Prometheus vs Datadog: A Comparative Overview

No items found.

When it comes to monitoring and observability solutions, Prometheus and Datadog are two popular choices among developers and DevOps teams alike. Both boast powerful features and capabilities for tracking, analyzing, and troubleshooting system performance. In this blog post we'll take a comprehensive approach in comparing them by exploring key parameters such as data collection & storage, metrics & instrumentation, visualization & alerting as well as ecosystem & integrations to make an informed decision when choosing between them.

Feature	Prometheus	Datadog
Data Collection and Storage	Uses a pull model to collect metrics from exporters. Metrics are stored in a time series database, such as Prometheus's own TSDB or InfluxDB.	Uses a push model to collect metrics from agents. Metrics are stored in Datadog's own database.
Metrics and Instrumentation	Supports a wide variety of metrics, including system metrics, application metrics, and custom metrics. Exporters are available for many popular technologies.	Supports a wide variety of metrics, including system metrics, application metrics, logs, and traces. Agents are available for many popular technologies.
Visualization and Alerting	Provides a built-in web dashboard for visualizing metrics. Alerts can be created based on metric thresholds or custom rules.	Provides a built-in web dashboard for visualizing metrics and logs. Alerts can be created based on metric thresholds, custom rules, or user-defined conditions.
Ecosystem and Integrations	Has a large and active open source community. The exact integration number is unknown but Prometheus is so popular that most of your techstack integrates with it.	Has 600+ integrations with many popular tools and technologies.
Pricing	Open-source - is free to download and use.	Free and paid plans are available. Paid plans start at $15 per host per month.

1. Data Collection and Storage:

Prometheus Data Collection and Storage Architecture

Data collection and storage are two essential aspects of any monitoring tool. Prometheus' pull-based model collects metrics from instrumented services at regular intervals and stores the collected information in a time series database for efficient querying and analysis, while Datadog offers both pull-based and push-based models, providing flexibility in data collection; its scalable distributed storage system also makes it suitable for large scale deployments.

2. Metrics and Instrumentation:

Both Prometheus and Datadog excel at metrics and instrumentation capabilities. Prometheus utilizes PromQL, a powerful query language designed to retrieve and analyze metrics. Creating custom queries and alerts becomes straightforward using PromQL's powerful query capabilities; dynamic service discovery makes monitoring new services automatic. Datadog offers numerous out-of-the-box integrations as well as comprehensive libraries and agents to collect metrics from various sources with dashboards for visualizations that add extra context.

3. Visualization and Alerting:

‍

Effective visualization and alerting play a vital role in monitoring and troubleshooting. Prometheus provides a user-friendly web-based interface called Prometheus Expression Browser that facilitates visualizing metrics and exploring data sets while supporting various graphing/charting options. Datadog on the other hand features a highly customizable dashboard with visually appealing visualization options and advanced alerting features like anomaly detection/threshold alerting for timely notifications of critical issues.

4. Ecosystem and Integrations:

‍

A monitoring tool's ecosystem and integrations can have a substantial effect on its usefulness and versatility. Prometheus' open-source community supports it well, creating a wide array of exporters, libraries and plugins which make it highly extensible while seamlessly integrating with popular systems and frameworks. Datadog offers 600+ preloaded integrations such as cloud providers, databases container platforms as well as APIs/SDKs to allow custom integrations for compatibility across environments.

5. Pricing:

‍

Prometheus is an open-source tool and does not incur licensing costs, although costs associated with running Prometheus at scale like computing, storage or networking costs may incur. There are paid Prometheus as a service (PaaS) offerings from providers like Amazon, Google, Microsoft etc. which would cost you anywhere between $0.03 - $0.06 per Prometheus node per hour.

Datadog follows a subscription-based pricing model. The cost depends on the number of hosts and features required; while this comes at a cost, using Datadog provides dedicated support, regular updates, and managed infrastructure management - making your life simpler in many ways!

Both Prometheus and Datadog are powerful monitoring and observability tools, each offering unique strengths. Prometheus excels in data collection and storage through its pull-based model and time series database, powerful query capabilities and extensive open-source ecosystem. Meanwhile, Datadog boasts flexible data collection models, extensive integrations, customizable dashboards as well as alerting/anomaly detection features. Ultimately the winner would be the tool that best meets the requirements, preferences, and existing technology stack of your organization.

‍

Squadcast is a Reliability Workflow platform that integrates On-Call alerting and Incident Management along with SRE workflows in one offering. Designed for a zero-friction setup, ease of use and clean UI, it helps developers, SREs and On-Call teams proactively respond to outages and create a culture of learning and continuous improvement.