DataDog vs Prometheus – Key features, differences, and alternatives

This content originally appeared on DEV Community and was authored by Ankit Anand ✨

Both DataDog and Prometheus are application monitoring tools aimed to improve application performance. While DataDog is a proprietary SaaS vendor in the APM domain, Prometheus is an open-source metrics monitoring tool that was the second project to graduate from Cloud Native Computing Foundation in 2018. Let us compare DataDog and Prometheus in this article.

In this article, we will explore the differences between DataDog and Prometheus based on these categories:

Getting started
Monitoring use-cases
User-experience and visualizations
Pricing

We will also explore the key features of DataDog and Prometheus.

While DataDog and Prometheus are great monitoring tools, they have their limitations. DataDog is an enterprise SaaS tool with complex pricing tiers. Prometheus is an open-source metrics monitoring tool with limited UI and requires effort to set up and scale.

You can check out SigNoz - an open-source APM tool that comes with great user experience in terms of getting started and web user experience.

Comparing DataDog and Prometheus

The major difference between DataDog and Prometheus lies in the scope of monitoring that each tool covers. DataDog is an enterprise SaaS tool that offers products that cover the entire domain of monitoring.

On the other hand, Prometheus is an open-source metrics monitoring tool used to track metrics like resource usage.

Some of the key differences between DataDog and Prometheus:

Getting started

DataDog is relatively simpler to get started than Prometheus. You need to sign up for a DataDog account and then install DataDog agents on your host. The DataDog agent can be installed on many platforms either directly or as a containerized version. The agent reports events and metrics from the host.

Prometheus installation requires a bit of configuration to get started. You also need to set up a long-term storage layer if you want to retain your metrics. It is easier to get started with Prometheus for monitoring other CNCF projects like Kubernetes.

Monitoring use-cases

DataDog has an extensive list of monitoring services it offers. List of all monitoring products that DataDog provides:

Log Management
APM
Security Monitoring
Infrastructure Monitoring
Network Monitoring

Prometheus enables you to capture time-series data as metrics. These metrics can be aggregated to give insights into the behavior of our systems.

User experience and visualizations

Prometheus ships with a visualization layer, but its functionality and UI are limited. Usually, if someone uses Prometheus, they integrate it with Grafana, another open-source web-based visualization tool.
DataDog comes with out-of-box charts and per-built widgets to build your own dashboards.

Prometheus charts are limited in functionality

DataDog dashboard for traces

Pricing

Prometheus is a free, open-source tool. Many SaaS vendors provide hosted Prometheus services as it takes time and effort to maintain Prometheus as your monitoring scales up.

DataDog is an expensive enterprise monitoring tool that has many different pricing tiers which vary on your use-cases. For example, infrastructure enterprise monitoring starts at $23 per host per month while its APM sand continuous profiler starts at $40 per host per month.

Key Features of DataDog

DataDog is an enterprise SaaS tool that offers an array of services in the monitoring domain. Some of the key features of the DataDog monitoring platform includes:

Log Management
DataDog offers scalable log ingestion and analytics through its log management product. You can search, filter, and analyze log data through its dashboard. You can route all your logs from one central control panel.

Application performance monitoring
DataDog's APM tool provides end-to-end distributed tracing from frontend devices to databases. You can connect the collected traces to infrastructure metrics, network calls, and live processes.

Security monitoring
Using DataDog security monitoring, you can analyze operational and security logs in real-time. It provides built-in threshold and anomaly detection rules to detect threats quickly.

Network monitoring
With DataDog network monitoring, you can analyze traffic as it flows across applications, containers, availability zones, and on-premise servers. You can track key network metrics like TCP retransmits, latency, and connection churn.

Real user monitoring
With DataDog's real user Monitoring, you can have end-to-end visibility into user journeys for web and mobile applications.

Key Features of Prometheus

Prometheus was initially developed at SoundCloud in 2012 before being released as an open-source project. It got accepted into the CloudNative Computing Foundation in 2016 and was the second project to graduate from the foundation, following Kubernetes.

Prometheus enables you to capture time-series data as metrics. These metrics can be aggregated to give insights into the behavior of our systems.

Some of the key features of Prometheus metrics monitoring are:

Multi-dimensional data model
Prometheus stores data as time-series. For example, it can store time-stamped values of the total number of HTTP requests received. You can also store an optional set of key-value pairs called labels for that metric. The multi-dimensional data model enables rich contextual metrics monitoring. Notation of time-series metrics:

<metric name>{<label name>=<label value>, ...}

Flexible query language
Prometheus provides a query language called PromQL. Using PromQL, you can filter and aggregate metrics data in real-time.

Pull model data collection
In contrast to most APM tools, Prometheus data collection is pull-based. It requires you to run an HTTP server that exposes Prometheus metrics.

Alert manager
You can use a rules.yml file to set alerts for critical issues. You need to install the alert manager to get useful notifications from Prometheus. It has some cool features like grouping alerts into one notification and silencing alerts for a period of time.

Visualization layer
The visualization layer of Prometheus is basic, but it can be combined with Grafana - another open-source web visualization tool to create rich visualizations of monitoring data.

Architecture of Prometheus (Source: Prometheus website)

A better to alternative to DataDog and Prometheus - SigNoz

SigNoz is a full-stack open-source application performance monitoring and observability tool which can be used in place of DataDog and Prometheus. SigNoz is built to give SaaS like user experience combined with the perks of open-source software. Developer tools should be developer first, and SigNoz was built by developers to address the gap between SaaS vendors and open-source software.

Key architecture features:

Native OpenTelemetry support
SigNoz is built to support OpenTelemetry natively which is quietly becoming the world standard to generate and manage telemetry data.

Flexible and scalable Database storage
SigNoz provides users flexibility in terms of storage. You can choose between ClickHouse or Kafka + Druid as your backend storage while installing SigNoz.

Architecture of SigNoz with ClickHouse as storage backend and OpenTelemetry for code instrumentatiion

SigNoz comes with out of box visualization of things like RED metrics.

SigNoz UI showing application overview metrics like RPS, 50th/90th/99th Percentile latencies, and Error Rate

You can also use flamegraphs to visualize spans from your trace data. All of this comes out of the box with SigNoz.

Flamegraphs showing exact duration taken by each spans - a concept of distributed tracing

You can also build custom metrics dashboard for your infrastructure.

You can also build a custom metrics dashboard for your infrastructure

Some of the things SigNoz can help you track:

Application overview metrics like RPS, 50th/90th/99th Percentile latencies, and Error Rate
Slowest endpoints in your application
See exact request trace to figure out issues in downstream services, slow DB queries, call to 3rd party services like payment gateways, etc
Filter traces by service name, operation, latency, error, tags/annotations.
Run aggregates on trace data
Unified UI for both metrics and traces

You can check out SigNoz's GitHub repo here 👇