Observability vs. Monitoring: Key Differences, Use Cases & Best Practices

Observability vs. Monitoring. Is there even a real difference, or is observability just the latest buzzword on the block? Let’s find out.

Monitoring is your early warning system.
Observability is your detective.

Monitoring tells you that something is wrong.
Observability helps you figure out why it’s wrong.

Think of it like this:

• Monitoring: There’s a fire.
• Observability: Where it started, how it spread, and what caused it.

Used together, they give IT teams powerful, end-to-end capabilities for early issue detection and deep problem-solving — essential for providing a seamless customer experience.

In a highly distributed and dynamic environment, relying on monitoring alone can leave teams stuck in a reactive mode, unable to dig deep enough to diagnose root causes effectively. Observability steps in with the insights needed to proactively investigate, resolve, and even anticipate system disruptions before they escalate.

Recognizing and leveraging the strengths of both monitoring and observability helps you:

• Minimize downtime
• Boost system resilience
• Deliver superior user experiences

At the end of the day, knowing your system inside out is what ensures availability, protects data, and speeds up problem resolution.

Together, observability and monitoring provide the full picture: proactive alerting + powerful diagnostics.

Key takeaways:

• Monitoring provides real-time alerts, letting you know when something’s wrong. Observability equips you with tools (like logs, metrics, and traces) to investigate and understand the root cause of the problem.
• Use monitoring for tracking system health and triggering alerts on known metrics. Use observability when you need deeper insight into unexpected issues, root-cause analysis, and complex system behavior.
• Combining both ensures proactive system health management. Monitoring allows for fast issue detection, while observability provides the depth needed to quickly pinpoint and fix root causes, ensuring faster resolution and minimal downtime.
• Common monitoring and observability tools include: UptimeRobot, Prometheus, Datadog, New Relic, Zabbix, Elastic APM, Dynatrace, Grafana.

What is monitoring?

Monitoring is the practice of periodically collecting, analyzing, and displaying data about a system’s performance, availability, and overall health.

Its core purpose is to detect known issues quickly, trigger alerts, and provide teams with real-time or near-real-time visibility into the system’s status.

Key monitoring characteristics

• Reactive in nature: Monitoring identifies when something has gone wrong based on predefined conditions and thresholds, allowing teams to respond quickly.
• Uses predefined metrics: Monitoring focuses on specific, expected indicators, such as CPU usage, memory consumption, disk space, or API error rates. These metrics are selected in advance based on known risks and performance targets.

Common monitoring tools

Several tools help organizations set up effective monitoring systems, including:

• Nagios: A comprehensive monitoring tool that tracks the health and status of applications, networks, and systems across various platforms like Windows, Linux, and UNIX. 
• Zabbix: An open-source platform that monitors a wide range of system parameters, including servers, virtual machines, networks, cloud services, databases, and applications. It offers real-time visibility into overall IT infrastructure health.
• Prometheus: A powerful open-source tool designed for real-time monitoring, especially in dynamic environments like Kubernetes clusters. Prometheus excels at time-series data collection, service monitoring, and integration with service meshes and proxies.

What is observability?

Observability is the ability to understand the internal state of a system based solely on the external data it produces. When a system is observable, IT teams can detect and resolve the underlying causes of issues.

The core concept of observability goes beyond detecting that something is wrong. It enables teams to ask and answer deeper questions about system behavior, including new or unexpected issues not anticipated earlier.

In other words, observability helps you understand any state your system might enter, not just the ones you planned for. It provides rich context around issues, answering critical questions like:

• Why did it happen?
• How did it happen?
• When did it happen?
• Where did it happen?

By delivering deeper insights, observability empowers teams to move from reactive troubleshooting to proactive diagnostics. It allows teams to identify anomalies, analyze system interactions, and anticipate problems before they impact users. This deep visibility into system behavior is essential for troubleshooting complex, distributed environments.

The three pillars of observability are:

• Metrics: Metrics are structured, numerical data points that measure system performance and resource usage over time. They provide a real-time, high-level view of system health, helping teams monitor trends like latency, error rates, CPU load, memory consumption, and request throughput. 

• Logs: Logs are time-stamped records of discrete events generated by systems, applications, or network components. They capture detailed context about what happened within the system, where it happened, and why it happened.

• Traces: Traces capture the complete path of a request as it moves through a distributed system. They highlight how long each operation takes and where delays or failures occur, making them essential for diagnosing bottlenecks and understanding system dependencies.

Modern architectures like microservices, serverless platforms, and distributed cloud environments are dynamic and complex. Traditional monitoring often falls short because it relies on fixed, predictable system behaviors.

Observability addresses this challenge by providing deep, real-time visibility into every component, service interaction, and dependency across the system. It helps teams:

• Understand how different services communicate and where failures might occur.
• Detect issues that emerge from unexpected system states or behaviors.
• Trace the flow of requests across multiple services to quickly pinpoint performance bottlenecks or failures.
• Adapt to rapid changes and deployments without losing insight into system health.

Key differences between observability and monitoring

While they are related concepts, they serve distinct purposes and offer different capabilities. 

Proactive vs. reactive

Monitoring is primarily reactive. It alerts you when predefined thresholds are crossed, enabling a response to known issues.

Observability is proactive. It allows teams to explore unknown system states, diagnose complex problems, and even anticipate issues before they escalate.

Data scope and depth

Monitoring relies on a limited set of predefined metrics and dashboards that track specific aspects of system health.

Observability uses a broader range of telemetry data (metrics, logs, and traces) to provide a deeper, richer view of system behavior across multiple layers.

System insights vs. system alerts

Monitoring focuses on generating alerts based on specific symptoms. It tells you that something is wrong but offers limited context.

Observability delivers full system insights, helping teams trace the origin of issues, understand their impact, and uncover hidden dependencies.

Real-world examples

Example 1: E-commerce application: Checkout failures

Monitoring: The monitoring system detects a spike in checkout errors and triggers an alert, notifying the team that something is wrong with the checkout process.

Observability: Using observability tools, the team correlates the spike with recent code deployments and traces the issue to a specific microservice updated in the latest release. Observability provides detailed logs, traces, and context, enabling the team to pinpoint the root cause and resolve the issue quickly

Example 2:  Ensuring uptime during major events

Monitoring: Monitoring tools track system uptime and alert the team if the platform goes down during a high-traffic event.

Observability: Observability enables the platform to capture rich, application-level data, understand user journeys, and proactively identify issues affecting user experience. This approach ensures 100% uptime and a seamless experience during events like the Olympics, far beyond what basic monitoring could achieve.

Example 3: Partial outage in a cloud-based platform

Monitoring: Traditional monitoring tools report increased API latency but do not provide information about the underlying cause.

Observability: With observability, engineers drill down into granular traces and metrics, discovering that a particular database query is responsible for the latency under certain conditions. This insight allows them to optimize the query and restore performance

Why observability doesn’t replace monitoring (but complements it)

While observability offers deeper insights into system behavior, it does not replace monitoring  (and it shouldn’t). Instead, the two work best together, each addressing distinct but equally critical needs.

• Monitoring provides real-time awareness. It tracks known metrics (like CPU usage, memory, error rates) and alerts teams when thresholds are breached, enabling immediate response.
• Observability complements monitoring by allowing deeper investigation. It helps teams uncover the root cause of issues, diagnose unknown failures, and understand unexpected behaviors across complex systems.

How do both work together in modern systems

In modern, distributed architectures like microservices and cloud-native platforms, monitoring and observability complement each other to create a complete system health strategy:

• Monitoring acts as the first line of defense. It continuously tracks known conditions — server load, error rates, response times — and triggers alerts when something crosses a predefined threshold.
• Observability kicks in when monitoring surfaces a problem but doesn’t explain why. It provides the tools (metrics, logs, traces) to explore unknown system states, uncover root causes, and diagnose issues that traditional monitoring can’t predict.

Example in practice:

• Monitoring alerts you that service latency spiked.
• Observability tools let you trace that latency spike back to a failing database query in one microservice, triggered by a rare edge-case input.

Real-life use case of a combined strategy in DevOps workflows

Imagine you’re an SRE managing a Kubernetes cluster for a fintech app. Payday hits, traffic spikes, and suddenly users start complaining about slow transactions.

Here’s how monitoring + observability save you:

1. Monitoring catches the first signs: Prometheus notices pod CPU usage spiking over 90% and fires an alert to your Slack.
2. Observability tools tell the full story: You dive into Jaeger traces and Fluentd logs, and trace the slowdown to one microservice stuck in a retry loop with an external payment API.
3. Root cause: It’s not the whole cluster, just one service misbehaving due to a buggy API timeout setting.
4. The fix: You roll back the deployment via GitOps. Traffic stabilizes, CPU usage drops, and users are back to smooth transactions within 20 minutes.

Benefits of using both in tandem

Based on the above use case, here’s how combining monitoring and observability delivers tangible benefits:

• Faster issue detection: Monitoring flagged the problem immediately. In the example, Prometheus detected the CPU spike before users even noticed. Without monitoring, you would have been reacting after the damage was done.
• Better root-cause analysis: Observability tools like Jaeger traces and Fluentd logs helped you quickly zero in on the specific microservice stuck in a retry loop, rather than guessing across the whole cluster.
• Enhanced system reliability: Combining real-time monitoring with observability insights meant you fixed the real issue fast without causing broader disruption. The system stayed stable, even during heavy payday traffic.

Benefits of implementing observability and monitoring together

Using both monitoring and observability together gives IT teams a comprehensive strategy to maintain healthy systems and drive optimal performance. 

The key benefits include:

1. Improved system reliability and uptime.
   Monitoring identifies performance issues, while observability helps teams dig deeper into the root causes. Together, they ensure continuous system optimization, minimizing slowdowns and bottlenecks for better overall system performance.
   
2. Faster incident response and recovery
   Monitoring provides real-time alerts on metrics and incidents, and observability allows for rapid deep dives into logs, traces, and metrics to pinpoint root causes quickly. This combination helps reduce Mean Time to Resolution (MTTR), minimizing downtime and improving recovery speed.
   
3. Enhanced user experience through proactive issue resolution
   Proactively detecting and resolving issues reduces disruptions for users. A more reliable system leads to faster load times, fewer errors, and a smoother overall user experience.
   
4. Optimized resource usage
   Insights from monitoring and observability help optimize resource allocation, plan for future capacity, and scale systems efficiently, ultimately reducing costs and improving system efficiency.

Common tools for observability and monitoring

Here are some commonly used tools for observability and monitoring:

Top 4 monitoring tools

1. UptimeRobot 

UptimeRobot is an uptime monitoring service that regularly checks the status of your websites, APIs, and servers to ensure they are always up and running.

Features

• Provides website and server monitoring services. 
• Does checks from multiple locations worldwide. 
• Notifies via email, SMS, push notifications, and third-party integrations.
• Keep customers informed with public status pages.
• Monitors HTTP, TCP/IP, ports, SSL certificates, and more.

Use case

UptimeRobot is ideal for website monitoring and uptime checks, especially for smaller websites or applications with straightforward monitoring needs.

2. Prometheus

Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It collects and stores time series data, offering powerful querying, visualization, and alerting features for tracking system performance and health.

Features

• No reliance on distributed storage; single server nodes are autonomous.
• Targets discovered via service discovery or static configuration.
• Multiple modes of graphing and dashboarding support.
• Time series collection via a pull model over HTTP. 
• Supports PromQL, a flexible query language.

Use case

Prometheus is perfect for monitoring dynamic, cloud-native environments like Kubernetes and microservices, with strong support for multi-dimensional data collection and querying.

3. Datadog 

Datadog is a SaaS platform that offers unified, real-time observability and security across your entire technology stack. It integrates infrastructure monitoring, application performance, log management, and more.

Features

• Full-stack monitoring and observability.
• Metrics, traces, and logs in a single platform.
• Advanced alerting and anomaly detection.
• Real-time, interactive dashboards.
• Support for open-source observability standards.

Use case

Datadog is best suited for organizations of all sizes-ranging from small startups to large enterprises-that need unified, real-time monitoring and observability across cloud, hybrid, and modern IT environments.

4. Zabbix

Zabbix is an enterprise-class open-source monitoring solution that tracks network parameters and server health. It provides flexible notifications, reporting, and data visualization, making it ideal for fast issue resolution and capacity planning.

Features

• Auto-discovery of servers and network devices.
• Distributed monitoring with centralized web administration.
• Support for both polling and trapping mechanisms.
• Flexible user permissions and secure authentication. 
• Agentless monitoring

Use case

Zabbix is ideal for IT professionals and network administrators who need real-time monitoring, instant alerts, and detailed data visualization. It helps track network health, detect failures, and optimize performance across diverse infrastructures.

Top 4 observability tools 

1. New Relic

New Relic is an AI-powered observability platform that unifies telemetry data (metrics, events, logs, and traces) to provide comprehensive insight into digital estates.

Features

• Real, actionable insights into your stack. 
• Proactive troubleshooting of infrastructure issues.
• Dashboards, alerts, and integrations in a single platform.
• Quick integration with hundreds of tools and open standards.
• Pay only for what you use, with no shelfware or overage penalties.

Use case

New Relic is ideal for software engineering teams that need to monitor, debug, and optimize their entire software stack.

2. Dynatrace

Dynatrace is an advanced platform that delivers analytics and automation for unified observability and security. It breaks down team silos, proactively preventing issues and offering comprehensive solutions to address a wide range of digital challenges.

Features

• Automatic, intelligent infrastructure monitoring and observability across hybrid and cloud environments.
• Best-in-class application performance monitoring (APM).
• Continuous application security posture insights and proactive attack protection.
• Quick detection, investigation, and response to threats through intelligent automation.
• Open and extensible platform that easily integrates with major cloud platforms and solutions.

Use case

Dynatrace is suitable for companies prioritizing digital performance, particularly large enterprises with complex IT environments. It delivers comprehensive monitoring and AIOps capabilities, making it invaluable in situations where real-time insights and quick problem resolution are essential.

3. Grafana

Grafana is an open-source data visualization platform that enables users to create interactive dashboards displaying data from various sources. It allows querying, setting alerts, and analyzing metrics across different environments like servers, Kubernetes, and cloud services.

Features

• Data visualization with histograms, graphs, geomaps, and heatmaps.
• Custom dashboard and alert creation.
• Unified alert management interface.
• Support for multiple data sources (Prometheus, Elasticsearch, InfluxDB, etc.)
• Support for plugins and extensions.

Use case

Grafana is perfect for organizations that require powerful, real-time data visualization and monitoring across multiple platforms. 

4. Elastic APM

Elastic APM is an application performance monitoring system built on the Elastic Stack. It provides real-time monitoring of software services and applications, collecting detailed performance data on response times, database queries, cache calls, external HTTP requests, and more.

Features

• End-to-end distributed tracing
• Developer-friendly, open-standard support.
• Automated, curated visual representation of all dependencies.
• Tail-based sampling of transactions for optimal visibility.
• Automatic anomaly detection using machine learning.

Use case

Elastic APM is best for organizations already using Elastic Stack, providing deep insights into web applications, microservices, and infrastructure monitoring.

Best practices for implementing observability and monitoring

To ensure effective implementation of observability and monitoring, IT teams must follow a set of best practices. These practices help build a comprehensive system for detecting, diagnosing, and resolving issues efficiently, while also enabling proactive improvements.

FREE CHECKLIST: Ready to put these best practices into action? Download the observability checklist to guide your implementation.

1. Define clear objectives for monitoring and observability

Well-defined objectives focus your monitoring and observability efforts. Without clear goals, teams risk collecting irrelevant data, overlooking critical issues, or misaligning with business priorities.

• Set specific, measurable goals: Define targets like
  • Reducing MTTR, 
  • Improving system uptime (e.g., “99.99% uptime“), or 
  • Enhancing user experience (e.g., ensuring page load time < 2 seconds).

• Align KPIs with business goals: Collaborate with stakeholders to map technical metrics to business KPIs. For example, you can align your application response time (technical metric) with customer satisfaction (business KPI).

• Automate alerts and data collection: Ensure automated data collection for key metrics. Set up alerts to track when service level objectives (SLOs) or critical thresholds are breached.

2. Implement metrics, logs, and tracing systematically

A structured approach to monitoring ensures consistent, actionable insights into system health, enabling faster issue detection and resolution.

• Metrics: Use tools like Datadog or Prometheus to track essential KPIs such as latency, error rates, and resource utilization. This helps you maintain a high-level view of system performance.

• Logs: Standardize log formats across services and centralize logs for easier search, correlation, and troubleshooting. Tools like the ELK Stack (Elasticsearch, Logstash, Kibana) provide powerful capabilities for log aggregation, visualization, and analysis.

• Tracing: Implement distributed tracing to track requests as they traverse multiple services. Jaeger, an open-source distributed tracing system, is ideal for visualizing and analyzing interactions in microservices-based architectures.

3. Automate alerting and anomaly detection

Automating alerts and anomaly detection ensures rapid identification of issues, improving system reliability and reducing downtime.

Proactive alerts:
Set automated alerts for critical thresholds to notify teams when performance deviates. Prioritize alerts by severity to focus on high-impact issues and avoid alert fatigue. 

Pro tip: Use Nagios for basic threshold alerts or Prometheus + Alertmanager to create custom, real-time alerts based on system metrics.

Anomaly detection:
Use machine learning to detect deviations from normal behavior, identifying issues early. Tools like New Relic and Dynatrace offer AI-powered anomaly detection, enabling teams to spot hidden problems before they escalate.

4. Regular audits and reviews for continuous improvement

Regular audits help ensure your monitoring setup stays up to date with evolving system architectures and business needs. By reassessing your metrics, alerting rules, and monitoring tools, you can keep everything relevant and effective.

Key actions: 

• Periodically review your metrics, alerting rules, and tools to stay aligned with architectural changes (e.g., migrating from monolithic to microservices).
• Ensure that your KPIs are still the most relevant as your system evolves.
• Regularly check the accuracy and reliability of the data you are collecting.
• Ensure your data is structured properly to provide actionable insights and improve diagnostics.

For example, If you’re scaling up Kubernetes environments, focus on metrics like pod resource usage and service latency, rather than just overall CPU and memory usage.

Pro tip: Combine Prometheus and Grafana

Use Prometheus for collecting time-series data and Grafana for visualizing it. This combination helps spot monitoring gaps and inconsistencies, ensuring your data is accurate and meaningful.

5. Integrating AI/ML for predictive observability

AI and ML help IT teams stay proactive by predicting and resolving issues before they impact users. These technologies enable better system performance, reduced downtime, and foresight into potential disruptions.

Key actions:

• Use machine learning models to detect deviations in system behavior early, preventing issues from escalating.
• Leverage historical data to forecast future system demands, resource usage, or traffic spikes, enabling better planning for peak loads.
• Train machine learning models on telemetry data to predict issues like disk saturation, query latency, or server overloads before they happen.

Pro tip: Combine Prometheus for data collection with TensorFlow for building custom machine learning models that predict potential issues based on historical data.

Industry-specific applications of observability and monitoring

Different industries face distinct challenges. Observability and monitoring tools help them address these by providing real-time insights and proactive issue resolution.

Finance: Security & compliance

In the financial industry, observability and monitoring tools are essential for ensuring system reliability, performance, and, most importantly, security compliance. Financial institutions must adhere to strict regulatory requirements (PCI-DSS, GDPR) while protecting sensitive data.

Use cases:

1. Fraud detection and prevention: Monitoring tools like Prometheus and Datadog track unusual transactions, system anomalies, and sudden spikes in activity. AI and ML-driven platforms, such as Dynatrace, can analyze user behavior patterns and identify potential fraudulent activities in real-time, reducing the risk of security breaches.
   
2. Compliance audits: Financial institutions need to maintain detailed logs of all activities. Observability tools like Elastic APM help track every action in real-time and store logs for audits, making it easier to comply with security regulations.
   
3. Real-time monitoring: With tools like New Relic, financial institutions can ensure the health of transaction systems, databases, and other critical infrastructure to prevent downtime and performance degradation that might violate service-level agreements (SLAs).

Healthcare: System reliability for patient care

In healthcare, system reliability is crucial for ensuring patient care continuity. Real-time monitoring and observability tools help ensure that hospital systems and patient management applications run smoothly without delays or errors that could affect care delivery.

Use cases:

1. Patient data access: Monitoring platforms like Prometheus help ensure that Electronic Health Records (EHR) systems are always accessible and performing well. Alerts are triggered immediately if there’s a spike in access time or a failure in retrieving patient data.
   
2. Critical infrastructure monitoring: Systems like Grafana integrated with Prometheus and Datadog can be used to monitor patient-facing applications, hospital networks, and medical devices to ensure they operate without interruption.
   
3. Incident management: Observability tools such as New Relic and Elastic APM can be used to trace system failures, pinpoint bottlenecks, and quickly identify the root cause of issues like delays in patient appointment scheduling or equipment malfunction.

E-Commerce: Handling traffic spikes & cart monitoring

E-commerce platforms need solid observability to ensure smooth user experiences, especially during traffic spikes (like Black Friday or other holiday sales) and ensure that users can complete purchases without encountering problems.

Use cases:

• Handling traffic spikes: Tools like Datadog and Prometheus help track server performance and monitor application metrics during traffic surges. These tools can automatically scale resources to handle increased traffic, reducing the risk of downtime or slow page loads.
  
• Cart monitoring and abandonment: With Grafana and Elastic APM, e-commerce platforms can monitor shopping cart actions in real-time to ensure users can easily add items to their carts, proceed to checkout, and complete purchases. Tools can trigger alerts to investigate potential issues if cart abandonment rates rise unexpectedly.
  
• Transaction performance: New Relic helps e-commerce businesses monitor checkout processes and transaction times. By observing transaction latency, businesses can optimize backend services and ensure that users don’t abandon their carts due to slow load times or errors.
  
• Personalized user experience: Leveraging machine learning tools for anomaly detection and predictive analytics, e-commerce platforms can also personalize user experiences based on traffic trends and predict demand for certain products, optimizing the supply chain.

Future trends in observability and monitoring

Starting in 2025, these three trends will define the future of monitoring and observability.

Rise of AI-driven observability

AI is enhancing observability by automating the detection of performance issues, diagnosing root causes, and offering predictive insights. Machine learning models learn from system behavior, spotting anomalies before they lead to disruptions. This proactive approach helps reduce incidents, minimize downtime, and speed up issue resolution.

In short, AI-driven observability helps:

• Proactively detect issues before they escalate. 
• Automate anomaly detection and root-cause analysis. 
• Improve system reliability and reduce manual effort

Major vendors are already integrating AI into their platforms:

• ManageEngine introduced OpenAI-powered observability to Site24x7 in July 2023.
• Splunk added new AI features to its unified security and observability platform last year.
• New Relic launched Grok, a generative AI observability assistant, earlier this year.
• Riverbed rolled out an AI-powered observability platform aimed at addressing blind spots in complex IT environments.

Use of OpenTelemetry and other open-source frameworks

OpenTelemetry is gaining traction as a unified, open-source framework for observability, enabling organizations to collect, process, and export telemetry data across multi-cloud environments. It integrates with major tools like Prometheus, Datadog, and AWS CloudWatch, allowing teams to monitor and debug applications more efficiently across diverse cloud environments.

Netflix is a prime example of how OpenTelemetry is used at scale. 

The company leverages OpenTelemetry to monitor logs, metrics, and traces across its services. By incorporating security into its observability framework, Netflix ensures it can not only track performance but also detect and respond to security incidents in real-time, providing a more resilient infrastructure.

Predictive analytics in monitoring systems

​​“There’s a growing demand for observability systems that can predict service outages, capacity issues, and performance degradation before they occur,” says Sam Suthar, founding director of Middleware.

While we can’t see the future, predictive analytics in monitoring systems can provide educated forecasts based on historical data, enabling proactive problem-solving. According to Grafana’s annual survey, 13% of respondents are already using profiling tools in production, and this number is expected to rise as predictive analytics matures.

How it helps:

• Proactive issue prevention: Predictive analytics identifies risks early, allowing teams to act on forecasts and prevent potential disruptions.
• Service continuity: Instead of reacting to service outages, teams can proactively address issues, ensuring minimal impact on end users and continuous performance.

Conclusion

Monitoring and observability serve distinct but complementary roles in modern system management. Monitoring acts as your system’s early warning system, providing real-time alerts when something goes wrong and helping you identify what and when an issue occurs. Observability, on the other hand, digs deeper, offering tools to uncover the “how” and “why” behind an issue, enabling thorough root cause analysis and proactive issue resolution.

Both are essential. Monitoring ensures you’re aware of problems as soon as they arise, while observability allows you to understand your system’s behavior on a deeper level, enabling smarter decisions and faster problem resolution.

When working with complex, distributed environments, combining both monitoring and observability helps maintain system health, improve reliability, and deliver seamless user experiences. Together, they offer an extensive approach to issue detection and resolution, enabling IT teams to manage modern systems more effectively.

If you haven’t implemented observability, you’re missing out on crucial insights into your system’s behavior and performance. Now is the time to evaluate your current approach. Adding observability to complement your monitoring efforts can lead to faster diagnostics, better system performance, and a more reliable infrastructure overall.