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Business News/ Brand Stories / Best Kubernetes Observability Tools in 2024
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Best Kubernetes Observability Tools in 2024

Here are the top Kubernetes observability tools of 2024, offering end-to-end visibility, timely alerts, enhanced visualization, and correlation of logs, metrics, and traces

Best Kubernetes Observability Tools in 2024Premium
Best Kubernetes Observability Tools in 2024

Kubernetes' ability to automate deployment, scale applications, and manage containerized applications across clusters has made it the go-to container orchestration tool for more than 5.6 million developers.

Given that it started in 2014, this is immense GROWTH!

However, with this increased adoption, security, cost management, and compliance concerns have also grown. For instance,

  • more than one-third of the companies using Kubernetes do not work on the right sizing and pay more, and
  • more than two-thirds of the companies are delaying deployments over security concerns.

Many Kubernetes monitoring and observability solutions have emerged to address these concerns.

Though the best solution among many is always the one that has features specific to your application, there is a general definition of what is the best Kubernetes observability solution.

In general, the best Kubernetes Observability solution provides a multidimensional view of the system's health, including metrics, logs, and traces. It enables teams to detect and diagnose issues swiftly, optimize resource utilization, ensure applications meet performance and reliability standards, and assure the security of applications running in dynamic, distributed environments.

Having tested multiple tools for clients here at ImprovMedia, we compiled the 8 such general best Kubernetes monitoring platforms.

1. Edge Delta Kubernetes Monitoring - Fast, Reliable, Cost-Effective

Fast setup, focus on speed, resource efficiency, remediating issues before they impact end users, and cost-effectiveness combined with comprehensive monitoring capabilities, real-time, actionable insights, and a multidimensional view of Kubernetes environments, going beyond the traditional observability pillars of logs, metrics, and traces, make Edge Delta the best observability tool for Kubernetes.

Its innovative approach to data analysis at source, correlating data streams rather than data sources enabling real-time insights, and emphasis on efficiency address the critical needs of DevOps teams, making it an invaluable asset for ensuring the performance, reliability, and security of dynamic and distributed Kubernetes applications.

Edge Delta Kubernetes Monitoring
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Edge Delta Kubernetes Monitoring

Key Features of Edge Delta Kubernetes Observability Tool:

  • Efficient Ingestion: Seamlessly ingests and handles large data volumes without compromising cost or performance.
  • Simplified Monitoring: Collects metrics without code changes and correlates alerts with logs.
  • Real-time Anomaly Detection: Detects anomalies instantly and provides full context for proactive issue resolution.
  • Streamlined Troubleshooting: Efficiently stores logs and search capabilities and flags anomalies by identifying repetitive logs.
  • Automated Monitoring: Provides alerting and pre-built dashboards for streamlined monitoring from a centralized interface.
  • Resource Assessment: Automatically identifies and maps Kubernetes resources to assess their health status and data size.

2. Datadog Kubernetes Monitoring - Obtain the Highest Level of Visibility of Your Kubernetes

Datadog Kubernetes Monitoring main page
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Datadog Kubernetes Monitoring main page

Datadog's Kubernetes monitoring tool, with its comprehensive monitoring capabilities, scalability, and user-friendly design, is at the top of every best Kubernetes observability tools list.

Its innovative features, full-stack visibility into Kubernetes environments, and in-time insights simplify the complicated task of monitoring and optimizing the health and performance of complex Kubernetes clusters, pods, and nodes.

Key Features

Automatic Service Monitoring: Automatically detects and monitors services, removing the need for manual setup.

Machine Learning Insights: Utilizes machine learning to spot unusual trends in infrastructure and application metrics, enhancing issue detection.

Extensive Integration: It offers comprehensive visibility into Kubernetes environments with 700+ integrations.

Customizable Troubleshooting: Enables precise issue scoping within clusters for faster troubleshooting and customizable dashboards for monitoring specific areas, aiding both problem resolution and team onboarding.

Essential Metrics and Insights: Facilitates monitoring of memory, CPU, and disk usage, along with critical Kubernetes services like the API server and schedulers, ensuring they operate correctly.

Scalable Data Management: The Cluster Agent streamlines data collection in large environments, while autodiscovery keeps track of new pods and nodes, adjusting monitoring setups automatically.

Comprehensive Data Correlation: Integrates metrics, traces, and logs for a unified infrastructure view, accelerating troubleshooting and enhancing decision-making.

Proactive Problem Solving: Offers preset alerts for proactive monitoring, allowing teams to address issues before they escalate.

3. Prometheus + Grafana: Proactive and Comprehensive Kubernetes Observability

Grafana Kubernetes Monitoring Page
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Grafana Kubernetes Monitoring Page

Prometheus + Grafana is a powerful combination for Kubernetes observability. Together they can offer detailed monitoring capabilities, customizable visualizations, and proactive alerting for managing complex, distributed systems. Scalability, flexibility, and the ability to provide federated monitoring across multiple clusters make this combination an excellent choice for organizations looking to maintain high visibility into their Kubernetes clusters.

  • Prometheus is an open-source monitoring and alert system designed to handle the highly dynamic nature of container-based architectures. It collects and stores metrics as time series data, enabling users to query and analyze their infrastructure and applications' performance.
  • Grafana is an open-source analytics and monitoring solution that complements Prometheus by providing a powerful platform for visualizing time series data. It offers customizable dashboards that allow users to create and share visual representations of their metrics, making it easier to understand complex data at a glance.

Key Features and Benefits

  • Comprehensive Monitoring: Together, Prometheus and Grafana offer a complete monitoring solution, capturing a wide range of metrics from Kubernetes clusters, including node, pod, and container metrics, as well as application performance data.
  • Dynamic Service Discovery: Prometheus supports dynamic service discovery, automatically adapting to changes in the cluster, such as adding or removing nodes, to ensure all relevant metrics are collected without manual intervention.
  • Customizable Dashboards: Grafana provides customizable dashboards, enabling users to tailor their monitoring views to meet specific needs. These dashboards can display various metrics for the entire cluster or specific components, including CPU usage, memory consumption, network IO, and more. 
  • Alerting Mechanism: Both Prometheus and Grafana support robust alerting mechanisms, allowing users to configure alerts based on specific metrics thresholds or conditions. This helps in proactive monitoring and incident response.
  • Scalability and Flexibility: The Prometheus and Grafana stack scales well with Kubernetes, offering the flexibility needed to monitor clusters of various sizes and complexities. This scalability is crucial for growing applications and infrastructure.
  • Federated Monitoring Across Clusters: For organizations managing multiple Kubernetes clusters, Grafana enables federated monitoring, allowing for centralized analysis and visualization of data from all clusters.
  • Enhanced Observability with Custom Dashboards: Utilizing custom dashboards, such as those from the dotdc/grafana-dashboards-kubernetes project, enhances observability by incorporating community-driven insights and optimizations for container and pod sizing, resource usage visualization, and more.
  • Ease of Container and Pod Sizing: The integration of features for easier container and pod sizing using Kubernetes requests and limits enables more efficient resource management and optimization.
  • Cross-Linking Between Dashboards: Data links between dashboards, such as those between nodes and pods dashboards, facilitate a more interconnected and seamless navigation experience, enhancing the analysis process.

4. New Relic Kubernetes Observability Tool - Pixie + Prometheus

New Relic provides a unified platform for Kubernetes monitoring, combining the automatic instrumentation capabilities of Pixie with the metric-focused strengths of Prometheus. This powerful combination within New Relic's ecosystem offers users data insights, from high-level cluster performance metrics to granular details like CPU and memory utilization, kubelet pod start times, and compute resource allocation across pods, nodes, clusters, and namespaces.

Key Features

  • Effortless Setup with Pixie: Jumpstart your Kubernetes monitoring with minimal configuration, thanks to Pixie's automatic instrumentation.
  • Comprehensive Observability Quickstart: Benefit from 23 predefined alerts to detect performance shifts, seamlessly integrated with communication tools for immediate notifications.
  • Enhanced Security and Privacy: Pixie's edge-based data collection ensures sensitive information remains within the cluster, mitigating security risks.
  • Cluster Explorer Visualization: Gain a holistic view of cluster performance, from the control plane down to individual pods, all on a single screen.
  • Predefined and Customizable Alerts: Address issues swiftly with predefined alerts and tailor your monitoring strategy with custom alert policies.
  • In-depth Custom Queries: Dive deeper into your Kubernetes environment with custom queries for a thorough analysis of containers, nodes, pods, and more.
  • Immediate Insights: Achieve real-time visibility into your clusters, accessing essential metrics, traces, and logs for quick diagnostics and resolution.
  • Targeted Deep Dive Analysis: Uncover detailed insights into specific Kubernetes components, improving troubleshooting and optimization.
  • APM and Events Integration: Correlate application performance data with Kubernetes metrics for a holistic monitoring approach and navigate Kubernetes events with ease.
  • Efficient Pod Autoscaling: Automatically scale pods to meet fluctuating demands efficiently.

5. IBM Instana Kubernetes Observability Tool - Dynamic, Real-Time Monitoring

IBM, a leader in technology and development, empowers innovation and enhances global infrastructure. Its clients, including Altissia and PathMotion, have seen significant improvements, such as 99.8% uptime and a 10% reduction in delivery latency, using Instana for website and microservices monitoring. Instana's seamless integration with IBM® Turbonomic® and other solutions offers a comprehensive view of application performance, requiring no plugins or restarts.

Instana simplifies Kubernetes service discovery and mapping through AI-driven automation, ensuring continuous, full-coverage visibility. It provides a dynamic, real-time understanding of application relationships, comprehensive stack monitoring to prevent outages, and full-stack observability of Kubernetes environments. With features like health monitoring, performance optimization, real-time alerts, and automatic data correlation, Instana makes managing Kubernetes' complexity more manageable..

Automated Discovery and Mapping: Instana uses AI to automate the discovery and mapping of Kubernetes services and components, ensuring continuous visibility without sampling and incomplete traces.

Contextual Diagnosis: Dynamic graphs model real-time relationships between application components, aiding in issue diagnosis and impact assessment.

Comprehensive Monitoring to Eliminate Blind Spots: Real-time monitoring of the entire Kubernetes stack prevents outages by capturing all important metrics and traces.

Full-Stack Kubernetes Observability: Automatic monitoring of services, APIs, and microservices on Kubernetes, with real-time monitoring configured automatically.

Health Monitoring and Performance Optimization: Users obtain actionable insights about their Kubernetes environment health, helping them address the performance of bottlenecks at various levels.

Real-Time Alerts and Notifications: The platform allows customizable alerts triggered by metrics or events, with notifications delivered via email, Slack, or other messaging platforms.

Real-Time Correlation: The tool automatically correlates all performance, event, and configuration data, simplifying the analysis of Kubernetes' impact on service levels.

6. Logz.io Kubernetes Observability - Best Observability Tool for Unified Orchestration

Logz.io is not open-source, but it uses the most popular open-source observability platforms, like Grafana and ELK Stack. It provides a single view of your Kubernetes environment and advanced analysis capabilities while ensuring scalability, availability, and security. 

Logz.io's Kubernetes 360 for unified log analytics, Prometheus metrics monitoring, and Jaeger-powered distributed tracing simplifies the consolidation of logs, metrics, and traces for comprehensive insights.

Key Features:

  • Seamless Setup: Users can achieve full Kubernetes observability quickly with a simple installation process.
  • Centralized Data Collection with Logz.io Telemetry Collector: Use a single agent to collect log, metric, and trace data and send it to Logz.io's Platform.
  • Out-of-the-Box Insights: Get immediate visibility into Kubernetes health and performance with K8s 360, requiring no dashboard setup.
  • Swift Root Cause Analysis: Easily drill down into issues using log, metric, and trace correlation for fast troubleshooting.
  • Full Stack Visibility: Centralize and visualize data from every Kubernetes component, including nodes, Control Plane, containers, and apps.
  • Control Plane Monitoring: Monitor every Control Plane component's critical metrics, like non-2000 HTTP response codes, to ensure smooth cluster operation.
  • Correlation Across Signals: Correlate logs, metrics, and traces to identify infrastructure issues quickly.
  • Cloud-Native SaaS Integrations: Seamlessly collect observability data using open-source technologies such as OpenTelemetry, Prometheus, and Fluentd.
  • Data Optimization Across Clusters: Filter unnecessary data to reduce noise and storage costs without compromising performance.

7. Splunk Kubernetes Monitoring - Best for Its Fully-Automated  Kubernetes Monitoring

Splunk empowers DevOps and site reliability teams with options like instant hierarchical view of nodes, pods, and containers, AI-driven analytics for quick anomaly detection, automated monitoring, streamlined root cause analysis with zero-touch configuration, and dynamic cluster maps.

By integrating infrastructure, application data, and logs on a single platform, Splunk enhances end-to-end observability, improving hybrid cloud performance by reducing downtime and mean time to remediation.

Key Features:

  • Easily Monitor Kubernetes Behavior: Receive an immediate and comprehensive hierarchical view of your nodes, pods, and containers.
  • Effortlessly Understand Kubernetes Data With Context: Splunk will bring your Kubernetes data with infrastructure data, application data, and logs without context switching.
  • Leverage AI-powered Analytics: With data-driven analytics, experience faster troubleshooting by identifying anomalous pods and services.
  • End-to-End Visibility: Utilizes the Splunk Smart Agent, deployed as a DaemonSet, to collect high-resolution, real-time metrics from Docker, Kubernetes, and the services within your containers, ensuring immediate visibility into your environment.
  • Automatic Microservices Discovery: The Smart Agent automatically discovers microservices running in containers and configures metrics for supported Splunk integrations, streamlining the monitoring process.
  • Dynamic Updates for Charts and Alerts: Charts and alerts in Splunk are updated in seconds, accurately tracking the dynamic behavior of Kubernetes Pods, and enhancing real-time observability.
  • Directed Troubleshooting with APM: Splunk Application Performance Monitoring (APM) guides you from an alert to the root cause of issues by correlating metrics and traces across the stack, simplifying the troubleshooting of microservices.

8. Elastic Observability - Customizable Alerts, Dashboards And Advanced Automation.

Elastic Observability simplifies Kubernetes monitoring by offering a comprehensive solution to manage and analyze metrics, logs, and traces from Kubernetes deployments. It addresses the challenges of data sprawl and complexity by providing a unified platform for observability data, enhancing the operational efficiency and reliability of Kubernetes clusters.

Centralized Data Storage with Elasticsearch: Serves as the heart of the ELK stack, storing all observability data, including logs, metrics, and other data types, enabling quick search and analysis.

Log Parsing and Enrichment with Logstash: Enhances the value of observability data through parsing and enrichment, utilizing a wide range of plugins for data processing and integration.

Interactive Data Visualization with Kibana: Allows users to create customizable dashboards for visualizing and sharing insights into Kubernetes environments, making data analysis intuitive and actionable.

Comprehensive Data Collection with Beats: A suite of lightweight agents (Filebeat, Metricbeat, Heartbeat, and others) deployed within the Kubernetes cluster for targeted data collection, including logs, metrics, uptime, and network data.

Unified Agent with Elastic Agent: Simplifies the deployment and management of observability agents by replacing individual Beats, streamlining data collection across the Kubernetes environment.

Seamless Integration and Automation: The ELK stack integrates seamlessly with Kubernetes, automating the discovery and monitoring of services, pods, and nodes, ensuring continuous and comprehensive observability without manual intervention.

Proactive Monitoring and Alerting: Enables the setup of customizable alerts based on specific conditions within the Kubernetes infrastructure, leveraging Elasticsearch's search capabilities for intelligent alerting and early issue detection.

The Ultimate Handbook: How To Choose The Best Kubernetes Observability Tool in 2024

An effective Kubernetes observability tool offers valuable insights into cluster behavior and performance. It must also facilitate proactive alerting, efficient troubleshooting, and resource optimization. Additionally, it should help organizations detect and mitigate threats in real-time.

Choose a Tool that Delivers Valuable Insights Based on the Three Pillars of Observability

The main pillars of observability consist of three data outputs: metrics, logs, and traces. 

  • Metrics: Simply refers to numerical values that define a system’s health. In many scenarios, these metrics are CPU, memory usage, disk utilization, etc. Some observability tools like Edge Delta allow users to set and view custom metrics depending on their business’s needs.
  • Logs: These are current reports and historical records of your systems. Logs are machine-generated and help you determine the current state of your system. Although logs are simple to collect, storing and analyzing them poses a challenge to traditional tools. A great observability tool will efficiently store, search, and flag logs with anomalies for faster problem-solving.
  • Traces: Tracing consists of tracking user requests across microservices, represented by end-to-end user requests in single or multiple spans. These traces will provide contextual information on microservices requests. Traces must be reliable as they provide background information on other parts of your data.

A good Kubernetes observability tool will successfully look into these three pillars simultaneously, connecting and analyzing them to provide valuable insights and a holistic view of the system. The best Kubernetes observability tool will also utilize these pillars to diagnose issues that could interrupt the company’s objectives.

Consider its Ability to Reduce Downtime

Data downtimes cause productivity loss, increase operational costs, and decrease revenue. They also cost time. An effective Kubernetes observability tool will determine any anomalies instantly and alert you immediately, efficiently avoiding downtime.

An Effective Observability Tool Improves System Performance Across Services

Your containerized applications can handle any increase in demand and scale growing data effectively in the hands of a high-quality Kubernetes observability tool. Moreover, the best observability tool will assist DevOps teams in monitoring, analyzing, and troubleshooting problems in real-time, even ones that are just about to manifest. 

The Best Kubernetes Observability Tool Reduces Operational Costs

In cloud-native setups, the explosions of monitoring data and the increasing quantity of metrics increased the costs and complexity of data observability. This can reduce the overall value of the observability. So it is crucial to support the scale and high cardinality of Kubernetes environments effectively. By optimizing data ingestion rates and cardinality it should focus on essential metrics and efficient data processing that helps organizations avoid the excessive costs associated with storing vast amounts of unused data, making Kubernetes monitoring both more effective and economically viable.

Look for Something With ML and AI for Comprehensive Monitoring Capabilities

Data-driven companies handle terabytes, sometimes, even petabytes, of data, all of which require comprehensive monitoring. Moreover, Kubernetes clusters have various microservices built around them from diverse technologies. These are called heterogeneous environments, and they require complex and well-thought-out configuration. Collecting data from some of these environments is challenging.

Not that accomplishing all of that is impossible manually, but it can be an extensive task. That’s why we highly recommend using a Kubernetes observability tool with machine learning and AI capabilities for more effective and comprehensive monitoring. 

Find a Tool to Conduct Observability at a Scale

As time passes, Kubernetes deployment grows and becomes more complex. This growth creates challenges in monitoring and managing larger clusters. You will need a Kubernetes observability tool that can scale monitoring and logging solutions to handle the growing volume of data.

Opt For A Kubernetes Tool With Time-series Data Collection

Kubernetes are dynamic as pods and containers are frequently created, scaled, and deleted. Its dynamic nature poses a challenge in tracking, monitoring, logging, and configurations. That leads to the bigger problem of accurate and up-to-date insights.

You can address that challenge by leveraging the best Kubernetes-native observability tool for timely data collection and automated monitoring. With that, your Kubernetes environment’s infrastructure will remain in sync and updated.

Choose Something That Will Improve Visualization Capabilities and Troubleshooting

As mentioned, Kubernetes's increasing data volume and dynamic nature demand constant monitoring and complex configurations. With the right Kubernetes observability tool, you can easily visualize the vast data on your system and immediately identify the root cause of issues in your Kubernetes environment.

Agency Name- ImprovMedia

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Published: 30 Mar 2024, 04:06 PM IST
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