Cloud-Native AIOps

Cloud providers offer native AIOps services that use ML to detect anomalies, surface probable root causes, and reduce alert noise within their own ecosystems. Choosing between native tooling and third-party platforms like Datadog depends on whether your infrastructure spans one cloud or many.

Overview

AIOps on the three clouds

Each major cloud ships native AIOps features, and each covers a different layer. Cross-cloud platforms such as Datadog sit above all three when workloads span providers.

Azure

Azure Monitor is Microsoft's unified telemetry platform for metrics, logs and traces from Azure resources, on-premises systems and third-party sources. Its query layer is KQL (Kusto Query Language), a columnar language optimized for log analytics that runs time-series and pattern-matching queries against Log Analytics workspaces.

Application Insights Smart Detection — Application, real time — ML anomaly detection on response times, failure rates and dependency failures; learns your baseline and alerts on statistically significant deviations before static thresholds would fire
Azure Advisor — Strategic — ML recommendations on cost, performance, security and reliability from your configuration and usage; flags drift from best practices, not a real-time tool
Azure Service Health — Platform — Proactive incident notices from Microsoft, including root cause for platform-level issues

AWS

DevOps Guru is AWS's most purpose-built AIOps service. It names specific resource anomalies such as a Lambda cold start spike, RDS connection pool exhaustion or ECS task placement failure.

CloudWatch Anomaly Detection — Per-metric ML model on history, with seasonality and trend, draws a dynamic expected band; alerts when the metric leaves it. The composable option
Systems Manager OpsCenter — Aggregates operational data across AWS services, with recommendations and runbooks beside each operational item
CodeGuru Profiler — ML over production profiling data for Java and Python; finds the lines consuming disproportionate CPU
X-Ray — Distributed tracing with a service map of topology and latency, useful for topology-based RCA

GCP

Google Cloud Operations Suite (formerly Stackdriver) covers metrics, logs, traces and error reporting. Error Reporting is its most distinctive feature: instead of ten thousand stack traces, you see one exception type occurring twelve hundred times per hour, with a representative trace.

> [!NOTE] GCP's native AIOps is less turnkey than DevOps Guru because Vertex AI is not pre-integrated with Cloud Monitoring. Custom ML alerting means building the models on Vertex AI yourself.

The multi-cloud correlation problem

No cloud-native tool from either provider models a dependency that crosses clouds, so latency on cross-cloud calls stays invisible without explicit instrumentation. Datadog is the most widely deployed cross-cloud AIOps layer: one agent across AWS, GCP and Azure, one control plane, and Watchdog running over the combined telemetry.

Native or third-party

Native cloud tools — All workloads on one cloud, within free tier limits
Third-party platform — Multi-cloud with a need for consistent alerting and AIOps, or the AIOps layer must span on-premises and SaaS dependencies too

Interview questions

Compare Amazon DevOps Guru and Azure Application Insights Smart Detection. What problems does each solve and where does each fall short?
A company runs services on both AWS and GCP. Service A on AWS calls Service B on GCP as a synchronous dependency. Latency increases by 200ms. How would you investigate this with and without a cross-cloud observability tool?
When would you recommend Datadog Watchdog over AWS DevOps Guru for an AWS-first company, and what are the cost tradeoffs?
How does GCP Error Reporting's exception clustering differ from traditional log-based alerting, and what operational problem does it solve?