Summary: Observability is a hot topic for organizations managing complex IT environments, but there’s a lot of confusion about what it really means, why it’s necessary, and what it can actually deliver. This article breaks down what observability is, what it promises, and how it can help organizations achieve their goals.
Did you know? Even to date, developers spend approximately 50% of their time debugging! This staggering statistic raises a critical question: “why do developers continue to waste so much time on debugging?”
The answer lies in the outdated debugging systems that dominate the industry. These legacy tools fail to provide developers with a clear understanding of the complex interactions between microservices, distributed systems, and other components in the IT ecosystem.
Without the ability to aggregate, correlate, and analyze performance data from applications, hardware, and networks, developers are left to navigate a sea of complexity, making maintenance and troubleshooting a daunting task.
This is where the control theory spin-off, a.k.a observability, comes in.
So, let’s deep dive into what observability is, how it works, and its various benefits to businesses.
What is Observability?
Observability is the ability to understand what’s happening inside a system based on the data it produces, like logs, metrics, and traces.
Instead of guessing where problems originate, observability gives DevOps teams a clear, data-backed picture of system behavior in real time.
In modern software environments, especially those built on microservices and distributed architectures, this visibility is no longer a luxury. It’s a necessity.
“The proliferation of microservices and distributed systems has made it more difficult to understand real-time system behavior, which is critical for troubleshooting problems. Recently, more businesses have solved this problem with automations to monitor distributed architecture, deep dive tracking and real-time observability,”
Laduram Vishnoi, Founder and CEO of Middleware.
Observability combines practices and tools that help you:
- Monitor system performance end-to-end
- Debug applications and infrastructure efficiently
- Detect anomalies before they cause downtime
- Align reliability with CI/CD pipeline processes
Ultimately, observability transforms raw telemetry into actionable insight, allowing teams to build resilient, scalable, and high-performing systems that support business goals.
A Brief History of Observability
Though observability feels like the latest concept, its origins go back centuries.
17th Century: Observability in Mechanical Systems
The idea of observability first emerged in the 1600s when scientists like Christiaan Huygens applied early control theory to mechanical systems such as windmills. The scientists used static external outputs, such as blade speed, and could assess their internal performance for the first time. Scholars had accepted that complex systems could be diagnosed formally, inferring the internal performance level in a complex dynamic system, without ever having those internal components available.
1960s–1980s: Observability Meets Computing
In the 1960s, control theory found its place in computer science. Although today’s standards limited computers at the time, their increasing complexity made it essential to monitor internal behavior through external signals. This period marked the beginning of system-level observability and telemetry in computing.
1990s: Internet Growth and Siloed Monitoring
As the internet’s role in the economy grew in the late 1990s, we started to see increased infrastructure complexity as well. Understanding and controlling that complexity means deploying a variety of “monitoring” tools for specific attributes- servers, network traffic, and very basic application performance & uptime.
2000s: The Rise of Application Performance Monitoring (APM)
The early 2000s saw the emergence of commercial APM tools such as AppDynamics and Dynatrace, which enabled engineering teams to:
- Monitor transactions across services
- Identify bottlenecks
- Visualize application status in real time
The shift from monitoring system performance to monitoring the application’s performance was monumental.
2010s: Cloud-Native Systems and Unified Observability
The cloud revolution brought even more distributed complexity. During this era:
- Cloud providers like AWS, Microsoft Azure, and IBM launched native observability tools
- Unified platforms like Datadog began monitoring metrics, logs, and traces together
- Open-source solutions like Prometheus democratized observability for engineering teams of all sizes
“Each decade has brought a sea change in how observability is expected to function. The last three decades have seen transformation after transformation — from on-premise to cloud to cloud-native. With each generation has come new problems to solve, opening the door for new companies to form.”
Laduram Vishnoi, Founder and CEO of Middleware.
The Best Observability Examples
Observability has grown from a specialized engineering task to a key part of modern software operations. Here are seven global companies showing top observability strategies, each designed for their size, systems, and customer experience goals.
1. Netflix: Chaos Engineering at Scale
Netflix was an early user of observability to keep its microservices and distributed systems reliable. Their internal platform, Chaos Monkey, is built to inject system failure so that Netflix can test its services in the worst conditions.
Observability tools used:
- Prometheus for metrics collection
- Grafana for real-time data visualization
- Custom tooling for failure injection and telemetry
Key takeaway: Netflix uses observability not just for monitoring, but for proactively testing system failure tolerance.
2. Meta (formerly Facebook): High-Scale Distributed Tracing
Meta powers some of the largest and most complex applications on the planet. Their observability framework is a combination of:
- Prometheus for time-series metrics,
- Zipkin for distributed tracing, and
- Proprietary custom in-house tooling for real-time diagnostics across services.
Key takeaway: The most important takeaway is that Meta’s observability strategy combines open-source and in-house tools such that they can achieve high performance across globally distributed systems.
3. Uber: Real-Time System-Level Insights
Because of Uber’s high volume of real-time transactions and high GPS volume, system health visibility is critical.
The observability architecture consists of:
• Datadog for service-level monitoring
• Jaeger for request tracing across microservices
• Prometheus for infrastructure-level metrics
Key takeaway: Uber relies on observability to support real-time decisions where data reliability, uptime, and scalability are critical.
4. Airbnb: Optimizing the Guest Experience
Airbnb’s platform has to be reliable and responsive for thousands of hosts and millions of guests. Their engineering teams use:
- Honeycomb for event-based observability
- Datadog for central log aggregation
- Custom dashboards to monitor behaviours and uptime of services
Key takeaway: Observability allows Airbnb to improve user experience and solve system issues before they impact bookings.
5. Spotify: Streaming Performance at Global Scale
Spotify has strong observability capabilities to support millions of concurrent global listeners and sustain a continuous stream. Their stack includes:
- Prometheus and Grafana for infrastructure monitoring
- Some internal tools to measure audio delivery performance and mobile latency
Key takeaway: Spotify’s observability abilities allow it to provide uninterrupted music streaming in real-time.
6. Slack: Reducing Downtime in Real-Time Communication
Downtime is unacceptable for a collaboration tool like Slack. That’s why their observability stack consists of:
- Datadog for infrastructure and application monitoring
- Custom alerting systems that help catch anomalies that may affect users.
Key takeaway: Slack uses observability to quickly identify and remediate issues to have reliable communication in the workplace.
How Does Observability Work?
Observability operates on three pillars: logs, metrics, and traces. By collecting and analyzing these elements, you can bridge the gap between understanding ‘what’ is happening within your cloud infrastructure or applications and ‘why’ it’s happening.
With this insight, engineers can quickly spot and resolve problems in real-time. While methods may differ across platforms, these telemetry data points remain constant.
Logs
Logs are records of each individual event that happens within an application during a particular period, with a timestamp to indicate when the event occurred. They help reveal unusual behaviors of components in a microservices architecture.
- Plain text: Common and unstructured.
- Structured: Formatted in JSON.
- Binary: Used for replication, recovery, and system journaling.
Cloud-native components emit these log types, leading to potential noise. Observability transforms this data into actionable information.
Start collecting and monitoring logs from any environment in 60 seconds. Get started!
Metrics
Metrics are numerical values describing service or component behavior over time. They include timestamps, names, and values, providing easy query ability and storage optimization.
Metrics offer a comprehensive overview of system health and performance across your infrastructure.
However, metrics have limitations. Though they indicate breaches, they do not shed light on underlying causes.
Traces
Traces follow a request’s journey through a distributed system, from start to finish. Each trace logs the interactions between services and components, making it easier to:
- Identify latency issues
- Pinpoint bottlenecks
- Understand service dependencies
They help analyze request flows and operations encoded with microservices data, identify services causing issues, ensure quick resolutions, and suggest areas for improvement.
Unified observability
Successful observability stems from integrating logs, metrics, and traces into a holistic solution. Rather than employing separate tools, unifying these pillars helps developers gain a better understanding of issues and their root causes.
As per recent studies, companies with unified telemetry data can expect a faster Mean time to detect (MTTD) and Mean time to respond (MTTR) and fewer high-business-impact outages than those with siloed data.
How is Observability Different from Monitoring?
Cloud monitoring solutions use dashboards to display performance indicators, enabling IT teams to identify and resolve issues. While monitoring shows what is happening, it often lacks insight into why it’s happening.
Traditional monitoring tools struggle to keep pace with complex cloud-native applications and containerized environments, which are increasingly prone to performance bottlenecks and security risks.
Observability, on the other hand, goes deeper by analyzing telemetry data logs, traces, and metrics across your infrastructure. It delivers actionable insights into system health at the earliest signs of trouble, helping DevOps teams address potential problems before they escalate.
With observability, teams can track system speed, connectivity, downtime, and bottlenecks in real-time, drastically reducing response times and ensuring optimal performance.
In fact, recent reports show that nearly 64% of organizations using observability tools have improved Mean Time to Resolve (MTTR) by 25% or more.
Read more about Observability vs. Monitoring.
Why is Observability Important For Business?
In the last decade, the emergence of cloud computing and microservices has made applications more complex, distributed, and dynamic. In fact, over 90% of large enterprises have adopted a multi-cloud infrastructure.
While these scalable systems bring immense benefits, they also introduce new challenges in monitoring and management, including:
- Outdated tools: Traditional monitoring solutions simply can’t keep up with modern, distributed architectures.
- Limited visibility: Legacy systems create silos, making it difficult to manage processes or automate effectively.
- Vendor lock-in: Switching from one proprietary tool to another used to be tedious, but observability platforms with vendor-agnostic data formats make data portability much easier.
- High costs: In some cases, legacy monitoring platforms cost more than the cloud infrastructure itself.
It’s no surprise that DevOps and SRE teams are increasingly adopting observability to understand system behavior better, troubleshoot issues faster, and optimize performance. In fact, the growing reliance on observability platforms is expected to push the market to USD 4.1 billion by 2028.
The Benefits of Observability
Observability is no longer just a technical advantage; it’s a business necessity. Let’s explore the key benefits it brings to modern enterprises.
1. Improved System Uptime and Reliability
Observability tools offer developers real-time insights into system health and behavior, empowering them to pinpoint and resolve issues before they can cause an outage. This subsequently leads to higher uptime and makes the overall system robust.
By 2025, 50% of surveyed respondents reported increased usage of OpenTelemetry and Prometheus for comprehensive monitoring and troubleshooting. Unified observability platforms ensure uninterrupted system performance while proactively addressing potential faults before they escalate, resulting in improved mean time between failures (MTBF) and mean time to recovery (MTTR).
See how Middleware helped Generation Esports slash observability costs & improve MTTR by 75%!
2. Increases Operational Efficiency
With real-time insights into system performance and behavior, better operational efficiency is an absolute given. If done right, developers can automate repetitive tasks, optimize resource consumption and operations, and streamline incident management processes. This leads to reduced operational overhead, improved resource utilization, and enhanced collaboration between DevOps teams.
3. Improves Security Vulnerability Management
Beyond DevOps, observability tools are extremely beneficial for security and DevSecOps teams as they allow them to track and analyze security breaches or vulnerabilities in real time and resolve them. This ensures a secure application environment, reduces the risk of data breaches, and enables proactive compliance with regulatory requirements.
Observability tools also facilitate the identification of potential security threats, enabling teams to take corrective action before attacks occur.
4. Enhanced Fault Detection and Prevention
Through preventive observability, AI-based platforms can predict potential issues in real time. For instance, a 400-millisecond span in system traces, combined with profiling data, can reveal the precise code causing the bottleneck and its resource consumption, enabling “surgical” optimization of system performance.
This approach has been shown to significantly reduce operational bottlenecks in critical real-time systems, improve fault detection accuracy, and minimize false positives.
5. Unified Insights Across Data Sources
Consolidating logs, metrics, and traces through platforms reduces the MTTR by up to 50%, significantly boosting operational speed and team efficiency. Approximately 13% of organizations are already using profiling tools in production, with growing demand for unified, streamlined observability systems.
This unified approach enables teams to gain a comprehensive understanding of system behavior, identify causal relationships between different data sources, and make data-driven decisions.
6. Operational Cost Optimization
Continuous monitoring using observability tools facilitates targeted cost optimization in cloud operations. For cloud-heavy organizations, reducing metric cardinality through tools such as Middleware can lead to as much as a 10X reduction in monitoring costs.
Observability also enables teams to monitor infrastructure economics effectively, implementing FinOps strategies across hybrid and multi-cloud environments, and optimizing resource utilization to reduce waste and minimize costs.
7. Performance Optimization for AI Systems
Observability tools track AI resource usage and performance intricately, ensuring efficiency. Around 18% of surveyed users consider AI/ML capabilities crucial for observability solutions. These capabilities support AI-driven models by accelerating root cause analysis and providing deeper operational insights. This ensures that AI performance is consistently aligned with business goals while reducing costs, improving model accuracy, and enhancing overall AI efficiency.
To address the growing demand for AI observability, Middleware introduced LLM Observability, a solution that provides real-time monitoring, troubleshooting, and optimization for LLM-powered applications. This enables organizations to proactively address performance issues, detect biases, and improve decision-making.
With comprehensive tracing, customizable metrics, and pre-built dashboards, LLM Observability offers detailed insights into LLM performance. Additionally, its integration with popular LLM providers and frameworks streamlines monitoring and troubleshooting, ensuring optimal performance and responsiveness for AI systems.
Want to see how LLM Observability can transform your AI performance? Learn more about LLM Observability.
8. Proactive Compliance and Security Measures
Observability platforms ensure adherence to regulations like the EU’s Digital Operational Resilience Act (DORA) and the U.S. Federal Reserve Regulation HH. Observability-integrated systems automate compliance tracking, reducing audit workloads by replacing periodic checks with continuous monitoring, while maintaining a proactive defense against security threats.
This enables organizations to demonstrate compliance, reduce regulatory risks, and improve their overall security posture.
9. Enhanced ROI on IT Investments
Organizations opting for unified observability platforms experience optimized resource utilization and increased return on investment. Consolidation strategies were seen in 2024 with large acquisitions – for example, Cisco acquiring Splunk for $28 billion – pointing to a trend of fewer, more capable platforms to meet demands effectively. This enables organizations to maximize their IT investments, reduce costs, and improve overall business outcomes.
10. Scalability in Cloud-Native Environments
Cloud-native infrastructure monitoring is simplified through tools like eBPF and OpenTelemetry. Nearly 25% of surveyed engineers identified the operational role of platform engineering within scalability as vital, with eBPF becoming foundational to modern observability frameworks. This enables organizations to efficiently monitor and manage cloud-native applications, ensure scalable infrastructure, and support business growth.
11. Improved Sustainability in IT
Observability tools monitor energy efficiencies better, particularly for cloud and AI-heavy operations. Leveraging “green coding” approaches, organizations optimize energy usage significantly, saving on operational costs while meeting global sustainability directives like the EU’s Green Deal.
12. Reduced Data Overload
Streamlining telemetry data in observability pipelines allows a focused analysis of only the most relevant metrics. Observability enables up to 30–50% reductions in metric cardinality, eliminating unnecessary complexity in system monitoring while maintaining precision. This leads to improved signal-to-noise ratios, reduced data noise, and enhanced analytics capabilities.
13. Security Resilience
Observability in cybersecurity enhances the detection of sophisticated threats while streamlining threat mitigation workflows. A human-in-the-loop approach ensures accountability, with observability tools recommending actionable measures in alignment with compliance and ethical standards.
This approach supports robust organizational defense mechanisms, reduces the risk of data breaches, and enables proactive compliance with regulatory requirements.
14. Enhances Real-User Experience
Observability tools play a vital role in enhancing customer experiences by ensuring seamless interactions with web and mobile applications. With capabilities like real user monitoring (RUM), developers can gain comprehensive user journey visibility, identifying and troubleshooting issues concerning front-end performance and user actions. This enables them to correlate issues, make data-driven decisions, and optimize user experiences, resulting in improved user engagement, retention, and overall business success.
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Proactive observability takes this a step further by enabling organizations to anticipate and prepare for high-traffic events, such as holiday surges. By scaling up resources and optimizing load management, organizations can reduce the risk of downtimes, ensuring smoother end-user interactions, higher customer satisfaction rates, and improved brand loyalty.
For instance, Hotplate, a popular food delivery platform, leveraged Middleware’s observability solution to ensure seamless customer experiences during peak hours. By gaining real-time visibility into their application’s performance, Hotplate’s team was able to identify and resolve issues quickly, resulting in a 90% reduction in errors and a significant improvement in customer satisfaction. With Middleware’s observability solution, Hotplate was able to deliver fast, reliable, and secure food delivery experiences to its customers, even during the busiest times.
15. Improves Developer Productivity and Satisfaction
“Developers spend nearly 50% of their time and effort on debugging. Observability tools have the potential to bring that down to 10%, allowing developers to focus on more critical areas.”
Laduram Vishnoi, Founder and CEO of Middleware.
Wondering how Middleware helps developers move over debugging? Check out our exclusive feature on YourStory.
Observability tools don’t just offer comprehensive visibility into distributed applications; they render actionable insights that developers can actually use to identify and fix bugs, optimize code, and enhance overall productivity. By providing developers with the right insights at the right time, observability tools enable them to work more efficiently, reduce manual effort, and focus on higher-value tasks that drive business innovation and growth.
“I don’t want my developers to stay up all night to try to fix issues. That’s a waste of everyone’s time. Middleware helped us become faster. It saves at least one hour of my time every day, which I can dedicate to something else.”
Akshat Gupta, Trademarkia.
Open standards such as OpenTelemetry reduce overhead and increase programmer focus, with the adoption rate of tools like these growing significantly. Standardized instrumentation eliminates the need for custom solutions, allowing more than 50% of users to benefit from faster development cycles and enhanced system monitoring capabilities. This leads to improved code quality, reduced technical debt, and increased developer satisfaction.
What is the Real Advantage of Using Observability?
These were just the tip of the iceberg. Companies using full-stack observability have seen several other advantages:
- Nearly 35.7 % experienced MTTR and MTTD improvements.
- Almost half the companies using full-stack observability were able to lower their downtime costs to less than $250k per hour.
- More than 50% of companies were able to address outages in 30 minutes or less.
- Additionally, companies with full-stack observability or mature observability practices have gained high ROIs. In fact, 71% of organizations see observability as a key enabler to achieving core business objectives.
- As of 2025, the median annual ROI for observability stands at 100%, with an average return of $500,000.
How can Observability Benefit DevOps and Engineers?
Observability is so much more than data collection. Access to logs, metrics, and traces marks just the beginning. True observability comes alive when telemetry data improves end-user experience and business outcomes.
Open-source solutions like OpenTelemetry set standards for cloud-native application observability, providing a holistic understanding of application health across diverse environments.
Real-user monitoring offers real-time insight into user experiences by detailing request journeys, including interactions with various services. This monitoring, whether synthetic or recorded sessions, helps keep an eye on APIs, third-party services, browser errors, user demographics, and application performance.
With the ability to visualize system health and request journeys, IT, DevSecOps, and SRE teams can quickly troubleshoot potential issues and recover from failures.
Can AI make Observability better?
Artificial intelligence is neither a silver bullet nor snake oil! Though AI has the power to take things up a notch, its promise is often overshadowed by exaggerated claims and misconceptions. However, when applied to observability, AI can truly transform the way organizations approach monitoring, troubleshooting, and optimizing their systems.
One pressing pain point for many organizations is the time-consuming process of debugging issues. Sifting through vast amounts of data to identify root causes can be costly and detrimental to business operations. This is where AI-enhanced observability comes in – automating and streamlining the debugging process, saving developers nearly half of their time.
By blending AIOps and Observability, organizations can optimize real user monitoring and automate the analysis of vast data streams. This allows teams to maximize their overall efficiency and automate critical tasks like anomaly detection, log grouping, and root cause analysis. AI-driven anomaly detection capabilities enable the identification of unknown unknowns, detecting unusual patterns of behavior not seen before, and allowing for timely investigation and remediation.
The integration of Generative AI into observability simplifies the complexities of accessing critical insights. AI models can pinpoint specific telemetry data that may indicate issues, enabling proactive remediation. This level of automation and foresight is redefining the future of observability and reliability.
“Want to know where your organization stands on its observability journey? Explore our Observability Maturity Model to assess your current capabilities, identify gaps, and map a clear path toward full-stack observability.”
Top 10 Observability Best Practices
There is no doubt that observability offers immense value. However, it’s important to understand that most available tools lack business context.
On top of that, several organizations look at technology and business as two separate disciplines, hindering their overall ability to maximize their use of observability. The situation highlights the need for a defined set of best practices.
- Unified telemetry data: Consolidate logs, metrics, and traces into centralized hubs for a comprehensive overview of system performance.
- Metrics relevance: Identify and monitor important metrics that are aligned with organizational goals.
- Alert configuration: Set benchmarks for those metrics and automate alerts to ensure quick issue identification and resolution.
- AI and machine learning: Leverage machine learning algorithms to detect anomalies and predict potential problems.
- Cross-functional collaboration: Foster collaboration among development, operational, and other business units to ensure transparency and overall performance.
- Continuous enhancement: Regularly assess and improve observability strategies to align with evolving business needs and emerging technologies.
Read more about observability best practices.
Finding the Right Observability Tool
Selecting the right observability platform can be a tad bit difficult. You must consider capabilities, data volume, transparency, corporate goals, and cost.
Here are some points worth considering:
User-Friendly Interface
Dashboards present system health and errors, aiding comprehension at various system levels. A user-friendly solution is crucial for engaging stakeholders and integrating smoothly into existing workflows.
Real-Time Data
Accessing real-time data is vital for effective decision-making, as outdated data complicates actions. Utilizing current event-handling methods and APIs ensures accurate insights.
Open Source Compatibility
Prioritize observability tools using open-source agents like OpenTelemetry. These agents reduce resource consumption, enhance security, and simplify configuration compared to in-house solutions.
Easy Deployment
Choose an observability platform that can be quickly deployed without stopping daily activities.
Integration-Ready Across Tech Stacks
The tools must be compatible with your technology stack, including frameworks, languages, containers, and messaging systems.
Clear Business Value
Benchmark observability tools against key performance indicators (KPIs) such as deployment time, system stability, and customer satisfaction.
AI-Powered Capabilities
AI-driven observability helps reduce routine tasks, allowing engineers to focus on analysis and prediction.
Middleware Observability Platform
Middleware is a full-stack cloud observability platform that empowers developers and organizations to monitor, optimize, and streamline their applications and infrastructure in real-time. By consolidating metrics, logs, traces, and events into a single platform, users can effortlessly resolve issues, enhance operational efficiency, minimize downtime, and reduce observability costs.
Middleware provides comprehensive observability capabilities, including infrastructure, log, application performance, database, synthetic, serverless, container, and real user monitoring.
With its scalable architecture and extensive integrations, it helps organizations optimize their technology stack and improve efficiency. Many businesses have seen significant benefits, including a 10x reduction in observability costs and a nearly 75% improvement in operational efficiency.
“Middleware has proven to be a more cost-effective and user-friendly alternative to New Relic, enabling us to capture comprehensive telemetry across our platform. This improved our operational efficiency, service delivery, and accelerated incident root cause analysis.”
John D’Emic, Co-Founder and CTO at Revenium.
Observability Challenges in 2025
Here’s an interesting question: if observability provides so many advantages, then what’s stopping organizations from going all in?
Cost: In 2023, nearly 80% of companies experienced pricing or billing issues with an observability vendor.
Data overload: The sheer volume, speed, and diversity of data and alerts can lead to valuable information surrounded by noise. This fosters alert fatigue and can increase costs.
“Excessive data collection has led to inflated costs without real value. Customizable observability platforms now enable companies to flag and filter unnecessary data, reducing expenses without sacrificing insights. By implementing comprehensive data processing with compression and indexing, companies can significantly reduce data size, leading to cost savings. This approach is expected to save companies 60-80% on observability costs, shifting from exhaustive data collection to efficient, targeted monitoring.”
Sam Suthar, Founding Director at Middleware.
Team segregation: Teams in infrastructure, development, operations, and business often work in silos. This can lead to communication gaps and prevent the flow of information within the organization.
Causation clarity: Pinpointing actions, features, applications, and experiences that drive business impact is hard. Companies need to connect correlations to causations regardless of how great the observability platform is.
The Future of Observability
As 2025 unfolds, the future of observability holds exciting possibilities.
In the days to come, the industry will see a major shift, moving away from legacy monitoring to practices that are built for digital environments. Full-stack observability tops this list, with nearly 82% of companies gearing up to adopt 17 capabilities through 2026.
“Observability in 2025 is evolving fast, don’t let your strategy fall behind. Explore our complete Observability 2.0 Guide”
The idea of tapping natural language and Large Language Models (LLMs) to build more user-friendly interfaces is also gaining steam. Furthermore, industry players are upping the ante by tapping into AI to offer unified systems of records, end-to-end visibility, and high scalability. They promise to democratize observability, deliver real-time insights into operations, reduce downtime, improve user experiences, and ensure customer satisfaction.
On the other hand, the proliferation of AI-generated code will drive the need for disaggregated observability approaches, allowing organizations to manage the complexities of AI-driven systems.
Additionally, the commoditization of observability stacks will give customers more choice and control over their data, with companies demanding more control over their data and prioritizing cost-efficiency and customizable observability solutions. Middleware is leading this change with its AI-powered observability solutions that can unify telemetry data into a single location and deliver actionable insights in real time – while giving you complete control over your data!
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FAQs
What is observability?
Observability entails gauging a system’s current condition through the data it generates, including logs, metrics, and traces. Observability involves deducing internal states by examining output over a defined period. This is achieved by leveraging telemetry data from instrumented endpoints and services within distributed systems.
What is Cloud Observability?
Cloud observability is the ability to monitor, analyze, and understand cloud systems in real time using logs, metrics, and traces to detect, diagnose, and resolve issues quickly.
Why is observability important in modern systems?
Observability helps teams detect, diagnose, and resolve system issues faster by providing real-time insights into performance and behavior.
What are the three pillars of observability?
The three key pillars are logs, metrics, and traces, collectively known as the telemetry data.
How do I implement observability?
Your systems and applications require proper tools to gather the necessary telemetry data to achieve observability. By developing your own tools, you can utilize open-source software or a commercial observability solution to create an observable system. Typically, implementing observability involves four key components: logs, traces, metrics, and events.
What tools are used for observability?
Popular observability tools include Middleware, Datadog, Splunk, Dynatrace, Prometheus, and OpenTelemetry.
