4 benefits of observability

As development methods evolve, monitoring needs to evolve, too. That’s where observability comes in. The evolution of technology to support hyper-distributed applications based on Kubernetes and microservices has created the need for modern, unified observability platforms. Full-stack observability offers an update to traditional monitoring with disparate legacy point tools. It lets you proactively gather valuable insights from your data in today’s complex cloud environments.

An all-in-one observability solution builds on classic monitoring tools to allow visibility in a single pane of glass. Ease of use is one of the major benefits of observability, alongside its ability to help you action your data to respond to alerts, do effective root cause analysis, and assess the overall health of your system. Many operations teams are finding that the exponential increase in applications has led to an exponential increase in tools. But do they live up to the hype? 

Often, these new solutions generate new challenges and require constant updates, and the mountain of data continues to grow. With the integration of artificial intelligence (AI) co-pilots and machine learning (ML), many modern observability tools can deliver on the promise of artificial intelligence for IT operations (AIOps) and generative AI (GenAI) without resorting to piecemeal solutions.

AI-powered observability is the cutting edge of a modern observability solution, giving you all benefits of observability and AI. In a landscape of increased architectural complexity, a unified data platform with search and AI capabilities unblinds your blindspots without the hassle. 

If you’re still teetering on whether or not AI search-bolstered observability is right for your organization, here are the benefits of a modern observability solution.

The volume of data created in an ever-sprawling, distributed environment calls for a serious update in your monitoring practices. Keeping your heterogeneous telemetry data siloed in separate, incompatible observability backends without a common schema is not only inefficient, but it can also be detrimental to your business. 

A unified data platform (on which an observability solution is built) can consolidate all types of data from various sources, simplifying data management, and enabling high-speed analytics. This, along with Search AI capabilities, empowers IT teams to troubleshoot in real time and perform proactive ad hoc analytics. With better instrumentation, ingestion of cloud services data, and adherence to open standards and semantic conventions, you can achieve consistent data structures, thereby improving your mean time to repair (MTTR). A modern observability solution also relieves the burden of telemetry data volume and velocity by leveraging AI/ML with enhanced search capabilities — so you can focus on innovation and providing customers the experience they expect.  

By reducing the number of tools your team uses and retiring unused tools, your organization can do more with less. Bottom line: tool consolidation helps productivity, and increased productivity translates into savings for your business along with better customer experiences.

Kubernetes and microservices are powerful and extremely flexible — but they’re also complex. Containerized applications are spun up, scaled down, and moved frequently, making identifying and solving issues when they occur very challenging. 

When diagnosing issues in this context, you need as much information as possible. By ingesting data from all your hyperscalers, modern observability tools give you unprecedented visibility: container lifecycles, interservice communications, and log events at various layers of the stack. This comprehensive data collection allows IT teams to quickly identify and resolve issues, minimizing downtime and ensuring that applications run smoothly. Armed with AI and powerful search capabilities, an observability tool correlates data from cluster to kernel-level points, so you win back some predictability in operations, development time, scalability, and spending.

Your applications likely rely on third-party services and external dependencies, introducing additional complexity. These external services often have heterogeneous and incompatible telemetry data formats that need to be ingested into a single, centralized data store to obtain a cohesive view of application performance.

Without a unified approach, tracking down performance issues related to external services can be like finding a needle in a haystack. This is where an observability solution built on open standards and a unified data platform comes in. Telemetry data from various vendors, sources, and providers can be collected, normalized, and analyzed in a single place. As a result, organizations gain visibility into the performance of all their internal and external components ensuring that they can manage and optimize their entire stack effectively. Your data is democratized, and with your AI search capabilities here, you get answers to your questions — fast.

The cloud enables flexibility and demands operational dexterity. Continuous integration/continuous delivery (CI/CD) practices deliver the agility required for software development in the age of cloud computing. However, the frequent changes and updates often lead to outages or performance issues. This rapid pace of change underscores the need for robust, full-stack observability.

DevOps teams need real-time visibility into their entire environment to detect and resolve issues quickly. After all, the ability to deploy changes rapidly and reliably can make or break an organization’s ability to innovate. Shifting observability practices left — integrating them into the development process, rather than patching issues at the production stage — means organizations can take a proactive approach to maintaining high velocity without compromising on stability and performance.One example of shifting observability practices left is the integration of OpenTelemetry instrumentation into the development and coding process to produce vendor-neutral metrics, logs, and traces based on open standards. Starting with a consistent and unified data platform is the foundation for the analytics and AI capabilities in modern observability.    

With Search AI powered observability, you can go one step further and address issues proactively while democratizing your data across teams (say bye-bye to silos), increasing productivity, and staying ahead of the curve. Since AI, though still in its infancy, is engrained in most cloud-native environments, your observability solution should be able to keep up. Instead of keeping up with the pace of change, you’ll be leading the pack.

Everything in your digital ecosystem generates data — the cloud, serverless, microservices, external applications, containers, runtimes, events, logs, traces, and metrics. This immense volume of data makes it a struggle for IT teams to find the root cause of outages. This issue trickles down into every aspect of your organization — therefore, many business problems are ultimately data problems.

Consolidating monitoring tools is one part of the solution. Consolidating your data onto a single, unified platform is the other. This is where modern observability — the ability to extract full potential from your data — begins. The most advanced tools use Search AI to help you find and analyze data even more efficiently. Even to find unknown unknowns — issues that you did not know even existed.

When considering a modern observability solution for your organization, understand that change is inevitable. This is especially true for technology — it’s constantly evolving. You don’t want to be in a position one or two years down the line having invested a significant amount of resources adopting one vendor’s solution only to realize that there are incompatibility issues with a new system in your stack. Proprietary vendor offerings, while initially attractive, can quickly end up costing a lot of money with no easy option to switch.

An observability solution built for open standards ultimately gives you back control, customization, and creativity. You won’t feel punished for scaling your success.

Consolidation into one unified platform is also essential to modern observability. Less signal fatigue empowers IT teams to achieve better MTTR, faster root cause analysis, and a bird’s-eye view of the landscape. By gaining context for issues when they arise, DevOps teams gain a better understanding of the system as a whole. Monitoring goes from a reactive practice to a proactive, data-driven approach. That’s modern observability.