary goal is to standardize the way telemetry data is generated and collected, offering a vendor-neutral approach that simplifies observability across diverse systems.

OpenTelemetry metrics capture quantitative data points about system performance, such as response times, request rates, and error counts. But OpenTelemetry goes beyond metrics by also handling distributed traces and logs, enabling developers to gain holistic insights into their applications.

Key features of OpenTelemetry include:

• Multi-signal Support: Unlike Prometheus, which focuses solely on metrics, OpenTelemetry supports metrics, traces, and logs, enabling comprehensive observability.
• Pluggable Architecture: OpenTelemetry uses a vendor-neutral, modular design, allowing seamless integration with a wide range of backends like Jaeger, Prometheus, Grafana, and ELK.
• OpenTelemetry Collector: A core component, the OpenTelemetry Collector, acts as an agent to collect, process, and export telemetry data to various analysis tools. It supports integrating Prometheus data through dedicated receivers, enabling a combined observability approach.
• Automatic and Manual Instrumentation: OpenTelemetry offers client libraries for various languages (e.g., Java, Python, Go) that developers can use to instrument their applications. It supports both manual code instrumentation and automatic instrumentation, making it flexible for different development workflows.

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What is Prometheus?

Prometheus is an open-source time-series metrics monitoring tool, widely adopted for tracking system and application performance. Initially developed by SoundCloud and now a CNCF project, Prometheus has become a cornerstone in cloud-native monitoring, particularly within Kubernetes ecosystems.

Prometheus specializes in:

• Metrics Collection: Prometheus scrapes metrics from configured targets at regular intervals. It collects and stores data as time-series, where each data point includes a metric name, labels, a timestamp, and a value.
• PromQL: One of Prometheus’s standout features is PromQL—a powerful query language designed specifically for working with time-series data. It allows users to create complex queries for monitoring and alerting.
• Built-in Storage and Visualization: Prometheus comes with its own time-series database and a basic web UI for querying and visualizing data. However, it’s often paired with Grafana for more sophisticated dashboards (OpenTelemetry vs Prometheus vs Grafana is a common setup for observability).
• Alerting and Scalability: Prometheus includes an Alertmanager to trigger alerts based on metric thresholds. For large-scale deployments, tools like Thanos or Cortex can extend Prometheus’s scalability and durability.

While Prometheus excels at metrics monitoring, it doesn’t natively support traces or logs, this is where OpenTelemetry offers a broader scope.

OpenTelemetry vs Prometheus: Core Differences

When evaluating OpenTelemetry vs Prometheus, it’s essential to understand how these tools differ in their scope, architecture, and use cases. While they both play significant roles in observability, their focus and capabilities vary considerably.

Scope

The most apparent distinction between OpenTelemetry and Prometheus lies in their scope.

• OpenTelemetry offers a comprehensive observability framework, covering metrics, traces, and logs. It acts as a data collection pipeline, focusing on generating and transmitting telemetry data rather than storing or visualizing it.
• Prometheus, on the other hand, is a dedicated time-series metrics monitoring tool. Its sole focus is collecting, storing, and querying metrics, without native support for traces or logs.

If your goal is holistic observability across multiple signal types, OpenTelemetry provides the tools to gather all the necessary data. However, if you’re focusing purely on metrics monitoring, Prometheus offers a more straightforward solution.

Data Types

The difference in supported data types further highlights the tools’ distinct purposes.

• OpenTelemetry metrics can represent various data forms, including delta and cumulative metrics, and supports advanced representations like exponential histograms. In addition to metrics, it also gathers traces (for distributed transaction tracking) and logs (for event data).
• Prometheus exclusively handles time-series metrics. Each data point consists of a metric name, labels, a timestamp, and a value. Prometheus metrics are generally cumulative, with changes calculated using PromQL during querying.

This broader support in OpenTelemetry allows for better correlation between different observability signals, such as linking spikes in latency (metrics) to specific requests (traces) and their associated logs.

Architecture & Flexibility

The architecture of these tools reflects their intended purposes.

• OpenTelemetry follows a vendor-neutral, pluggable design. It doesn’t provide its own storage or querying layer but instead focuses on collecting telemetry data and forwarding it to various backends. Through the OpenTelemetry Collector, users can export data to tools like Prometheus, Jaeger, Grafana, or even ELK, creating flexible observability pipelines.
• Prometheus is a self-contained system. It includes its own time-series database and a built-in querying language (PromQL) for data analysis. While Prometheus can export data to long-term storage systems like Thanos or InfluxDB, its core design revolves around being a complete solution for metrics monitoring.

This difference makes OpenTelemetry more adaptable for organizations that want to mix and match observability tools, while Prometheus offers a simpler, out-of-the-box solution for metrics.

Scalability

When it comes to scalability, the two tools approach the challenge differently.

• OpenTelemetry is designed for large-scale, distributed systems. It can handle complex microservice architectures, collecting telemetry from multiple languages and frameworks. Its Collector component allows for scaling horizontally and vertically, ensuring that even high-traffic environments can be observed effectively.
• Prometheus works well in single-node or small-cluster environments but faces challenges at larger scales. While it can scrape thousands of metrics per second, long-term scalability often requires additional tools like Thanos or Cortex to federate data and provide global views.

For organizations managing large, distributed systems, OpenTelemetry may offer more flexibility and scalability out of the box, while Prometheus remains a solid choice for focused metrics monitoring in smaller or medium-scale environments.

Interoperability

One of the key strengths of modern observability tools is their ability to integrate and work together.

• OpenTelemetry Prometheus integration is straightforward. OpenTelemetry Collector can scrape Prometheus targets, process the data, and forward it to any backend of your choice. This allows teams to continue using Prometheus for metrics while leveraging OpenTelemetry for traces and logs.
• Conversely, Prometheus can scrape metrics from applications instrumented with OpenTelemetry, making it possible to combine the strengths of both tools.

This interoperability enables a hybrid approach, where OpenTelemetry handles data collection and Prometheus focuses on metrics storage and querying, often visualized through Grafana (OpenTelemetry vs Prometheus vs Grafana).

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Beyond Metrics: Integrations and Ecosystem Comparisons

While the core comparison between OpenTelemetry vs Prometheus highlights their differences in focus and capabilities, the true power of these tools often comes from their ability to integrate with other observability solutions. 

In this section, we’ll explore how OpenTelemetry and Prometheus stack up against popular tools like Grafana, Jaeger, OpenTracing, and ELK, and how they complement each other in complex observability ecosystems.

OpenTelemetry vs Prometheus vs Grafana

Grafana is one of the most widely-used tools for visualizing metrics and observability data. Though Prometheus comes with a built-in web UI, many teams prefer using Grafana for its rich, interactive dashboards and extensive plugin ecosystem.

• Prometheus + Grafana: This combination is a staple in many monitoring stacks. Prometheus acts as the data source, scraping and storing time-series metrics, while Grafana provides the visual layer, allowing users to build custom dashboards and create alerts based on PromQL queries.
• OpenTelemetry Grafana: While OpenTelemetry doesn’t offer storage or querying capabilities, it integrates seamlessly with Grafana when paired with a metrics backend like Prometheus or InfluxDB. In this setup, OpenTelemetry handles telemetry data collection (metrics, traces, and logs), while Prometheus stores the metrics, and Grafana visualizes the data.

Key Takeaway: For comprehensive observability, many teams use OpenTelemetry for data collection, Prometheus for metrics storage, and Grafana for visualization, creating a powerful end-to-end observability pipeline.

OpenTelemetry vs Jaeger

Jaeger is an open-source distributed tracing system that helps developers trace transactions across microservices. It focuses on visualizing the flow of requests, identifying bottlenecks, and troubleshooting latency issues.

• OpenTelemetry vs Jaeger: While Jaeger specializes solely in distributed tracing, OpenTelemetry offers tracing as part of its multi-signal approach. OpenTelemetry’s tracing capabilities are robust and can export data to Jaeger for visualization. This means you can instrument your application using OpenTelemetry libraries and send trace data directly to a Jaeger backend.

Key Takeaway: If your observability stack already includes Jaeger, integrating it with OpenTelemetry for tracing is a natural fit. OpenTelemetry’s broader scope allows you to collect traces, metrics, and logs while still leveraging Jaeger’s powerful tracing visualization.

OpenTelemetry vs OpenTracing

OpenTracing was one of the first projects aimed at standardizing distributed tracing APIs. However, it has since been merged with OpenCensus to form OpenTelemetry, making OpenTelemetry the successor to OpenTracing.

• OpenTelemetry vs OpenTracing: While OpenTracing focused solely on traces, OpenTelemetry expanded its reach to include metrics and logs, creating a unified observability framework. OpenTelemetry also introduced more standardized data models and protocols, such as OTLP (OpenTelemetry Protocol), simplifying integration with various backends.

Key Takeaway: As OpenTracing is now deprecated, it’s advisable to use OpenTelemetry for any new instrumentation efforts. OpenTelemetry supports backward compatibility, allowing teams to migrate from OpenTracing with minimal disruption.

OpenTelemetry vs ELK

The ELK stack; Elasticsearch, Logstash, and Kibana; is a popular solution for log aggregation, search, and visualization.

• OpenTelemetry vs ELK: While ELK focuses on logs, OpenTelemetry provides the capability to collect logs alongside metrics and traces. OpenTelemetry logs can be exported to ELK for indexing and analysis, enabling you to correlate logs with traces and metrics for a complete observability picture.
• Integration Example: Imagine using OpenTelemetry to collect logs from microservices and sending them to Logstash, which processes and forwards the logs to Elasticsearch. Kibana then visualizes the logs, providing deep search and analysis capabilities.

Key Takeaway: Combining OpenTelemetry and ELK allows teams to build a robust logging pipeline while maintaining consistent instrumentation for metrics and traces.

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Choosing the Right Tool for Your Use Case

With a deeper understanding of OpenTelemetry vs Prometheus, the next logical step is determining which tool; or combination of tools; is best suited for your specific observability needs. Both platforms excel in different areas, and the right choice often depends on the complexity of your systems, your observability goals, and existing infrastructure.

When to Choose OpenTelemetry

OpenTelemetry shines in environments where holistic observability is a priority. Here’s when it makes the most sense to opt for OpenTelemetry:

1. Comprehensive Observability Needs:
   If your goal is to monitor not just metrics but also traces and logs, OpenTelemetry offers a unified solution. It allows you to collect all three telemetry signals with consistent instrumentation, making it easier to correlate events across different data types.
2. Distributed, Polyglot Architectures:
   In microservices environments where multiple programming languages are in use (e.g., Java, Go, Python), OpenTelemetry’s multi-language support makes it easier to maintain consistent observability across the stack.
3. Vendor Neutrality and Flexibility:
   OpenTelemetry’s vendor-neutral approach means you can collect data and send it to any backend of your choice, whether that’s Prometheus, Jaeger, Grafana, ELK, or others. This flexibility is valuable if you want to avoid vendor lock-in or plan to experiment with different backends over time.
4. Future-Proofing Your Observability Stack:
   With growing industry adoption, OpenTelemetry is becoming the standard for observability. Choosing it now can help future-proof your observability strategy as more tools and platforms integrate with OpenTelemetry natively.

Example Scenario:

A company running a complex microservices architecture across Kubernetes, with services written in various languages, could use OpenTelemetry to collect metrics, traces, and logs. The data could then be routed to Prometheus for metrics, Jaeger for traces, and ELK for logs, all through the OpenTelemetry Collector.

When to Choose Prometheus

Prometheus remains a strong contender, especially for teams that need a focused and robust metrics monitoring solution. Consider Prometheus when:

1. Metrics Monitoring is the Primary Focus:
   If your observability needs are centered around time-series metrics, such as CPU usage, memory consumption, or request rates; Prometheus offers a purpose-built solution with rich features.
2. You Need a Self-Contained Monitoring System:
   Prometheus includes data collection, storage, and querying capabilities out of the box. It’s a self-contained system that doesn’t require additional components to get started, making it a great choice for teams seeking simplicity.
3. You Rely on PromQL for Advanced Queries:
   Prometheus’s PromQL is one of its standout features, allowing complex queries and aggregations over time-series data. If your team is already proficient with PromQL or relies heavily on custom metrics queries, Prometheus remains a strong option.
4. You Need Proven Stability:
   Prometheus has been battle-tested in thousands of production environments, particularly within the Kubernetes ecosystem. Its stability and maturity make it a reliable choice for metrics monitoring.

Example Scenario:

A DevOps team responsible for maintaining a Kubernetes cluster might use Prometheus to scrape metrics from nodes and pods, visualize them in Grafana, and set up alerts using Alertmanager to notify the team when performance thresholds are breached.

Hybrid Approach: The Best of Both Worlds

In many cases, organizations benefit from combining OpenTelemetry and Prometheus to create a robust observability stack.

• OpenTelemetry Prometheus Integration:
  The OpenTelemetry Collector can scrape Prometheus metrics or export OpenTelemetry metrics directly to Prometheus. This setup allows teams to use OpenTelemetry for collecting traces and logs while leveraging Prometheus’s mature ecosystem for metrics storage and querying.
• Visualization with Grafana:
  In a typical OpenTelemetry vs Prometheus vs Grafana setup, OpenTelemetry handles data collection, Prometheus stores the metrics, and Grafana visualizes the data, offering a comprehensive view of system health.

Example Scenario:

A SaaS company could use OpenTelemetry to collect traces, logs, and metrics from their applications. Metrics would be routed to Prometheus for storage and visualization via Grafana, while traces go to Jaeger and logs to ELK, creating a fully integrated observability pipeline.

Choosing between OpenTelemetry and Prometheus (or deciding to use both) comes down to your specific needs. If you require a flexible, future-proof observability solution that supports multiple telemetry signals, OpenTelemetry is the way to go. If your focus is on time-series metrics monitoring with a stable, self-contained system, Prometheus remains a top choice.

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Future-Proofing Observability Strategies

As modern applications continue to evolve, so do the requirements for robust observability solutions. Organizations need tools that can not only monitor current system health but also scale with future needs, accommodate diverse architectures, and integrate with evolving ecosystems. 

When comparing OpenTelemetry vs Prometheus, it’s essential to consider how each tool positions itself for the future and how they can help future-proof your observability strategy.

OpenTelemetry: The Unified Future of Observability

OpenTelemetry has quickly emerged as a leading standard in the observability space, driven by its comprehensive scope and vendor-neutral approach. Its ability to handle metrics, traces, and logs under a single framework positions it as the future-proof choice for many organizations.

1. Unified Data Collection:
   OpenTelemetry eliminates the need for multiple instrumentation libraries by supporting metrics, traces, and logs through a single API. This reduces overhead for developers and ensures consistent data collection across different signals.
2. Growing Ecosystem and Community Support:
   With backing from the Cloud Native Computing Foundation (CNCF) and wide industry adoption, OpenTelemetry continues to expand its ecosystem. More tools are becoming OpenTelemetry-native, ensuring long-term viability and integration options.
3. Flexibility and Interoperability:
   One of OpenTelemetry’s strongest points is its flexibility. It allows data to be routed to various backends, including Prometheus, Jaeger, Grafana, and ELK. This means organizations can adopt OpenTelemetry now and change their backend systems in the future without re-instrumenting their applications.
4. Advanced Features:
   OpenTelemetry is not just replicating existing observability patterns, it’s innovating. Features like delta metrics, exponential histograms, and enhanced context propagation are being adopted to provide more granular and actionable insights.

Prometheus: Stability with Evolving Capabilities

While Prometheus has been a staple in metrics monitoring for years, it’s also evolving to meet the growing demands of modern systems.

1. Adapting to Complex Architectures:
   Prometheus’s core focus on time-series metrics remains strong, but integrations with tools like Thanos and Cortex have expanded its capabilities for long-term storage and horizontal scaling. This makes Prometheus suitable even for large-scale, distributed environments.
2. New Features and Improvements:
   The Prometheus community is actively working on adding support for features like exponential histograms and improving its remote write capabilities. These improvements aim to close some of the gaps between Prometheus and more flexible systems like OpenTelemetry.
3. Continued Ecosystem Integration:
   Prometheus remains a cornerstone in the Kubernetes ecosystem, with native integrations and widespread adoption. Its pairing with Grafana for visualization continues to be a popular and powerful combination (OpenTelemetry vs Prometheus vs Grafana remains a hot topic for observability discussions).
4. Compatibility with OpenTelemetry:
   The ability to integrate with OpenTelemetry through the OpenTelemetry Collector ensures that Prometheus remains relevant in observability stacks that require multi-signal support. Organizations can use Prometheus for metrics while relying on OpenTelemetry for traces and logs.

The Hybrid Approach: Embracing Flexibility

For many organizations, the future of observability lies not in choosing between OpenTelemetry and Prometheus, but in using them together. This hybrid approach allows teams to capitalize on the strengths of each tool.

• Data Collection and Routing:
  Use OpenTelemetry for unified data collection across metrics, traces, and logs. The OpenTelemetry Collector can then route metrics to Prometheus for storage and querying, while sending traces to Jaeger and logs to ELK.
• Visualization and Analysis:
  Pair Prometheus with Grafana for rich, interactive dashboards, and use tools like Jaeger and Kibana for visualizing traces and logs. This setup offers a comprehensive observability solution that can scale with your needs.
• Future-Proof Scalability:
  By adopting OpenTelemetry now, organizations can ensure that they’re prepared for future changes in observability tools and standards. Since OpenTelemetry supports multiple backends, switching or adding new tools becomes much easier.

When it comes to observability, flexibility and adaptability are key. OpenTelemetry offers a forward-thinking approach with multi-signal support and vendor neutrality, while Prometheus continues to provide rock-solid metrics monitoring with a growing feature set. Combining the two offers a powerful, future-proof observability strategy.

Conclusion

Observability is advancing, and understanding the strengths and use cases of tools like OpenTelemetry and Prometheus is important for building an effective monitoring strategy. While they serve different purposes, they can also complement each other, offering a comprehensive approach to observability.

OpenTelemetry stands out as a versatile, vendor-neutral observability framework that collects metrics, traces, and logs from applications. Its ability to correlate these signals provides developers with deep insights into system performance, making it ideal for modern, distributed architectures. 

As the industry standard for telemetry collection, OpenTelemetry offers future-proofing, multi-language support, and integration flexibility with popular tools like Jaeger, ELK, and Grafana.

On the other hand, Prometheus excels in time-series metrics monitoring, offering a self-contained system for metrics collection, storage, and querying through its powerful PromQL. 

It has long been a cornerstone in the observability space, particularly within Kubernetes environments, and continues to evolve with features like exponential histograms and enhanced scalability through integrations like Thanos and Cortex.

When deciding between OpenTelemetry vs Prometheus, consider the following:

• Choose OpenTelemetry if you need a comprehensive, multi-signal observability solution with the flexibility to route data to various backends.
• Opt for Prometheus if your focus is on robust, scalable metrics monitoring with mature querying capabilities.
• Embrace a hybrid approach to leverage the best of both worlds, using OpenTelemetry for data collection and Prometheus for metrics storage and visualization through Grafana.

Most organizations will benefit from integrating both tools, creating a powerful observability stack that scales with their needs.

 Whether you’re monitoring a simple application or a complex microservices architecture, combining OpenTelemetry and Prometheus ensures that you have the insights and flexibility required to maintain performance, troubleshoot issues, and future-proof your observability strategy.

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