Cloud Monitoring Services Compared: CloudWatch, Azure Monitor, GCP

Cloud Monitoring Services Compared: CloudWatch, Azure Monitor, GCP

When you run on the cloud, the first line of monitoring usually comes from the vendor's native service: AWS CloudWatch, Azure Azure Monitor, and Google Cloud Cloud Monitoring. All three integrate deeply with their own resources — out-of-the-box metrics and portal-consistent alerting — but their design philosophy, query languages, and ecosystems differ. Understanding these differences helps you avoid "the cloud's built-in monitoring goes unused while we rebuild the same thing elsewhere".

AWS CloudWatch: resource-centric, all-in-one operations

CloudWatch's core is metrics, alarms, and dashboards: a large number of AWS services report metrics automatically, custom metrics are supported, and alarms can notify or trigger automated actions when thresholds are breached. Its operational surface is broad:

• Instrumentation-free APM: Application Signals auto-detects key indicators like latency, error rates, and request rates; CloudWatch Synthetics adds proactive canary scripts; CloudWatch RUM collects real user session data.
• Unified logs: CloudWatch Logs provides Log Insights querying (SQL and PPL), log anomaly detection, and metric filters — turning log fields into metrics for alerting.
• Native OpenTelemetry: it offers native OTLP endpoints, so any OTel SDK/Collector can report metrics, logs, and traces directly, and PromQL queries OTel metrics.

Azure Monitor: unified data platform + AIOps

Azure Monitor gathers metrics, logs, and traces into one data platform. Its signature feature is "two workspaces, two query languages": Log Analytics workspaces analyze logs and traces with KQL, while Azure Monitor workspaces analyze Prometheus/OTel metrics with PromQL. It also embraces OpenTelemetry deeply — Application Insights is essentially Azure Monitor's OTel-based APM.

Another highlight is AIOps: dynamic thresholds and smart detection surface anomalies automatically, and the Azure Copilot Observability Agent correlates alerts, auto-classifies incidents, and suggests next steps — turning "a stream of alerts" into "high-signal issues". This is especially valuable for large teams struggling with alert fatigue.

Google Cloud Monitoring: strong on metrics and SLOs

Google Cloud Monitoring (formerly Stackdriver) inherits Google's SLO/SRE DNA, with built-in SLO monitoring and error budget tracking that aligns with Google's operations culture. It also supports the Prometheus ecosystem, OTel, and multi-platform collection, and usually feels smoothest for GCP users.

Differences at a glance

A table only goes so far — here are two examples you can run today. To add a CPU alarm for an EC2 instance, the AWS CLI does it in one command:

aws cloudwatch put-metric-alarm \
  --alarm-name high-cpu \
  --metric-name CPUUtilization --namespace AWS/EC2 \
  --statistic Average --period 300 --threshold 80 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 2 --alarm-actions arn:aws:sns:us-east-1:...

For the "instrument once, export everywhere" approach, the heart of an OpenTelemetry Collector config is the exporters section — telemetry arriving on one receiver can fan out to the native cloud stack and a self-hosted Prometheus at the same time:

exporters:
  otlp/aws:   { endpoint: "ingest.us-east-1.amazonaws.com" }
  prometheus: { endpoint: "0.0.0.0:9090" }
service:
  pipelines:
    metrics:
      receivers: [otlp]
      exporters: [otlp/aws, prometheus]

On the query side, the three platforms show their different languages: for the same "5-minute average CPU above 80%," CloudWatch uses metric filtering plus a threshold alarm, Azure queries logs with KQL, and GCP writes it directly as a PromQL or MQL expression.

A pragmatic small-team setup often looks like this: native monitoring plus two key alarms for the single-cloud workload, site availability covered by Synthetics/canary probes, and one Grafana dashboard that puts cloud-native and self-hosted metrics on the same screen. You get the timeliness of resource-level alerts without juggling three consoles.

Cost and lock-in: two underrated variables

Beyond features, factor in two costs. The cost ledger: cloud-native monitoring bills by metric count, log ingestion, alerts, and query volume, with log volume usually dominating; run a month within each vendor's free tier and estimate monthly cost from real data instead of guessing. The lock-in ledger: native monitoring integrates best with resources but creates migration friction — switching clouds means rebuilding the monitoring stack. A middle path is "instrument once, export everywhere": collect with OpenTelemetry once, write to the native stack, and replicate to self-hosted Prometheus or a third party as needed, keeping telemetry as a portable asset.

Common Questions

In a multi-cloud setup, should I buy all three monitoring stacks? No, and it is not advisable. Run the main workload on one cloud and treat its native monitoring as the source of truth; funnel secondary traffic from other clouds through OTel into the main platform or a self-hosted Prometheus, and alert centrally.

How do I tame alert fatigue? Turn on each vendor's built-in smart detection first (Azure dynamic thresholds, CloudWatch anomaly detection), then layer the tiering strategy from alert fatigue management to collapse alerts into issues.

What if log costs explode? Logs usually dominate the bill. Start by ingesting only WARN/ERROR levels, sample or aggregate access logs, and scale up gradually; archive rarely used historical logs to cold storage and keep only the query entry point.

16IDC Take

The selection rule is simple: use the native monitoring of the cloud where your business mainly runs, because its integration with billing, IAM, and alerting can't be replicated by third parties. To avoid lock-in, standardize instrumentation with OpenTelemetry so the same telemetry can flow into the native stack and a self-hosted Prometheus. For tiered alerting see alert fatigue management, for SLO discipline see SLO/SLI and error budget practice, for a self-hosted comparison see Prometheus + Grafana basics, and for cloud cost governance see Grafana cost attribution. See more in the Monitoring & Alerting category.

Source: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/WhatIsCloudWatch.html

Reference: Azure Monitor docs https://learn.microsoft.com/en-us/azure/azure-monitor/; Google Cloud Monitoring docs https://cloud.google.com/monitoring/docs