Review of AI Tools for Cloud Monitoring and Observability

Cloud monitoring and observability are essential practices for ensuring the availability, performance, and security of cloud-based systems and applications. Cloud monitoring and observability involve collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the cloud environment, such as metrics, logs, traces, and user experience.

However, cloud monitoring and observability can also be challenging and complex, as cloud environments are dynamic, distributed, heterogeneous, and scalable. Traditional monitoring and observability tools may not be able to cope with the volume, velocity, variety, and veracity of cloud data and events. Moreover, human operators may not be able to process and act on the data and events in a timely and effective manner.

This is where artificial intelligence (AI) tools can help. AI tools can leverage machine learning (ML), natural language processing (NLP), computer vision (CV), and other techniques to enhance cloud monitoring and observability capabilities. AI tools can provide benefits such as:

  • Automated data collection and ingestion from various sources and formats
  • Intelligent data processing and analysis to identify patterns, anomalies, correlations, and causations
  • Actionable insights and recommendations to optimize performance, reliability, security, and cost
  • Automated remediation and resolution of issues using predefined or self-learning actions
  • Enhanced user interface and user experience using natural language or visual interactions

In this article, we will explore some of the AI tools that are used or can be used for cloud monitoring and observability. We will also review some of the features, benefits, and challenges of these tools.


Dynatrace is a software intelligence platform that provides comprehensive observability for hybrid and multi-cloud ecosystems. Dynatrace uses AI to automate data collection and analysis, provide actionable answers to performance problems, optimize resource allocation, and deliver superior customer experience.

Some of the features of Dynatrace are:

  • Automatic discovery and instrumentation of all applications, containers, services, processes, and infrastructure
  • Real-time topology mapping that captures and unifies the dependencies between all observability data
  • Causation-based AI engine that automates root-cause analysis and provides precise answers
  • OpenTelemetry integration that extends the breadth of cloud observability
  • Scalability and efficiency that ensure complete observability even in highly dynamic environments

Some of the benefits of Dynatrace are:

  • Simplified procurement and management of cloud observability tools
  • Enhanced visibility and correlation across multiple sources and types of data
  • Improved scalability and performance of cloud observability solutions

Some of the challenges of Dynatrace are:

  • Reduced negotiating power and flexibility with vendors
  • Potential single points of failure or compromise in case of vendor breaches or outages
  • Increased dependency on vendor support or updates

IBM Observability by Instana APM

IBM Observability by Instana APM is a solution that provides end-to-end visibility into serverless applications on AWS Lambda. IBM Observability by Instana APM uses AI to collect metrics, logs, and traces from AWS Lambda functions, provide real-time dashboards, alerts, and insights into the performance, errors, costs, and dependencies of serverless applications.

Some of the features of IBM Observability by Instana APM are:

  • Agentless data ingestion that does not require any code changes or configuration
  • Domain-specific AI engine that enables data organization and analysis
  • High-cardinality view that allows filtering and slicing by any attribute or dimension
  • Distributed tracing that supports OpenTelemetry standards
  • Cost optimization that monitors usage and cost of serverless functions

Some of the benefits of IBM Observability by Instana APM are:

  • Easy deployment and integration with AWS Lambda
  • Comprehensive coverage and granularity of serverless data
  • Fast detection and resolution of serverless issues

Some of the challenges of IBM Observability by Instana APM are:

  • Limited support for other serverless platforms or providers
  • Dependency on AWS services for data storage or streaming
  • Potential data privacy or sovereignty issues

Elastic Observability

Elastic Observability is a solution that provides unified observability for hybrid and multi-cloud ecosystems,
including AWS, Azure, Google Cloud Platform, and more. Elastic Observability allows users to ingest telemetry data from various sources such as logs, metrics, traces, and uptime using Elastic Agents or Beats shippers It also provides powerful search, analysis, and visualization capabilities using Elasticsearch engine, Kibana dashboard, and Elastic APM service.

Some of the features of Elastic Observability are:

  • Agent-based or agentless data ingestion that supports various protocols, formats, and standards
  • Open source platform that allows customization, extension, and integration
  • Scalable architecture that can handle large volumes of data at high speed
  • Anomaly detection that uses ML to identify unusual patterns or behaviors
  • Alerting framework that supports multiple channels, actions, and integrations

Some of the benefits of Elastic Observability are:

  • Flexible deployment options on-premises, in the cloud, or as a service
  • Cost-effective pricing model based on resource consumption
  • Rich ecosystem of plugins, integrations, and community support

Some of the challenges of Elastic Observability are:

  • Complex installation and configuration process
  • High learning curve for users who are not familiar with Elasticsearch or Kibana
  • Potential security or compliance issues with open source software


AI tools can enhance cloud monitoring and observability capabilities by automating data collection and analysis, providing actionable insights and recommendations, and enabling automated remediation and resolution of issues. We have reviewed some of the AI tools that can be used for cloud monitoring and observability:

  • Dynatrace
  • IBM Observability by Instana APM
  • Elastic Observability

These tools have different features, benefits, and challenges that users should consider before choosing one.