A Guide to Observability Tools and Platforms

Your organization is faced with a huge number of options for observability tools and platforms. Without understanding your needs and use cases, you could be leaving your organization vulnerable to costly disruptions.

Without the right observability tools in place, your organization could face downtime, buggy user experiences and, eventually, a loss of revenue for your products.

Luckily, there’s no shortage of tools and platforms across the tech landscape that can help with observability. Your practice can be as big or as small as you choose. Yet those factors can also present their own set of difficulties for you and your business.

Navigating the world of observability can be challenging, but with the right insights and understanding of your own use case, you can find the right tools.

We created this guide to help you understand the role and benefits of observability tools in your tech stack, features needed for successful observability, steps for successful implementation, and challenges to understand.

Let’s dive in!

Defining Observability Tools, Their Role in the Tech Stack & Benefits of Adoption

Observability is the ability to measure a system’s current state based on the telemetry data it generates, such as logs, metrics, and traces.

Observability ensures businesses have constant visibility into their operations, and related observability tools are designed to provide insight into how each component of a system is functioning. These tools collect and analyze data from various sources to give businesses a complete picture of their system’s current state.

Organizations need to consider the kind of outcomes they want to see from observability tools and platforms. What insights are you looking to gain? What systems need observability practices attached to them? What can you afford?

Perhaps you’ve got a younger company with just a few services. You may want to consider open source software for observability – like Prometheus, OpenTelemetry, or OpenSearch. If you’re an organization with more advanced systems, big deployments and complex architectures, you’ll likely need observability tools with unified insights to correlate data and fix problems fast, which are often proprietary.

Those who find the observability technologies the match their use case and requirements can realize:

  • Improved system monitoring, issue detection and troubleshooting capabilities so you avoid downtime and frustrated customers
  • Full visibility into their system without overwhelming costs
  • Better collaboration between development and operations teams, creating more efficient workflows and a focus on digital business innovation vs. constantly running down problems
  • A more proactive approach to system management, including improved performance optimization throughout your critical architecture

Features to Look For in Observability Tools

Any organization weighing their options for observability tools needs to take a hard look at what features they need for success. Many of these features appear in open source software tools, but some can only be acquired through buying a proprietary software platform.

Here are features that need to be considered in observability tools:

Alerting. This ensures you’re notified of critical events, and configuring the right alerts is the foundation of any proactive development, DevOps, and validation practice.

Your tool should allow for a search query that continuously scans your telemetry data and alerts you when certain conditions are met. Some use a simple search query or filter, while others are more complex and involve numerous conditions with varying thresholds. 

A good alerting system should meet your use case, and alert you on measures critical to your business and product needs. Those alerts should be delivered to your relevant stakeholders on multiple pathways to ensure they get to the right people for quick action. 

Anomaly detection. Especially for organizations looking to scale their systems and thus scale their observability practice, having a tool that allows for anomaly detection is essential.

With data coming in through numerous components—potentially hundreds and thousands of them—having anomaly detection that’s automated through AL/ML capabilities becomes very important for a competent observability practice. Algorithms used for this should be trained on large datasets of normal behavior and detect a wide range of different anomalies.

These capabilities should allow you to accelerate debugging and troubleshooting to reduce service interruptions, and identify anomalous data spikes to reduce costs as well.

Cost control through data optimization. It doesn’t take long for observability costs to spiral out of control. It’s possible you could be paying for reams upon reams of data that’s useless and noisy to be analyzed and stored through your observability systems. Especially if you aren’t in a highly-regulated industry where data needs to be retained, consider ways to cut down on these data costs. 

Organizations need automated capabilities, including storage and data optimization, within their observability tools that directly enable continuous control over data volumes and related charges. This way, your organization will only pay for only the data necessary to meet your unique observability requirements.

If you’re analyzing and paying for data that isn’t important to your business mission you’re not doing observability in the right or most advantageous way. Data optimization plays a huge role in getting your observability practice right.

Pre-built dashboards. Observability requires quickly interpreting signals and information within huge volumes of telemetry data, generated from hundreds or thousands of distinct cloud components. You can put together queries, dashboards, and alerts to provide these insights, but it can require hours of configuration, tweaking, and reconfiguration. 

Plus, all too often, these insights live in separate silos that can obstruct troubleshooting flows that require seamless analysis across different datasets.

Instead of the deep and manual work often required to come up with your own observability dashboards, your tools should give you the option of utilizing pre-built dashboards that can be iterated on to meet your needs.

Data correlation. Troubleshooting in your environment might require constant switching across different interfaces and contexts to manually query data where there may be a problem, prolonging incident investigations.

Even more cumbersome can be troubleshooting microservices, which requires engineers to correlate different information from many components to isolate issues within complex application requests.

Data correlation can help engineers overcome analysis challenges and reduce MTTR for production issues. Having a single pane of glass where all your relevant telemetry data is correlated automatically can help you get to the bottom of challenges faster.

Service instrumentation. Data collection technologies—such as open source ones like OpenTelemetry and Fluentd—can be burdensome to configure, upgrade, and maintain, especially when multiple different technologies are in production. Plus, instrumenting services to expose logs, metrics, and traces can be complex and time consuming.

Invest in observability tools that provide automated service instrumentation, alongside associated capabilities like service discovery and data collection. These will save you time and get you up and running with your observability in minutes.

Distributed tracing. A method used for profiling and monitoring applications—especially those built using a microservices architecture—distributed tracing helps pinpoint where failures occur and what causes poor performance. 

Your observability platform should adopt a method to conduct distributed tracing as a more advanced way to keep tabs of what’s happening in your environment. You’ll be able to pinpoint the sources for request latency, find the service at fault when experiencing an error, and realize the full context of the request execution.

Steps for Successfully Implementing Your Observability Tools

After you’ve selected your observability tools, you’ll need to implement them correctly to get the most out of your investment. This takes proper planning and assessment and if done correctly will save significant headaches for you and your team down the road.

First, make sure your new tool integrates with other related tools in your current tech stack. Ensure that your applications are correctly instrumented to start emitting the correct telemetry data you’re trying to measure. They should be instrumented to reflect your business logic, not be constrained. The last thing you want is to be forced to re-instrument and redeploy during an emergency.

Setting up your monitoring and alerting is also critical as part of the implementation process. What are you trying to observe as part of using your tool? Ensure your monitoring can keep up with your business as you scale and as business priorities change. This blog contains more details about how you can simplify your cloud monitoring during implementation and setup.

Cost is a critical factor for implementation as well. As data volumes increase, the cost of your system can become prohibitive. During implementation, utilizing sub-account features within your observability tools is advisable. You can segregate your data based on specific use cases and retention requirements with different policies for each sub-account, ensuring critical data is preserved for the required duration while less crucial data can be retained for shorter periods.

Ensure that you have the correct support system in place so your entire team can start using the tool. Find out if your customer support is always available, responsive and can answer any questions you have in a timely manner. 

Determine if your vendor provides training on your new tools. During the evaluation process, find out if you need formal training or a more ad hoc approach (note: Logz.io provides both for customers).

Common Challenges for Observability Tools and How to Overcome Them

Observability, like every discipline of IT, carries its own challenges. But, if you’re aware of those challenges going into your journey, you’ll be able to overcome them. 

Alert fatigue. Data and alert volume, velocity, and variety can mean that signals get lost among the noise, as well as create alert fatigue. To overcome this, identify the most critical alerts and establish appropriate thresholds within your observability tools. 

Don’t try to list every potential error scenario and create alerts for each of them. This is called cause-based alerting and should be minimized. Instead, it’s advisable to opt for symptom-based alerting. This way, you’ll get alerts triggered when observable symptoms that affect users become evident or are anticipated to happen in the near future.

Team siloes. Siloed infrastructure, development, operations and business teams can lead to many key insights getting lost or surfacing too late for taking meaningful action. Overcome this by fostering a culture of collaboration and establishing cross-functional teams.

Lack of standardization. When there isn’t enough standardization across the tech stack, that can make it difficult to track system performance consistently. The solution is to implement industry standards across your tech stack, which is something open source tools like OpenTracing can help with.

Poorly-configured tools. These can provide inaccurate data, leading to incorrect insights. This is overcome through careful planning, configuration, and testing of the observability tools before you begin using them.

Limited or incomplete data. Without the full picture of your data, you can have blind spots that prevent you from identifying potential issues. The solution: use a range of data sources and analysis tools to gain a complete view of system performance.

Cost spiraling out of control. These days cost has to be top of mind for all organizations when considering technical resources. Many observability tools charge you for keeping and storing data you don’t need and will never need. You can overcome this challenge by utilizing cost control tools and configuring your systems to keep cost in mind.

Six Top Observability Platforms in 2024

Let’s take a closer look at six of the top observability software platforms as you weigh your decision.


Splunk was effectively the first company to provide centralized log management. They’ve been around since 2003 and are widely known for the advanced analytics and machine learning provided by their widely-adopted platform. 

For observability, Splunk provides a suite of tools including Application Performance Monitoring (APM), Infrastructure Monitoring, IT Service Intelligence, Log Observer Connect, Real User Monitoring, Synthetic Monitoring and On-Call, their automated incident response feature.

Splunk has a reputation for being an expensive service, which in some larger use cases may be warranted. However, it won’t be justified in all use cases and organizations are best served to determine  and their data filtering capabilities are limited. Organizations will pay for data that’s never needed. Plus, with Splunk, organizations will need to identify and remove unneeded data manually. 

New Relic

Providing an all-in-one observability solution, New Relic boasts a platform that contains many observability tools needed for success today.

This includes application monitoring, infrastructure monitoring, browser monitoring, application security, log management and a new generative AI assistant for observability called New Relic Grok.

New Relic takes an approach to data and storage optimization that doesn’t provide the best service for customers. They don’t simplify the process of identifying and de-prioritizing telemetry data that isn’t needed, and they lack a cold storage option that could reduce the cost of log data.


Datadog is a publicly traded cloud monitoring, observability and security company, with products serving many observability and security use cases. Their platform users deploy an agent that ships observability and security data to the Datadog platform, where it is stored and analyzed by the user. From there, customers decide which products they want to analyze the data on Datadog’s SaaS platform. 

Datadog, despite its strengths, is expensive, mostly proprietary, lacks key administrative controls including manual data filtering, and customer support has been cited as a weakness.


Over the last decade, Prometheus has emerged as the most prominent open source monitoring tool in the world. Prometheus collects and stores metrics as time series data, or info stored with timestamp data so users can get a better understanding of metrics at a certain point in time. There’s no upfront cost or vendor lock-in.

You’ll need to use third-party implementations for service discovery, alerting, visualization and export with Prometheus. The platform offers those integrations, but they don’t come natively.


OpenTelemetry (informally called OTEL or OTel) is an observability framework that generates and collects telemetry data from cloud-native applications OpenTelemetry aims to address the full range of observability signals across traces, metrics and logs. While they are working to solve issues around data collection for teams, data storage and analysis is still siloed in OTEL and requires further solutions.

OTEL is a community-driven open source project, which is the result of a merge between OpenTracing and OpenCensus projects. 

OpenTelemetry offers several components, most notably: APIs and SDKs per programming language for generating and emitting telemetry; a collector component to receive, process and export telemetry data; OTLP protocol for transmitting telemetry data and more.


In many cases, you have two choices when it comes to observability tools. You can use open source software tools that can be cumbersome to manage and scale with no unified way to analyze logs, metrics and traces, or you can use proprietary tools that can be extremely expensive.

Logz.io meets customers in the middle. Our Open 360™ platform helps customers unify data, extract meaningful insights from it quickly, and reduce costs at the same time. We provide support for the best-in-class open source observability tools while also being much less expensive than proprietary solutions. Backed by AI, we’re committed to providing customers a cloud native observability service that only charges them for the data they need and nothing else.

Logz.io provides unified observability for logs, metrics and traces in one platform for complete telemetry analysis and correlation. Open 360 features allow you to: 

  • Unify the most critical telemetry data from Kubernetes-based infrastructure in a single view with Kubernetes 360.
  • Unify distributed tracing, service topology and services analysis to provide full visibility into application health and performance, formalizing an application observability-based alternative to traditional APM with App 360.
  • Automatically discover every service running on your cluster with Easy Connect, while providing the option to collect logs, metrics, and or traces from each one. You can automatically instrument your applications to expose trace data with one click.
  • Inventory all of your incoming telemetry data to easily determine what you need, and what you don’t with Data Optimization Hub.
  • Collect your logs, metrics, and traces with a single agent with Telemetry Collector.

If you’re interested in seeing how our Open 360 platform for essential observability can meet your needs, sign up for a free trial today.

Get started for free

Completely free for 14 days, no strings attached.