What metrics should I monitor in the cloud?

As organizations move workloads to the cloud, visibility becomes critical for maintaining performance,reliability, and cost efficiency. Cloud environments are inherently dynamic, resources scale up or down, workloads shift, and dependencies evolve. Without a structured approach to cloud monitoring, identifying issues such as latency spikes, resource bottlenecks, or service degradation becomes difficult. Tracking the right cloud metrics enables IT teams to detect anomalies early, optimize resource allocation, and ensure consistent user experience across distributed systems.

1. Compute metrics

Compute resources like virtual machines, containers, and serverless functions, form the foundation of most cloud deployments. Monitoring them ensures that workloads are running efficiently and within performance thresholds.

Key compute metrics to monitor:

• CPU utilization: Tracks how intensively compute resources are used. Sustained high utilization indicates under-provisioning, while persistently low values may suggest over-allocation.
• Memory usage: Critical for identifying memory leaks or workloads that exceed capacity.
• Disk I/O (Input/output operations): Reveals read/write performance issues that can slow down applications.
• Instance availability: Ensures that virtual machines and services remain accessible and responsive.

In AWS EC2, metrics such as CPUCreditBalance (for burst-able instances) and StatusCheckFailed help gauge performance and detect unhealthy instances early. Similarly, Azure VM Insights and Google Cloud Monitoring provide real-time compute visibility through CPU and memory tracking dashboards.

2. Network metrics

Network performance directly impacts cloud application responsiveness. Delays in data transfer or packet loss can degrade end-user experience, especially in multi-region architectures.

Essential network metrics:

• Latency: The time it takes for data to travel between services or regions.
• Throughput/bandwidth: Measures total data transferred over time; helps detect congestion.
• Packet loss and jitter: Indicate instability in network routes or overloaded endpoints.
• Connection errors: Reveal communication failures or configuration issues in services like load balancers and gateways.

Cloud providers like AWS CloudWatch, offer metrics such as NetworkIn, NetworkOut, and NetworkPacketsDropped for detailed network performance insights.

3. Database metrics

Databases often act as the performance bottleneck in cloud applications. Continuous monitoring ensures data consistency and query efficiency.

Key metrics include:

• Query latency: Tracks the time taken for query execution.
• Connections and session counts: Detects connection pool exhaustion or un-closed sessions.
• Cache hit ratio: Indicates how effectively queries are served from cache, impacting speed and cost.
• Replication lag: Monitors synchronization delays in multi-region or failover setups.

In Amazon RDS or Azure SQL, tracking ReadIOPS, WriteIOPS, and Deadlocks provides actionable insights into database load patterns and tuning opportunities.

4. Security and compliance metrics

Beyond performance, monitoring security metrics is vital to safeguard workloads and maintain compliance.

Crucial security metrics:

• Failed login attempts: Indicates brute-force or unauthorized access attempts.
• Access policy changes: Helps track unexpected permission modifications.
• Encryption status: Ensures data in transit and at rest remain protected.
• Vulnerability and patch status: Identifies outdated or unpatched components that pose risks.

These metrics not only enhance security posture but also simplify compliance with frameworks like ISO 27001 and SOC 2.

5. Advanced and emerging cloud metrics

Modern cloud ecosystems demand visibility beyond traditional infrastructure metrics.

Some advanced metrics include:

• Application response time (ART): Tracks end-user transaction performance.
• Service dependencies: Measures the health of interconnected microservices.
• Cost efficiency metrics: Analyze spend per workload, helping optimize cloud budgets.
• Sustainability metrics: Track energy consumption and carbon footprint for green cloud initiatives.

Cloud-native observability platforms now correlate metrics, logs, and traces, allowing faster root cause analysis and predictive scaling.

6. Visualization and alerting best practices

Monitoring is only effective if insights are actionable. Visualization dashboards help interpret metric trends, while alerts ensure timely responses to anomalies.

Best practices:

• Use custom dashboards to correlate metrics across compute, database, and network layers.
• Implement threshold-based alerts for key metrics (e.g., CPU > 80% for 5 minutes).
• Employ anomaly detection and AI-driven alerts to reduce noise and highlight meaningful deviations.
• Regularly review alert policies to align with changing application workloads.