As businesses increasingly rely on complex, distributed systems, the ability to gain insights into the performance, health, and security of cloud infrastructure has never been more critical. That’s where the best AWS monitoring tools and practices step in.
AWS offers powerful native tools, alongside open-source and third-party options, to give you end-to-end visibility. With the right setup, you can boost performance, stay compliant, and keep costs in check.
This blog post will examine the foundational principles of AWS observability, explore the implications of the shared responsibility model, and provide an in-depth look at the AWS native monitoring ecosystem.
We’ll examine both infrastructure monitoring solutions and application performance management tools, equipping you with the knowledge to build a robust monitoring strategy for your AWS environment.
Navigating modern AWS infrastructure architectures that span regions and accounts requires more than just surface-level monitoring. It demands a shift toward true observability.
At its core, AWS observability represents the combination of technical instrumentation and strategic insight. This paradigm goes beyond traditional monitoring by weaving together disparate data threads into actionable intelligence, transforming raw metrics into organizational insights.
The three pillars of modern observability
The architecture of AWS observability stands on three foundations:
- Metrics – Quantitative data points (like CPU usage or request rate) that provide a high-level overview of system performance
- Logs – Timestamped records of events that give detailed context and help in debugging and auditing
- Traces – Visual representations of request flows across services, used to detect performance issues in distributed systems
These pillars don’t operate in isolation. A robust observability strategy effectively combines them through correlation analysis.
For example, a spike in a specific metric might be cross-referenced with logs showing recent configuration changes, while traces identify specific microservices causing cascading delays. This triad forms the basis for diagnosing issues that would remain invisible through traditional single-dimensional monitoring.
Shared responsibility in monitoring and observability
The AWS Shared Responsibility Model guides users in thinking about their monitoring and observability obligations.
While AWS takes care of the physical and network infrastructure (security of the cloud), users bear responsibility for security in the cloud, including the observability of their applications and data. This layered accountability demands observability strategies that adapt to this model.
Maturity model for monitoring and observability evolution
The AWS Observability maturity model progresses through distinct evolutionary stages:
1. Foundational monitoring (Collecting telemetry data)
The first step includes defining basic monitoring and alerting on infrastructure metrics. At this stage, there isn’t a well-defined organizational strategy for monitoring, and it is common for different teams to use various tools.
2. Intermediate monitoring (Telemetry analysis and insights)
In this stage, organizations have started standardizing processes for aggregating telemetry data, including metrics, logs, and distributed traces, from diverse operational landscapes spanning on-premises data centers and cloud platforms.
Although data collection works fine, organizations tend to spend a lot of time on analysis, debugging, and issue resolution, often overwhelmed by the volume of data and the extra cognitive load.
3. Advanced observability (Correlation and anomaly detection)
Here, organizations can find the root of issues without spending a lot of time debugging. Teams can effectively correlate metrics, logs, and traces to understand problems holistically and achieve swift remediation and healthy service metrics.
4. Proactive observability (Automatic and proactive root cause identification)
In this advanced stage of observability maturity, organizations leverage data not just reactively but proactively to prevent issues before they occur. They leverage predictive analytics and machine learning models, automated resolution backed by Amazon EventBridge and AWS Lambda, and AI-driven insights to automatically fetch context and analysis for a deeper understanding of system behavior.
The AWS monitoring landscape contains a carefully curated collection of specialized tools that work together to diagnose every aspect of your cloud environment.
This ecosystem doesn’t just watch your infrastructure; it aligns with your architecture through metrics, traces, and logs, transforming raw operational data into actionable intelligence.
AWS monitoring tools are services and integrations provided by Amazon Web Services and other third parties that help track, analyze, and respond to performance, availability, and resource usage across AWS environments.
AWS monitoring tools include:
Let’s start with the best AWS monitoring tools for the infrastructure:
1. Amazon CloudWatch
CloudWatch operates as the central nervous system of AWS monitoring, constantly pulsing with data from almost all the AWS services.
Beyond basic metric collection, CloudWatch’s Metric Streams feature enables real-time data piping to third-party analytics platforms. At the same time, Metric Math allows engineers to create derived custom metrics through mathematical transformations of raw data points.
Metrics and statistics
CloudWatch collects and tracks AWS monitoring metrics, which measure the performance and health of your AWS resources and applications. These metrics include system-level metrics (e.g., CPU utilization, memory usage, network traffic), application-level metrics, and custom metrics defined by users.
Metrics are stored in namespaces, allowing for easy organization and segregation of data from various services and applications.
CloudWatch offers two levels of monitoring for metrics:
- Basic monitoring – Provides metrics at five-minute intervals and is available at no additional cost
- Detailed monitoring – Offers one-minute granularity, enabling quicker detection of performance anomalies (additional charges apply)
Alarms and automated actions
CloudWatch allows users to set alarms based on metric thresholds. When a threshold is breached, CloudWatch can send notifications, trigger automated actions (e.g., scaling resources, shutting down underutilized instances), or integrate with other AWS services like Auto Scaling, Amazon SNS, and Lambda for more complex responses and actions.
Dashboards and visualization
CloudWatch provides customizable dashboards for visualizing metrics and logs. Users can create reusable graphs, visualize metrics and logs side by side, and gain a unified view of operational health across resources and applications.
Logs and log analytics
CloudWatch Logs allows users to collect, monitor, and analyze log data from various AWS services and custom applications. Features include log storage and retention, log analysis using CloudWatch Logs Insights, and even the creation of metrics from log data.
Container insights
Container Insights provides monitoring and troubleshooting capabilities for containerized applications and microservices. It collects and aggregates metrics and diagnostic information from containers running on services like Amazon ECS and Amazon EKS.
Lambda insights
Lambda Insights is a monitoring and troubleshooting solution for serverless applications running on Lambda. It provides:
- Collection and aggregation of system-level metrics (CPU time, memory, disk, and network usage)
- Diagnostic information on cold starts and Lambda worker shutdowns
- Lambda Insights dashboard with multi-function overview and single-function view
- Near real-time metrics and logs for each Lambda function invocation.
Database Insights
Database Insights is a comprehensive database observability solution designed for DevOps engineers, application developers, and database administrators. It offers
- A consolidated view of logs and metrics for databases
- Pre-built dashboards and recommended alarms
- Fleet-level database health monitoring
- Instance-level dashboards for detailed database and SQL query analysis
- Integration with CloudWatch Application Signals for correlation between application and database performance.
Network Flow Monitor
Network Flow Monitor is a feature of Amazon CloudWatch Network Monitoring that provides near real-time visibility into network performance. Key aspects include:
- The collection of performance and availability metrics for network flows
- Near real-time metrics on latency and packet loss for TCP-based traffic within VPC networks
- The ability to identify underlying AWS network issues through network health indicator (NHI) values
Contributor Insights
Contributor Insights analyzes time-series data to identify top contributors influencing system performance. It helps users isolate and diagnose operational issues, understand which resources, customer accounts, or API calls are impacting performance, and evaluate patterns in log events in near real-time.
2. AWS CloudTrail
CloudTrail serves as a critical infrastructure monitoring tool, logging all API calls in AWS environments. Advanced users employ CloudTrail Insights to detect unusual patterns like bursts of API calls indicating suspected activity or unauthorized requests indicating potential breach attempts.
Another use of CloudTrail is to provide a log of all the actions and API calls performed for auditing purposes.
Native AWS monitoring tools for security include:
3. Amazon GuardDuty
GuardDuty performs infrastructure security monitoring through continuous threat detection.
Its machine learning models analyze VPC Flow Logs for east-west traffic anomalies, DNS query patterns revealing potential data exfiltration, CloudTrail management events showing privilege escalation attempts, and scanning of various other AWS services, such as runtime monitoring for EKS, ECS, and EC2.
Furthermore, it offers malware protection for EC2 and S3, Amazon RDS protection with login activity monitoring, and identification of potential security threats when Lambda functions get invoked.
4. AWS Config
Config is a service that continuously monitors, records, and assesses the configuration of AWS resources in your cloud environment. It provides a detailed inventory of AWS resources and the history of their configuration changes.
It also evaluates resource configurations against desired settings and helps with compliance auditing and security analysis capabilities.
AWS Config enables organizations to maintain compliance with internal policies and regulatory standards, detect unauthorized changes, and support effective cloud governance.
5. Amazon Detective
Detective is an AWS service that simplifies security investigations and root cause analysis of potential security issues or suspicious activities in your AWS environment. It automatically collects and analyzes log data from various AWS sources, including CloudTrail, VPC Flow Logs, GuardDuty findings, and EKS audit logs.
Using machine learning, statistical analysis, and graph theory, Detective creates interactive visualizations and a unified view of resource behaviors and interactions over time.
6. AWS Security Hub
Security Hub is a cloud security posture management service that provides a comprehensive view of security alerts and compliance status across AWS accounts. Key features include:
- Centralized security oversight
- Aggregating findings from various AWS services and third-party solutions
- Automated security assessments based on industry standards and best practices
Furthermore, it enables you to automatically find updates and remediation through custom actions and integrations, and it supports reporting against compliance frameworks such as CIS, PCI DSS, and NIST.
7. AWS X-Ray
X-Ray transforms application monitoring by dissecting request flows and analyzing their paths through complex microservices architectures. X-ray includes service map generation and auto-discovery of service dependencies and latency hotspots.
It also inspects individual service components down to the database query level and provides an annotation system that tags traces with custom metadata for business context.
8. CloudWatch RUM (Real User Monitoring)
CloudWatch RUM is a service that allows you to collect and analyze real-time performance data from actual user sessions on your web applications. RUM collects metrics such as page load times, client-side errors, and user behavior and provides insights into user sessions, including navigation patterns and popular features.
It also offers customizable dashboards for visualizing performance metrics and enables quick identification and debugging of client-side performance issues.
RUM can greatly help with understanding user impact across different browsers, devices, and geographic locations.
9. CloudWatch Application Signals
CloudWatch Application Signals is an automatic instrumentation tool for applications running on AWS. It automatically collects metrics and traces from applications without manual coding
and provides pre-built, standardized dashboards showing key performance metrics.
In addition, it allows the creation and monitoring of Service Level Objectives (SLOs) and can help correlate telemetry across metrics, traces, logs, real user monitoring, and synthetic monitoring. All in all, this feature offers an integrated experience for analyzing performance in the context of applications.
10. CloudWatch Synthetics
CloudWatch Synthetics allows users to create canaries – configurable scripts that monitor endpoints and APIs. These canaries can help you run tests on endpoints 24/7 and discover issues as fast as possible.
Apart from the AWS monitoring tools, several open-source options can be used on AWS to track system performance, application metrics, and infrastructure health.
11. OpenTelemetry & ADOT
OpenTelemetry is an open-source observability framework that can be integrated with the rest of the AWS observability ecosystem for enhanced monitoring capabilities. This combination offers standardized collection and export of telemetry data from both AWS and non-AWS services.
To achieve this, AWS offers direct integration with the AWS Distro for OpenTelemetry (ADOT) for a simplified setup. ADOT offers automatic Instrumentation with pre-configured collectors for multiple services (e.g., ECS, EC2, EKS), eliminating manual coding through managed layers and sidecars.
It also offers multi-destination exports through a single instrumentation point that can feed metrics to AWS native or open-source monitoring services or any other third-party observability tool while maintaining OpenTelemetry’s standard conventions.
It is easy to leverage the OpenTelemetry framework to enforce consistent telemetry collection across EC2, EKS, on-premises, and multicloud deployments.
12. Amazon Managed Grafana
Amazon Managed Grafana (AMG) is a fully managed data visualization service that enables users to query, analyze, and visualize operational metrics, logs, and traces from multiple data sources. AWS handles the provisioning, scaling, and maintenance of Grafana servers, eliminating the need for manual infrastructure management.
AMG allows visualization of data from various cloud environments and projects and native integration with services like CloudWatch, X-Ray, and Amazon Managed Service for Prometheus, enabling easy data access across multiple AWS accounts and regions.
13. Amazon Managed Service for Prometheus
Amazon Managed Service for Prometheus (AMP) is a fully managed, serverless monitoring service that provides a scalable and secure platform for collecting, storing, and querying Prometheus-compatible metrics.
It automatically scales to handle large volumes of metrics without manual infrastructure management and offers native PromQL support without modifying existing dashboards and alerts. AMP simplifies the process of setting up and maintaining Prometheus at scale.
In addition to open-source options, many third-party proprietary monitoring tools are widely used with AWS for enhanced observability, automation, and analytics.
Here are some of the most popular tools:
- Datadog: A comprehensive monitoring and analytics platform that integrates deeply with AWS services, it provides infrastructure, application, and log monitoring with built-in alerting and dashboards.
- New Relic: Known for full-stack observability, New Relic offers real-time insights into AWS workloads, including APM (Application Performance Monitoring), logs, and infrastructure.
- Dynatrace: This uses AI-driven monitoring for applications, infrastructure, and cloud environments. It offers deep AWS integration and automatic root cause detection.
- Splunk Observability Cloud: This combines logs, metrics, and traces to deliver full-stack monitoring for AWS environments. It’s especially strong in log analytics and security event monitoring.
- AppDynamics (by Cisco): This focuses on application performance and business transaction monitoring, with AWS integration for infrastructure visibility.
- LogicMonitor: This provides automated discovery and monitoring for AWS resources, with detailed performance analytics.
These tools often offer SaaS deployment models, quick AWS integration via APIs or CloudFormation, and advanced features like machine learning for anomaly detection.
Designing an effective monitoring and observability strategy requires balancing AWS-native services, open-source tools, and organizational requirements.
Below, we explore modern patterns and principles for building scalable, secure, and cost-efficient observability systems on AWS.
1. Define clear monitoring goals and strategy
Establishing observability begins with aligning instrumentation efforts to business priorities through systematic planning. Teams must first catalog mission-critical resources and map their operational health to revenue-impacting outcomes.
For each identified component, organizations define measurement frequency balancing granularity against cost, such as collecting CPU utilization metrics every minute but sampling application logs hourly for trend analysis.
These technical decisions feed into incident response playbooks that formulate escalation paths, ensuring that metric threshold breaches trigger predefined remediation workflows rather than ad hoc interventions.
2. Multi-layer telemetry collection framework
Modern systems demand instrumentation strategies spanning infrastructure, applications, and business processes. AWS Distro for OpenTelemetry (ADOT) provides unified data collection across different programming languages through automatic trace propagation and metric aggregation, minimizing code instrumentation overhead.
Serverless architectures benefit from ADOT Lambda Layers that capture X-Ray traces and CloudWatch metrics without requiring function code modifications. Beyond default metrics, teams implement CloudWatch Custom Metrics to track domain-specific and custom indicators, enriched with resource tags (Environment=Production, Team=Blue) for cross-dimensional analysis.
Structured logging practices enforce JSON formatting with standardized fields (timestamp, severity, correlation IDs), enabling CloudWatch Logs Insights to filter operational signals from noise.
3. Efficient alerting and automated response systems
Traditional threshold-based alerting gives way to adaptive systems leveraging AWS machine learning capabilities.
CloudWatch Anomaly Detection can automatically adjust alert boundaries for various patterns like nightly batch processing spikes or holiday sales traffic.
Service Level Objective (SLO) monitoring shifts focus from infrastructure uptime to user experience, with CloudWatch Synthetics validating API response times against business agreements (e.g., 95th percentile under 200ms) while Managed Prometheus helps track metrics across complex microservices.
Integrated automation workflows trigger Lambda functions or AWS Systems Manager documents when issues arise, such as restarting unresponsive EC2 instances or rolling back problematic ECS task revisions without human intervention.
4. Centralized observability architecture
With modern, enterprise-scale, complex, multi-region, and multi-account architectures, the centralized hub-and-spoke model has emerged as the gold standard for enterprises.
A dedicated monitoring account aggregates telemetry data from workload accounts across regions and environments. This pattern attempts to centralize metrics, logs, and trace collection without manual data replication to a single source of truth, often leveraging CloudWatch Cross-Account Observability capabilities.
5. Open standards adoption through OpenTelemetry
ADOT simplifies instrumentation by collecting metrics, logs, and traces in OpenTelemetry specifications, which are compatible with AWS and third-party tools like Prometheus and Grafana.
For serverless applications, the ADOT Lambda Layer auto-instruments functions, exporting data to CloudWatch, X-Ray, or Managed Prometheus. The adoption of open standards offers flexibility to support even hybrid or multi-cloud environments, such as on-premises or other cloud environments emitting telemetry to AWS.
6. AI-Driven Operational Intelligence Integration
AWS’s AI capabilities transform observability from reactive monitoring to proactive optimization. CloudWatch Anomaly Detection employs machine learning to refine alert thresholds based on metric feedback loops, reducing false positives during infrastructure scaling events.
Amazon Q Developer operational investigations offer a generative AI-powered assistant that can help you respond to incidents in your system. It correlates CloudWatch metric anomalies with X-Ray trace maps and CloudWatch Application Signals, automatically generating incident timelines and root-cause hypotheses.
Predictive Scaling for EC2 Auto Scaling analyzes historical load data to detect recurrent patterns in traffic flows. It uses this information to forecast future capacity needs, so Amazon EC2 Auto Scaling can proactively increase the capacity of your Auto Scaling group to match the anticipated load.
7. Security observability integration
In AWS environments, security is tightly related to observability. In alignment with AWS’s Shared Responsibility Model, observability architectures incorporate effective security monitoring.
Configuration compliance tracking uses AWS Config rules to detect security configuration drift, alerting via EventBridge when deviations happen. Identity analytics identify anomalous IAM credential usage patterns that could indicate compromise attempts with the help of services such as CloudWatch and GuardDuty. Data protection monitoring correlates network traffic with data access logs, detecting potential exfiltration attempts through unexpected cross-account object copies.
Finally, AWS Security Hub aggregates findings from all the other security services into unified dashboards, enabling SOC teams to prioritize issues and vulnerabilities based on operational context.
8. Continuous improvement cycle
Maintaining observability effectiveness requires ongoing improvement cycles across people, processes, and tools. Post-incident reviews conducted through AWS Incident Manager identify monitoring gaps.
Proactive resilience testing via AWS Fault Injection Service tests observability coverage by simulating various failures and assessing detection capabilities.
Financial governance integrates AWS Cost Explorer reports with observability data, identifying underutilized instances and right-sizing recommendations from AWS Compute Optimizer.
Spacelift allows you to connect to and orchestrate all of your infrastructure tooling, including infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.
It enables powerful CI/CD workflows for OpenTofu, Terraform, Pulumi, Kubernetes, and more. It also supports observability integrations with Prometheus and Datadog, letting you monitor the activity in your Spacelift stacks precisely.
The platform enhances collaboration among DevOps teams, streamlines workflow management, and enforces governance across all infrastructure deployments. Spacelift’s dashboard provides visibility into the state of your infrastructure, enabling real-time monitoring and decision-making, and it can also detect and remediate drift.
You can leverage your favorite VCS (GitHub/GitLab/Bitbucket/Azure DevOps), and executing multi-IaC workflows is a question of simply implementing dependencies and sharing outputs between your configurations.
Global payments platform Checkout.com committed itself to the goal of “IaC for everything,” and Spacelift delivered, offering a platform that teams could start using independently with minimal configuration — all within the constraints of the regulated environment Checkout.com operates in.
With Spacelift, you get:
- Multi-IaC workflow
- Stack dependencies: You can create dependencies between stacks and pass outputs from one to another to build an environment promotion pipeline more easily.
- Unlimited policies and integrations: Spacelift allows you to implement any type of guardrails and integrate with any tool you want. You can control how many approvals you need for a run, which resources can be created, which parameters those resources can have, what happens when a pull request is open, and where to send your notifications data.
- High flexibility: You can customize what happens before and after runner phases, bring your own image, and even modify the default workflow commands.
- Self-service infrastructure via Blueprints: You can define infrastructure templates that are easily deployed. These templates can have policies/integrations/contexts/drift detection embedded inside them for reliable deployment.
- Drift detection & remediation: Ensure the reliability of your infrastructure by detecting and remediating drift.
If you want to learn more about Spacelift, create a free account today or book a demo with one of our engineers.
In this blog post, we explored why effective observability in your AWS environment requires a layered approach: foundational services (CloudWatch, X-Ray, AMP, AMG), architectural patterns (centralized logging, hybrid monitoring), and advanced techniques (AIOps, SLOs).
By aligning instrumentation and collection with business KPIs, automating responses, and embracing OpenTelemetry, organizations can achieve resilience in complex environments.
Take DevOps monitoring to the next level
Spacelift is a infrastructure orchestration platform that allows you to connect to and orchestrate all of your infrastructure tooling, including monitoring, infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.
