Prometheus vs Wavefront: What are the differences?

Introduction

Prometheus and Wavefront are both popular monitoring tools used for collecting, storing, and visualizing time series data. While they have similar functionalities, there are several key differences that set them apart.

1. Data Model: Prometheus uses a pull-based model, where it periodically scrapes metrics from target systems, while Wavefront uses a push-based model, where applications and systems actively send metrics to Wavefront. This difference in data collection method can impact the scalability and flexibility of monitoring in different environments.

2. Data Storage: Prometheus stores its collected metrics in a local, on-disk time series database. On the other hand, Wavefront leverages cloud-based storage for its metrics, allowing for unlimited scalability and easy integration with other cloud services. This difference in data storage approach affects the long-term retention and scalability of metrics data.

3. Query Language: Prometheus offers PromQL, a powerful and flexible query language tailored for time series analysis and monitoring. Wavefront, on the other hand, supports a rich query language called Wavefront Query Language (WQL), which includes advanced features like analytics functions and outlier detection. These differences in query languages enable users to perform different types of complex analyses and data manipulations.

4. Alerting and Notification: Prometheus has a built-in alerting system that supports alert rule definitions and alert manager integration for configurable notifications. Wavefront also provides alerting capabilities but offers more advanced features like anomaly detection, anomaly alerting, and smart alert routing based on dynamic baselines. The differences in alerting and notification capabilities enable more sophisticated monitoring and alert management strategies with Wavefront.

5. Integration Ecosystem: Prometheus has a rich ecosystem of exporters and integrations, making it easy to collect metrics from different types of systems and applications. Wavefront also offers integrations with various systems and supports multiple ingestion methods, including agents and APIs. However, Wavefront's integration ecosystem is more oriented towards cloud-native environments and supports seamless integration with popular cloud platforms like Kubernetes and AWS. These differences in integration ecosystem cater to different monitoring requirements and environments.

6. Visualization and Dashboards: Prometheus provides a basic web interface for visualizing collected metrics and building custom dashboards using PromQL queries. Wavefront, on the other hand, offers a highly intuitive and interactive visualization platform with pre-built dashboards, charts, and rich visual analysis capabilities. Wavefront's focus on data visualization and exploration enables users to gain deeper insights from their metrics data with ease.

In summary, Prometheus and Wavefront differ in their data collection models, data storage approaches, query languages, alerting and notification capabilities, integration ecosystem, and visualization options. Choosing the right tool depends on the specific monitoring requirements, scalability needs, and desired feature set for effective monitoring and observability.