Observability tools are united in a centralized platform that aggregates and visualizes your network’s key statistics and data. This information is sourced from across your applications and infrastructure components, then presented as part of the dashboard, giving admins a vital insight into their network. Observability tools go beyond your standard monitoring solution, they provide a comprehensive insights into your entire system, allowing teams to proactively address potential concerns and enhance overall system performance.
While traditional monitoring tools alert users to known issues, observability platforms delve deeper, shedding light on unknown (and developing) issues. They can highlight intricate dependencies, allowing you to understand the knock-on impact of a failure or issue. Observability tools combine metrics and logs, allowing you access to a holistic view of your network’s performance and health. This approach allows organizations to gain vital information as soon as it is available. This type of solution is particularly useful amongst businesses operating microservice architectures and distributed systems, where pinpointing issues can be akin to finding a needle in a haystack.
An effective observability tool should be proactive. It should provide predictive analytics to highlight potential bottlenecks or failures before they become critical. Additionally, with the rise of DevOps and continuous integration/continuous deployment (CI/CD) practices, they should seamlessly integrate with the development lifecycle, supporting faster releases without compromising on quality.
In this article, we’ve compiled a list of the best observability tools on the market currently. In each case, we’ll identify a solution’s key features and use cases, thereby assisting you in selecting the right solution for your organization.
Cisco AppDynamics is an application performance monitoring platform designed for both cloud-native and on-premises environments. The platform focuses on real-time performance monitoring to offer users insights into their applications’ health and behavior. One of AppDynamics’ primary offerings is real-time monitoring, this allows users to detect potential issues before they affect end-users. For businesses transitioning to the cloud, AppDynamics provides end-to-end visibility to facilitate accurate planning and migration validation. Additionally, the platform emphasizes the correlation between application performance and business results.
Through machine learning capabilities, AppDynamics aids in accelerating root-cause analysis and automating remediation processes. Users can expect comprehensive visibility into their application’s experience, facilitating a proactive approach to performance monitoring. The platform also reduces MTTR by quickly identifying the root causes of issues and correlating software performance with business KPIs. AppDynamics is adaptable to various environments, including public, private, and multicloud settings, ensuring consistent application performance. For larger enterprises, the platform promises scalability through low-overhead monitoring agents. The platform addresses security considerations with a secure-by-design architecture complemented by granular, role-based access controls.
Datadog Observability Pipelines is a comprehensive platform designed to manage logs, metrics, and traces from various sources, allowing users to collect, transform, and route their data to desired destinations, even at a petabyte scale. It emphasizes flexibility and control, facilitating decisions that optimize data volume, routing, compliance, and standardization within an organization’s infrastructure. Datadog’s key features include efficient data ingestion and processing, with the ability to direct specific data to cost-effective storage solutions and retrieve it when required. It offers rule-based data sampling and aggregation, thereby reducing total data volume while preserving essential KPIs and trends.
For data security and compliance, Datadog can redact sensitive data before it exits the infrastructure and provides tools for maintaining compliance with residency laws. The platform also offers data delivery orchestration, allowing data transition from any source to destinations, including on-site locations. This eliminates vendor lock-in and provides flexibility in adopting new technologies. Datadog also prioritizes data quality, offering automatic data parsing, enrichment, mapping to appropriate schemas, and maintaining consistency through enforcing these rules. Users can monitor the performance of their pipelines and are granted an overview of their health and potential bottlenecks—all via a user-friendly interface that makes it easy to build, edit, and deploy pipeline configurations.
Sumo Logic offers an integrated observability platform designed to manage and monitor application data across various environments, including cloud, on-premises, and hybrid setups. The platform provides a comprehensive view of users’ infrastructure, enabling users to address application performance issues proactively and reduce unplanned outages.
One of Sumo Logic Observability’s standout features is its ability to automatically generate application topologies by synchronizing and analyzing traces, logs, and metrics in real-time. The cloud-native platform provides a centralized location for the collection, storage, and search of security information and cloud data, supported by flexible licensing and data tiering. In addition, it facilitates real-time monitoring, alerting, and data analysis across a wide range of security tools, cloud infrastructures, and SaaS applications. Sumo Logic Observability also offers modern log management that enhances monitoring and troubleshooting, strengthens security measures, and helps admins derive pivotal insights. On the security front, Sumo Logic prioritizes data protection by maintaining several compliance certifications such as PCI, HIPAA, FISMA, SOC 2 Type II, GDPR, and FedRAMP.
Dynatrace enhances observability through the incorporation of contextual information, artificial intelligence, and automation. The platform is designed to minimize blind spots in data analysis, streamline problem resolution, and optimize customer experience. It gives users an understanding of the interdependencies in the data it monitors, ranging from user impact to the complex network of entity interdependencies.
Dynatrace’s AI system, Davis, facilitates a detailed root-cause analysis to help pinpoint performance problems. This causation-based AI seeks to relieve human operators from the tedious task of manual root-cause analysis, by offering precise answers automatically. It also offers automatic discovery and instrumentation, which ensures scalability and comprehensive coverage in dynamic environments, eliminating the need for manual configuration. One of its standout features is the Dynatrace OneAgent, a tool designed to instantaneously detect system components such as applications, containers, and services upon startup, initiating immediate high-fidelity data observability with no need for manual configuration or code alterations. The platform can learn and adapt itself to the “normal” performance patterns dynamically, ensuring secure and automated updates throughout the environment and providing a real-time entity topology map that serves as a core mechanism for intelligent observability.
Grafana Cloud Frontend Observability is a hosted service that facilitates real user monitoring (RUM) for web applications. The service offers insights into the end user experience by collecting and analyzing data on various parameters such as page load times, user interactions, and cumulative layout shifts. This enables a more in-depth understanding of application usage and performance, helping businesses optimize their website and application performance based on real-time frontend health indicators.
Grafana Cloud Frontend Observability assists in troubleshooting user-facing issues by reconstructing user behavior that leads up to a specific issue, correlating the data with backend requests to aid in performance issue debugging. It further helps reduce the Mean Time to Repair (MTTR) for front-end errors by assessing the severity of frontend errors based on volume and frequency, investigating each issue with beneficial contextual metadata, and automatically grouping similar errors, which enables investigations down to specific lines of code. Additionally, the service allows the segmentation of performance metrics in ways that align with business goals, offering insights into how different user groups interact with your website. Finally, Grafana Cloud Frontend Observability integrates with Grafana Cloud Logs and visualizes data in Grafana, offering flexible analysis and reporting. This integration ensures that frontend performance data is accessible, manageable, and utilized optimally to enhance the user experience.
IBM Instana specializes in real-time observability for data monitoring and issue resolution across DevOps, SRE, and ITOps. It offers a comprehensive view of performance data, placing it in a context that allows for the swift identification and remediation of potential issues across different platforms including mobile, web, and various applications and infrastructures. A single lightweight agent per host is equipped to discover all components and deploy sensors, which persistently monitor a range of elements including databases, APIs, serverless structures, and containers.
IBM Instana automatically monitors various aspects including application performance, microservices, and Kubernetes in real-time without any sampling. It also integrates capabilities like automatic discovery, mapping of services, and observability metrics ingestion, which together (with threshold-based smart alerts, automatic detection, and correlation of events) aim to decrease the mean time to resolution (MTTR). What sets IBM Instana apart is its unified approach to monitoring mobile applications and websites, which serves as a central data source for understanding user behavior and addressing frontend issues swiftly. The tool integrates seamlessly with other monitoring systems like IBM Turbonomic to offer a holistic view of application performance across the IT infrastructure without the need for plugins or application restarts, thereby aiming to enhance efficiency in troubleshooting and problem resolution.
Prometheus is a robust monitoring system that utilizes a highly dimensional data model to enhance data analysis and visualization. The platform identifies time series through a unique metric name accompanied by a series of key-value pairs, facilitating precise and efficient data management. Central to Prometheus’ functionality is its query language, PromQL, which enables the detailed dissection of collected time series data. This feature facilitates the creation of ad-hoc graphs, tables, and alerts, enhancing the user’s ability to monitor and analyze data effectively.
Prometheus offers a range of visualization modes including a built-in expression browser, Grafana integration, and a console template language. Together, these features make data representation more versatile and user-friendly. In terms of storage and operation, Prometheus is designed for efficiency and simplicity. It stores time series both in memory and on local disk in a custom format, optimizing the use of space and resources. Its operational simplicity is reflected in its independent server functioning, which relies solely on local storage, and its straightforward deployment process facilitated by binaries written in Go. Prometheus also features a flexible and precise alerting system, supported by an array of client libraries and integrations that allow it to incorporate third-party data.
New Relic is an integrated solution for observability, allowing users to analyze a diverse range of telemetry data through one centralized platform. The platform is equipped with full-stack analysis functionalities that facilitate an in-depth analysis of networks, infrastructures, applications, and end-user experiences. New Relic’s full-stack monitoring capability provides a live, comprehensive, and unified observability experience, seeking to eliminate the barriers created by observability silos through the provision of immersive cross-platform experiences, complemented by AI assistance at each stage of utilization.
What makes this product distinctive is its secure and highly scalable data platform, capable of instrumenting all your telemetry data from different sources into a single cloud platform, thereby eliminating the need for sampling. It also aims to democratize observability, fostering an environment where engineers can optimize their work based on data-driven insights throughout the entire software lifecycle, enhancing the precision and efficiency of engineering projects.
The SolarWinds Observability platform is a SaaS solution that enhances visibility across cloud, on-premises, and hybrid systems. It aims to facilitate work for DevOps, IT, and Cloud Ops teams by streamlining the development process of modern applications and infrastructures. SolarWinds Observability offers comprehensive application observability, aiding in the maintenance of both custom and commercial applications, and infrastructure observability to ensure the smooth running of on-premises and cloud-based resources.
Its functionality extends to log observability offering full-stack, multi-source log management, and database observability, which provides deep performance monitoring and analysis capabilities. The platform also offers digital experience observability to help optimize web application customer experiences and network observability for maintaining the health and performance of networks. This comprehensive observability suite integrates seamlessly with SolarWinds Hybrid Cloud Observability, offering a unified view across various environments, thus promising a consolidated and efficient monitoring solution. SolarWinds Observability is equipped with AIOps enhanced with machine learning to simplify the management of distributed environments, coupled with automated instrumentation and dependency mapping. It stands out for its quick installation process and user-friendly interface.
Splunk Enterprise is a software platform aimed at bolstering digital resilience through IT monitoring and analytics. The software suite offers a range of tools to assist teams in quickly identifying and resolving issues, enhancing reliability through predictive analytics, and developing a deep understanding of applications, infrastructure, and user experiences, all in real time. One of the distinguishing features of Splunk Enterprise is its ability to provide insights into both cloud-native and on-premise applications through its Application Performance Monitoring tool. This features NoSample distributed tracing and code-level visibility.
The platform also extends its capabilities to infrastructure monitoring, offering real-time alerts and instant visibility to help improve hybrid cloud performance. Its IT Service Intelligence tool ensures optimal service performance by offering full visibility, AIOps, and incident intelligence. To enhance customer experiences, the software also offers Real User Monitoring, which allows teams to identify and fix customer-facing issues with full visibility into the end-user experience across both web and mobile platforms. The Splunk Synthetic Monitoring tool proactively identifies and resolves performance issues, facilitating smooth user flows and business transactions. Finally, Splunk On-Call automates incident responses to make the on-call process more efficient and less frustrating for teams, aiming to improve business outcomes in the long run.
Everything You Need To Know About Observability Tools (FAQs)
What Is Observability And What Are Observability Tools?
Observability tools allow you to monitor your system’s current state, providing reports and metrics on data and processes. This data includes metrics, traces, and logs. Observability uses data generated by endpoints and services in your multi-cloud computing environment, to grant extensive insight across your entire network. Each asset, be it a device, hardware, software, container, or open-source tool has a record of all activity. Observability helps teams understand events so they can get a wider understanding of their network. From here they can detect and remediate issues faster, ensuring that everything is working correctly and efficiently.
Observability tools act as a centralized platform for aggregating and visualizing this telemetric data. They monitor application behavior and infrastructure, before performing careful analysis on it, then delivering actionable insights. This aids organization’s in being able to spot and address problems before they have a chance to develop. Observability tools integrate a range of monitoring capabilities, ensuring that they can discover deep, meaningful insights to help find issues, optimize performance, and ensure continued availability.
How Do Observability Tools Work?
Every aspect of your environment will generate a record of its activity, giving a wealth of data that, if utilized properly, provides an insight into your entire system. This data can be used to identify areas that need improving, performance levels, and any outstanding and developing issues. Observability tools measure and analyze system performance and health, using this telemetry that comes from endpoints and services in your multi-cloud environment.
Using this data, observability tools can detect, analyze, and help teams understand the significance of events that occur. This gives an insight into network operations, application security, software development life cycles, and end-user experiences. Going beyond monitoring, observability tools can also identify trends and anomalies, send alerts, and will use data optimization and correlation to produce actionable insights.
Top Observability Tool Features To Look For
When looking for an observability tool for your organization, you should consider the following features:
- Compatibility: Ideally, your solution should be compatible with a range of environments, including public, private, and multi cloud settings.
- Visibility: Your solution should have complete, holistic visibility with no blind spots. It should offer a comprehensive view of your entire network in order to deliver the best possible service.
- Data Processing: Data processing should be efficient and fast. Some solutions are able to direct certain data to storage units and retrieve it when needed.
- Security And Compliance: Your chosen solution must have robust security features – for example, redacting sensitive information before it leaves your organization – and adhere to any compliance guidelines relevant to your business.
- Alerting: The solution should continuously monitor telemetry data and alert administrators when certain conditions are met and when they aren’t, helping them to identify and investigate critical events.
- Anomaly Detection: The solution should use artificial intelligence and machine learning to automate anomaly. AI can be used on datasets to understand “normal” behavior and create a baseline from which the solution can identify abnormal behavior.
- Data Optimization: Automated capabilities such as data optimization and storage allow for continuous control over mass volumes of data and any costs associated with it.
- Dashboards: Dashboards should be intuitive, clean, easy to navigate, and updated in real-time. Your solution will be collecting and processing mass volumes of telemetry data which needs to be presented in a digestible way. While customization allows for more flexibility, pre-built dashboards can reduce time spent on configurations.
- Customization: Your solution should be customizable to your specific business needs, including configurations, and dashboards.
- Distributed Tracing: Your solution should profile and monitor applications, honing in on exactly where failures and problems arise and identifying the cause.
- Data Correlation: Data correlation is an important feature that can aggregate and pool data, helping to identify trends across data sets and present it in a unified way.
