OpenTelemetry Backend: How to Choose the Right Backend for Traces, Metrics, and Logs

OpenTelemetry is a Cloud Native Computing Foundation (CNCF) graduated project that standardizes how we instrument applications to generate telemetry data (logs, metrics, and traces). While OpenTelemetry provides the tools to collect and export this data, it purposefully does not include a storage, analysis, or visualization layer. That is the job of an OpenTelemetry backend.

Reading the above, you might wonder: why is observability even needed in the first place?

Cloud computing and containerization made deploying and scaling applications easier. Modern applications make use of modular code and architectures like microservices and serverless that enable engineering teams to ship features faster, and meet any surge in user demand by spinning up more containers, either manually or via auto-scaling.

But every coin has two sides. While having benefits like smaller engineering teams and on-demand scaling of applications, cloud computing and containerization have also increased operational complexity manifold. Troubleshooting an application based on a distributed system for performance issues is like finding a needle in a haystack.

Collecting telemetry data from applications that can act as a signal for troubleshooting performance issues is a practice as old as writing software. OpenTelemetry (OTel) solves the problem of inconsistent instrumentation by providing an open standard.

What is an OpenTelemetry Backend?

Earlier we mentioned that OpenTelemetry itself does not store, query, or visualize telemetry data, instead assigning that responsibility to an OpenTelemetry backend.

An OpenTelemetry backend is the software that sits at the end of your telemetry pipeline and handles:

• Storage of logs, metrics, and traces at scale
• Querying across signals for debugging and analysis
• Visualization through dashboards, flamegraphs, and service maps
• Alerting and correlation to surface issues before users notice them

OpenTelemetry is an open standard that consists of APIs, SDKs, the Collector, and semantic conventions. It provides a pluggable architecture that frees users from vendor lock-in, as the same instrumentation logic works across all compatible backends.

Since it does not define guidelines for building backends, observability vendors freely choose different mechanisms to store, process, and visualize ingested telemetry based on their needs and goals.

In practice, the data flow looks like this: OpenTelemetry SDKs instrument your application code, the generated telemetry is sent to the OpenTelemetry Collector for processing and routing, and the Collector then exports it to one or more observability backends.

The Collector can receive, process, and export data, but it does not replace a backend. It cannot store data persistently, correlate it across signals, or present it visually. A dedicated OpenTelemetry backend is essential to turn raw telemetry into actionable insights.

How to choose an OpenTelemetry backend

OpenTelemetry provides you the freedom to choose a backend analysis tool of your choice, with most observability vendors now offering support for OpenTelemetry. But how do you ensure which OpenTelemetry backend to go for?

There are three main components that an OpenTelemetry backend is responsible for:

Data Storage

Observability data can be large in size, and is the reason why most observability vendors bill for signals per-GB. After all, instrumented systems (applications and infrastructure) typically generate significant enough data that enable users to visualize, understand, and learn from their behaviour over a period of time.

The data storage capabilities of an OpenTelemetry backend should be highly scalable, and query execution performance should be top-notch. Consider whether the backend can handle high-cardinality trace and log data, supports configurable retention policies, and can scale horizontally as your telemetry volume grows.

Query Service

OpenTelemetry has three distinct signals. Each signal has different data formats with different use-cases. Moreover, developers often correlate signals for better debugging capabilities. The query service of an OpenTelemetry backend should be built with these considerations in mind: supporting fast filtering, aggregation, and cross-signal correlation.

Visualization

OpenTelemetry backends should provide intuitive dashboards that enable end-users to take quick actions on performance issues. Look for built-in support for flamegraphs, service maps, and the ability to build custom dashboards without needing a separate visualization tool.

Once you have configured alerts and consolidated queries into dashboards, the visualization medium becomes critical. Lack of proper visualization mechanisms, or non-intutive experiences will hamper your ability to get the most out of your OpenTelemetry deployment.

Beyond these three considerations, you should further consider these specific factors when selecting an OpenTelemetry backend:

• Support for all distinct signals of OpenTelemetry
  Currently, OpenTelemetry collects telemetry data in three distinct signals, namely, logs, metrics, and traces. Setting up a robust observability framework requires the use of all three signals.

  Utilizing all three signals enables users to understand scenarios like: how a request led to certain events, which led to increase in request latency over the past 30 minutes.

  

• Native OTLP and semantic convention support
  In OpenTelemetry, telemetry signals and the resources they originate from (like applications or infrastructure) are enriched with key-value pairs known as attributes, providing context like the response method or error code.

  The OpenTelemetry specification understood that a lack of a common standard would mean different vendors, or even different teams in the same organization, might define these attributes differently, leading to a fragmentation in end-user observability pipelines.

  To solve this problem, they introduced standards known as OpenTelemetry semantic conventions. These conventions provide a common language for describing observability data, ensuring that everyone speaks the same language, regardless of their vendor or team.

  For example, here is a glimpse of how some HTTP attributes are defined via semantic conventions:

  Attribute	Description	Example
  http.method	HTTP request method	GET; POST; HEAD
  http.target	The full request target as passed in an HTTP request line or equivalent	/blog/june/
  http.scheme	The URI scheme that identifies the used protocol	http; https

  An OpenTelemetry backend should natively support OTLP ingestion and should store data using these semantic conventions. Many observability vendors usually transform the data collected using these conventions into their proprietary formats, which can result in lost context and make future migrations harder.
  OpenTelemetry has a large and growing list of conventions which might not be fully utilized in such scenarios.

• Should allow aggregates on trace data
  Running aggregates on trace data enables you to create service-centric views. OpenTelemetry also provides you the ability to create custom tags. Combined with custom tags and aggregated trace data gives you a powerful magnifying glass to surface performance issues in your services. For example, you can get the error rate and 99th percentile latency of customer_type: gold or deployment_version: v2 or external_call: paypal.

• Open source and deployment model
  OpenTelemetry is an open source standard with a huge community backing. It is testimonial to the fact that community-driven projects can solve large complex engineering problems. It is not necessary for the OpenTelemetry backend to be open source.

  But having an open source OpenTelemetry backend can enable you to have a full-stack open source solution. Open source solutions have more flexibility, and if you self-host, you have complete control over your data. Beyond the open-source question, also consider whether the backend offers a managed cloud option, a self-hosted deployment, or both, as this affects operational overhead, data residency, and long-term cost.

Nearly all observability vendors now claim to be 100% compatible with OpenTelemetry. But it's difficult to move away from legacy systems. A solution built natively for OpenTelemetry can be a good choice for an OpenTelemetry backend, and that's where SigNoz comes into the picture.

Top OpenTelemetry Backends

Choosing the right OTel backend starts with understanding two broad categories:

• All-in-one backends ingest logs, metrics, and traces under a single pane of glass, making cross-signal correlation straightforward during an incident. Examples: SigNoz, Honeycomb.
• Signal-specific backends are optimized for one signal type and typically need to be paired with other tools for full observability coverage. Examples: Jaeger (traces only), Prometheus (metrics only).

For most teams building on OpenTelemetry, an all-in-one backend reduces operational overhead and removes the need to manually correlate data across separate tools. Below are some of the top OpenTelemetry backends to consider:

SigNoz: OpenTelemetry-native backend for traces, metrics, and logs

SigNoz is a full-stack open-source APM tool built to support OpenTelemetry natively. It serves as a backend for storing telemetry data, provides metrics monitoring, distributed tracing, and log management under a single pane of glass.

SigNoz is an ideal choice for distributed tracing based on OpenTelemetry. With SigNoz, you can do the following:

• Visualise Traces, Metrics, and Logs in a single pane of glass
• Monitor application metrics like p99 latency, error rates for your services, external API calls, and individual endpoints.
• Find the root cause of the problem by going to the exact traces that are causing the problem and see detailed flamegraphs of individual request traces.
• Run aggregates on trace data to extract business-relevant metrics
• Filter and query logs, build dashboards and alerts based on attributes in logs
• Monitor infrastructure metrics such as CPU utilization or memory usage
• Record exceptions automatically in Python, Java, Ruby, and Javascript
• Easy to set alerts with DIY Query Builder

Jaeger: Tracing-focused backend

Jaeger is an open-source distributed tracing tool developed at Uber, later donated to the Cloud Native Computing Foundation(CNCF). It is used to monitor and troubleshoot applications based on microservices architecture.

Jaeger is used to store, analyze, and visualize tracing data but it does not support logs and metrics.

Some of its key features include:

• Distributed context propagation
• Distributed transaction monitoring
• Root cause analysis
• Service dependency analysis
• Performance/latency optimization

Jaeger supports three popular open-source NoSQL databases as trace storage backends: Cassandra, Elasticsearch and OpenSearch. Jaeger's UI can be used to see individual traces. You can also filter the traces based on service, duration, and tags. However, its UI is a bit limited for users looking to perform more sophisticated data analysis.

Best for: Teams that need a lightweight, self-hosted distributed tracing solution and are comfortable pairing it with other tools for metrics and logs. To better understand Jaeger and see how it stacks up against OpenTelemetry, check out OpenTelemetry vs Jaeger.

Watch out: Jaeger does not support logs or metrics, so it cannot serve as a standalone observability backend for all three OpenTelemetry signals.

Prometheus: Metrics-focused backend

Prometheus is an open-source metrics monitoring and alerting toolkit designed to monitor the performance and health of various components in a distributed system. It excels at collecting time-series data, making it particularly effective for tracking metrics and trends over time. Prometheus employs a pull-based model, where it scrapes data from instrumented applications and services at regular intervals.

Prometheus can be a good choice for you if you want to simply collect OpenTelemetry metrics.

Some of the key features of Prometheus are:

• Multi-dimensional data model
• A query language called PromQL to query the metrics data collected
• Pull model data collection over HTTP
• An alert manager to handle alerts

The only challenge with Prometheus is its basic visualization layer. You must combine it with a tool like Grafana to get better metrics visualization.

Best for: Teams focused primarily on metrics monitoring, especially those already familiar with PromQL and the Prometheus ecosystem. See also: OpenTelemetry vs Prometheus.

Watch out: Prometheus does not handle traces or logs. If you need full observability across all three signals, you will need to pair it with additional tools.

Honeycomb: Cloud-based observability backend

Honeycomb is a full-stack cloud-based observability tool with support for events, logs, and traces. Honeycomb seamlessly integrates with OpenTelemetry, allowing for the collection of telemetry data for storage, visualization, and analysis.

Some of the key features of the Honeycomb include:

• Quickly diagnose bottlenecks and optimize performance with a waterfall view to understand how your system is processing service requests
• Advanced querying capabilities and visualization tools
• Full-text search over trace spans and toggle to collapse and expand sections of trace waterfalls
• Provides Honeycomb beelines to automatically define key pieces of trace data like serviceName, name, timestamp, duration, traceID, etc.

Best for: Teams that prefer a managed SaaS experience with strong high-cardinality querying and don't need self-hosting.

Watch out: Honeycomb is proprietary and cloud-only. There is no self-hosted option, and costs can scale quickly with high-volume telemetry.

Grafana Tempo: Trace storage in the Grafana stack

Grafana Tempo is an open-source distributed tracing backend. It is designed to be a scalable and efficient solution for storing and querying traces. It integrates seamlessly with OpenTelemetry to collect and analyze trace data, and with Grafana for visualization of trace data.

It is important to note that Tempo handles trace storage specifically. Within the broader Grafana ecosystem, Loki handles logs and Prometheus handles metrics. So while Tempo is excellent at what it does, achieving full observability requires operating the complete Grafana stack.

Some of the key features of Grafana Tempo include:

• Compatible with popular open-source tracing protocols like Zipkin and Jaeger
• Supported by Grafana as a separate data source for trace visualizations
• Available as self-hosted and cloud version
• Provides service graph

Best for: Teams already invested in the Grafana ecosystem (Grafana + Prometheus + Loki) who need a trace storage layer that fits naturally into their existing stack. See also: SigNoz vs Grafana.

Watch out: Tempo is not an all-in-one backend by itself. You will need Loki for logs and Prometheus for metrics, which means operating and maintaining three separate systems for full OpenTelemetry coverage.

OpenTelemetry Backend Comparison

The table below summarizes the key characteristics of each backend to help you make a quick decision:

If you need a self-hosted OpenTelemetry backend that covers all three signals without stitching together multiple tools, SigNoz is the most feature-rich, OpenTelemetry-native option available today.

As a quick decision shortcut:

• All three signals under one roof, with self-hosting or cloud options and native OpenTelemetry support → SigNoz
• Metrics-only monitoring with a mature ecosystem → Prometheus
• Lightweight, self-hosted distributed tracing → Jaeger
• Already invested in the Grafana stack → Tempo + Loki + Prometheus
• Managed SaaS with strong querying, no self-hosting needed → Honeycomb

Trying out an OpenTelemetry backend with SigNoz

If you are looking to try out an OpenTelemetry backend, SigNoz is one of the easiest ways to get started. With its OTel-native APM capabilities, your instrumentation works out of the box without any proprietary agents or format conversions.

The getting-started flow is straightforward:

1. Instrument your application using OpenTelemetry client libraries for your language of choice.
2. Send data to SigNoz by either pointing your SDK or Collector to a specific port on your self-hosted SigNoz instance, or by using the SigNoz cloud ingestion URL.
3. Explore your telemetry — metrics, traces, and logs are all available in a single interface with out-of-box charts, flamegraphs, and query capabilities.

Frequently Asked Questions

Does OpenTelemetry store data?

No. OpenTelemetry is responsible for generating, collecting, and exporting telemetry data from your applications. It does not provide storage, querying, or visualization. You need a dedicated OpenTelemetry backend such as SigNoz to store and analyze the data that OpenTelemetry exports.

What is the difference between the OpenTelemetry Collector and an OpenTelemetry backend?

The OpenTelemetry Collector is a vendor-agnostic proxy that receives, processes, and routes telemetry data. Rather than act like a storage or analysis system, it instead acts like a pipeline layer.

This data ultimately lands in an OpenTelemetry backend which is responsible for storing, indexing, querying, and visualizing the telemetry. Think of the Collector as the router; the backend as the destination.

Can I use one backend for OpenTelemetry logs, metrics, and traces?

Yes, and for most teams this is the logical approach. All-in-one backends like SigNoz support all three signals natively, letting you correlate a spike in error traces with a specific log event or a metrics anomaly, all in one view. Signal-specific backends like Jaeger (traces) or Prometheus (metrics) can work, but require operating multiple tools and stitching cross-signal correlation together manually.

How do I choose a self-hosted OpenTelemetry backend?

SigNoz is one of the most popular self-hosted OpenTelemetry backends because it is built natively on OpenTelemetry and supports logs, metrics, and traces out of the box. It uses ClickHouse as its storage engine, which provides strong query performance at scale. For teams that need traces only, Jaeger is a lightweight self-hosted option. For metrics-only workloads, Prometheus remains the standard choice.