Amazon CloudWatch vs Prometheus

Struggling to choose between Amazon CloudWatch and Prometheus? Both products offer unique advantages, making it a tough decision.

Amazon CloudWatch is a Ai Tools & Services solution with tags like monitoring, metrics, logs, events, aws.

It boasts features such as Metrics - Collect and track metrics, collect and monitor log files, Alarms - Set alarms that automatically trigger actions, Events - Send custom events to CloudWatch Events, Logs - Monitor, store, and access log files, Dashboards - Create visualizations of metrics and alarms and pros including Real-time monitoring of AWS resources, Automatic scaling and EC2 instance recovery, Log aggregation and analysis, Trigger notifications and auto-scaling based on metrics, Easy to set up and integrate with other AWS services.

On the other hand, Prometheus is a Ai Tools & Services product tagged with monitoring, alerting, metrics.

Its standout features include Multi-dimensional data model with time series data identified by metric name and key/value pairs, PromQL, a flexible query language to leverage this dimensionality, No reliance on distributed storage; single server nodes are autonomous, Time series collection happens via a pull model over HTTP, Pushing time series is supported via an intermediary gateway, Targets are discovered via service discovery or static configuration, Multiple modes of graphing and dashboarding support, and it shines with pros like Highly dimensional model allows flexible and efficient queries, PromQL supports aggregation and recording rules for pre-calculation, Built-in alerting and notification routing, Highly available with simple operational model, Native support for Kubernetes, Strong ecosystem integration.

Feature	Amazon CloudWatch	Prometheus
Overall Score	N/A	1
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

Amazon CloudWatch

Description: Amazon CloudWatch is a monitoring and observability service that provides data and actionable insights for AWS resources and applications. It delivers metrics, logs, and events to help developers and operators optimize applications, understand resource utilization, and get a unified view of operational health.

Prometheus

Description: Prometheus is an open-source systems monitoring and alerting toolkit. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if certain conditions are met.

Key Features Comparison

Amazon CloudWatch Features

• Metrics - Collect and track metrics, collect and monitor log files
• Alarms - Set alarms that automatically trigger actions
• Events - Send custom events to CloudWatch Events
• Logs - Monitor, store, and access log files
• Dashboards - Create visualizations of metrics and alarms

Prometheus Features

• Multi-dimensional data model with time series data identified by metric name and key/value pairs
• PromQL, a flexible query language to leverage this dimensionality
• No reliance on distributed storage; single server nodes are autonomous
• Time series collection happens via a pull model over HTTP
• Pushing time series is supported via an intermediary gateway
• Targets are discovered via service discovery or static configuration
• Multiple modes of graphing and dashboarding support

Pros & Cons Analysis

Amazon CloudWatch

Pros

• Real-time monitoring of AWS resources
• Automatic scaling and EC2 instance recovery
• Log aggregation and analysis
• Trigger notifications and auto-scaling based on metrics
• Easy to set up and integrate with other AWS services

Cons

• Additional charges for storage and API requests
• Delayed metrics (1-minute granularity)
• Steep learning curve for queries and dashboards
• No application performance monitoring for non-AWS resources
• Logs can get costly if storing large volumes of data

Prometheus

Pros

• Highly dimensional model allows flexible and efficient queries
• PromQL supports aggregation and recording rules for pre-calculation
• Built-in alerting and notification routing
• Highly available with simple operational model
• Native support for Kubernetes
• Strong ecosystem integration

Cons

• Pull-based model can miss short-lived spikes between scrapes
• No automatic removal of stale metrics (extra storage usage)
• Limited tooling for stats analysis, forecasting, anomaly detection
• No built-in federation for massive scale
• Steep learning curve for PromQL and architecture

Pricing Comparison

Amazon CloudWatch

• Pay-As-You-Go

Prometheus

• Open Source