Improve your AWS CloudWatch Observability with AI-Powered Investigations
NeuBird’s Hawkeye integrates seamlessly with AWS CloudWatch, acting as an agentic AI Site Reliability Engineer. Hawkeye delivers autonomous investigation and intelligent correlation that extends far beyond traditional AWS-native monitoring. Process CloudWatch data points with AI-powered analysis, correlate AWS telemetry with multi-cloud environments, and correlate logs across Lambda, RDS, ElastiCache, and external tools.
Why DevOps Teams Choose Hawkeye with AWS CloudWatch
Investigate incidents in minutes, not hours
By correlating data across your observability stack, Hawkeye swiftly pinpoints the root causes of incidents, reducing the time spent on investigations.
Autonomous CloudWatch investigation
Move beyond generic CloudWatch alarms to AI-driven root cause analysis that automatically identifies relationships between AWS microservices, traces, configuration changes, and detects performance patterns across dynamic cloud infrastructure.
Enterprise-scale observability
Handle thousands of weekly CloudWatch alerts with intelligent correlation, reducing alert fatigue while enabling engineering teams to shift from reactive monitoring to strategic optimization and innovation.
How it Works
Connect CloudWatch to Hawkeye
Link your AWS environment by providing read-only API access. Once connected, data begins syncing automatically so Hawkeye can monitor and analyze your AWS services, including CloudWatch, in real time.
Investigate across Lambda, RDS, ECS, and AWS Ecosystem
Hawkeye integrates with CloudWatch to analyze metrics data from Lambda functions, RDS databases, ECS containers, and Step Functions, enabling the identification of performance issues and optimization opportunities often missed by conventional monitoring.
Hawkeye analyzes automatically when alarms fire
When CloudWatch alarms trigger, Hawkeye reviews metrics, logs, and traces alongside data from ServiceNow, DataDog, and Splunk. This creates a complete view of the incident and accelerates response.
Get actionable insights across your infrastructure
Root-cause analysis combines CloudWatch metrics with correlated events, impacted services, and remediation recommendations. Your team immediately sees what failed and how to fix it.
CloudWatch + Hawkeye AI SRE Use Cases
Orchestrate CloudWatch data
Automatically process millions of CloudWatch data points to identify performance patterns, resource constraints, and configuration impacts across complex AWS environments with AI-powered analysis and correlation.
Multi-service AWS investigation without manual correlation
Correlate CloudWatch metrics across Lambda, RDS, ElastiCache, and API Gateway to provide comprehensive root cause analysis that connects application performance with underlying infrastructure health.
Hybrid cloud observability with unified investigation
Extend CloudWatch monitoring with intelligent correlation to Azure, GCP, and on-premises infrastructure, providing unified operational intelligence across your entire technology stack.
Transform CloudWatch alerts into intelligent ServiceNow incidents
When CloudWatch alarms fire, Hawkeye automatically correlates data from ServiceNow tickets with AWS metrics and logs to identify affected AWS services, recent configuration changes, and recommended remediation steps. Your engineers get actionable intelligence instead of raw CloudWatch metrics.
Integration Help
Setup takes less than 10 minutes. Follow our step-by-step AWS integration setup guide or contact our team, we are always available.
Frequently Asked Questions
CloudWatch includes Anomaly Detection and Logs Insights for identifying trends and querying logs within the AWS ecosystem. Hawkeye builds on this by applying AI-powered investigation that correlates CloudWatch data with metrics and logs from multiple platforms. It delivers automated root-cause analysis and intelligent recommendations that go beyond AWS-native visibility.
Resources
Transforming AWS CloudWatch and ServiceNow Integration and Workflows with GenAI: Tackle Cloud Complexity
SRE teams are drowning in cloud complexity, manually correlating CloudWatch metrics with ServiceNow incidents while struggling to keep pace. Hawkeye transforms this reality, acting as a GenAI-powered SRE that automates investigations, connects observability data, and delivers real-time root cause analysis.
Model Rocket’s AWS Ops Breakthrough with AI SRE Agent
Model Rocket’s lean engineering team struggled with complex AWS operations, spending hours troubleshooting incidents. By integrating Hawkeye, an AI SRE Agent, they achieved 92% faster incident resolution and improved service reliability. Now, their engineers focus on innovation while AI handles CloudOps seamlessly.
Image Pull Errors: How Hawkeye Streamlines Container Deployment Troubleshooting
AI-powered teammates are transforming IT operations by automating investigations, accelerating decision-making, and reducing manual effort. By analyzing AWS, Kubernetes, and CI/CD telemetry, they pinpoint deployment issues in minutes, ensuring faster and more reliable rollouts. This shift allows SRE teams to focus on innovation rather than firefighting.
