15 monitoring platform capabilities for enterprise DevOps teams

DevOps teams need robust monitoring solutions to support observability requirements and safely maintain their workloads. Having access to the right data at the right time lets developers, operators, and site reliability engineers accurately analyze performance problems, system utilization, and how services are contributing business value.

Not all observability suites are comprehensive. This guide reviews 15 key features and characteristics that enterprise DevOps teams need to look for in a monitoring tool. Together, these traits produce an ideal end-to-end monitoring solution that lets you efficiently capture and evaluate metrics across your apps and environments.

1. Collation of all observable data

Many tools claim to offer complete observability but are in fact limited to specific layers of the stack, for example, application metrics such as request error count or system metrics such as CPU utilization. However, this gives an incomplete picture. A holistic collation of insights across both these layers, in addition to network, WAN, storage, and other layers, is required to attain full observability and accurately interpret how your workload arrived at its current state.

Consequently, organizations should ensure their observability solution is capable of ingesting all types of monitoring data, including cloud performance, logs, network activity, and application traces. Deep visibility into system components allows you to jump from metrics anomalies and warning messages to detailed traces that explain why a component has slowed down or how a security issue was exploited.

2. Monitoring system with versatile alerting options and customizable notification channel

Merely capturing data isn't enough to inform you when problems arise. This makes it imperative that your monitoring tools are equipped to send real-time alerts and notifications. Precisely defining alert triggers, conditions, and actions will ensure relevant stakeholders are directed to new incidents as soon as they occur, minimizing incident resolution time. AI-powered dynamic alerting thresholds can help ensure accurate notifications are automatically generated when anomalies are detected, even if static threshold configurations haven’t been met.

3. Self-service developer access

Developers are often best placed to spot anomalous trends in metrics and suspicious error reports in log files. Observability systems should thus accommodate self-service developer access, such as by allowing developers to directly request access to specific data without leaving the platform. This enables more efficient processes for incident investigation and resolution by tightening communication loops between devs and platform administrators.

Platforms that aren't designed to support self-service access could even mean they demand that access be restricted to a subset of users, reducing the data's utility.

4. Support for all environments across cloud and on-premises

The ability to capture data from all potential sources should be treated as a fundamental requirement of any observability system. Having to chase data across multiple tools and environments makes it harder to track trends and follow which issues are new, ongoing, and resolved.

Unified platforms consolidate insights from cloud, hybrid, and on-premises environments across your entire fleet of applications and infrastructure components. These are always the best fit for enterprise use, as they provide a single destination to centrally review collated insights.

5. Customization through plugins and integrations

Observability solutions should support a wide variety of data sources in their default configuration; however, sometimes an organization may require a customizable platform to add compatibility with their existing systems and services.

AI-powered observability platforms such as Site24x7 enable this by offering plugin-based architectures, an extensible API, and officially supported integrations with popular vendors. These capabilities save time and result in lower maintenance overhead. They also help the platform scale as you grow, meaning less chance you will be constrained by your observability system as you add new tools and processes to your DevOps workflows.

Site24x7 provides plugin integrations for your entire stack, including popular cloud, database, and security platforms. You can also write your own integrations to connect to bespoke data sources and gain more insights from across your apps.

6. Surfacing of SDLC and DevOps performance insights

Correctly configured observability solutions deliver comprehensive visibility into your overall DevOps activity and performance because they're positioned to monitor every change you make. These insights should be surfaced to help you identify broader trends in your software delivery lifecycle (SDLC).

Metrics, including cycle time for delivering new features, number of deployments that have occurred, and change failure rate provide invaluable data to DevOps leaders seeking to improve productivity and reliability. These values help organizations understand how resources are being used, as well as if factors such as an increased deployment rate have reduced stability.

These capabilities are usually available in advanced AI-driven monitoring platforms like Site24x7. Its cloud deployment monitoring and log analysis enable accurate attributing of metrics changes to the DevOps activity that caused them; this, in turn, allows you to understand how your software delivery cycle is performing.

7. Direct integration with developer tools

The best observability solutions directly integrate with the tools, technologies, and processes that DevOps teams are already using to achieve their tasks, including CI/CD pipelines, GitOps-driven delivery methods, and cloud platforms. This helps ensure that observability coverage is automatically enabled for new infrastructure components and software deployments.

For example, you should be able to use infrastructure-as-code (IaC) providers to install your observability solution’s monitoring agents. Site24x7 provides ready-to-use Ansible playbooks that let you easily deploy its agent to environments within your regular infrastructure provisioning workflow. You can add the playbook to your Ansible state files to enable Site24x7 monitoring for your hosts, with no complex manual configuration changes required.

imilarly, you need to be able to automate the management of the monitoring platform itself, such as the configuration of alerts and enabled data sources. Site24x7 offers a Terraform provider that can control the resources in your account. Including Site24x7 resources alongside your other Terraform components allows you to automatically register new monitoring targets when you deploy your app, preventing coverage gaps from occurring.

8. Intuitive dashboard visualizations

Raw metrics data is rarely useful to developers—there's simply too much data to filter out meaningful records from the noise. Hence, it's essential that observability platforms provide intuitive tools for filtering, tagging, and visualizing collected data; this includes powerful dashboard tools for graphing trends and easily spotting anomalies.

Moreover, these capabilities should include support for generating both high-level overview visualizations—suitable for sharing with executives—and precisely tuned technical views for developers and operators to interrogate. This ensures everyone is viewing the same live data set, albeit in different ways, instead of a snapshot that's been duplicated into an external reporting solution.

9. Easy data sharing

Observability data collected by a platform should be easily sharable, such as via publicly accessible links, albeit ones with security options. Site24x7 allows you to restrict access to certain IP address ranges, even when public dashboard access is enabled.

Not only does data sharing ensure all relevant product stakeholders can conveniently access data, it also allows you to archive relevant insights beyond the platform’s retention period. This can be critical if you need to demonstrate continual compliance as part of any future audit procedure.

10. Scalability, fault tolerance, and high availability

Since organizations use their observability solution to investigate system failures, the platform must be dependable and highly available. Similarly, the solution should be capable of scaling to support both current and future utilization, including sudden spikes in metrics volume.

Scalability can be difficult to analyze when you're choosing a commercial vendor. Reviewing the SLA terms will help reveal the degree of stability you can expect. If you're self-hosting your own installation, then make sure to consult the vendor to determine the hardware resources you'll need to support your environments.

11. Compatibility with containers and cloud-native environments

Container-driven cloud-native development models can accelerate DevOps velocity, but they also bring unique observability challenges. Containers are ephemeral short-lived resources, so the observability solution must be specifically designed with them in mind. For example, solutions that require significant manual effort to register new monitoring targets won't be suitable for containerized workloads.

Similarly, your platform should be capable of accommodating complex cloud deployment scenarios. This includes multi-cloud environments where data needs to be ingested from a variety of infrastructure components, including a mix of manually provisioned, automatically created, and vendor-managed resources. The ability to distinguish between all these asset classes—as well as any on-premises services you use—is crucial for finding valuable insights within the huge volume of data that cloud-native deployments produce.

Site24x7’s platform allows organizations to cohesively monitor public, private, and hybrid cloud deployments, offering a unified view of applications whether they’re in the cloud or a company’s own virtualized data center.

12. Correlation of metrics to business value and processes

Observability solutions should be capable of correlating metrics changes to their effects on your processes and the value created within your business. How this is achieved can vary, but the most powerful platforms offer AI-powered analysis tools to link metrics to an organization's KPIs.

For example, an increase in request latency could be detected as a potential breach of revenue targets if it lasts for longer than a calculated time, due to the increased risk of customers abandoning their carts.

Intelligent observability systems can also take action to automatically mitigate the negative effects of incidents. In the above example, the service could respond by spinning up additional replicas to serve more traffic. This resolves the incident without requiring developer intervention, in turn maximizing the time devs can otherwise spend creating value.

Continual analysis of resource utilization also helps inform long-term capacity planning, such as by predicting future incidents based on previously observed usage trends.

13. Simple configuration with automatic monitor discovery

Managing your observability solution should not be a chore—you should be able to focus on the data within it while the platform recedes into the background. To this end, organizations should favor services with simpler configuration experiences, as these will minimize set-up time and reduce maintenance overhead.

In particular, you should seek solutions that continually monitor cloud resources, detect newly added workloads, and automatically begin monitoring them. Not only does this remove the need to spend time regularly reconfiguring monitoring, it also guarantees coverage of actively used assets as you create and destroy environments.

14. Clear and accessible documentation

Organizations must implement detailed and easy-to-understand documentation to get the most out of any solution. Ideally, docs should be accessible either within the platform or on a dedicated site, so that anyone with access to your data can understand the meaning of different terms and learn how to complete key processes.

Good documentation needs to be supported by a variety of examples that explain how to use the solution in common scenarios. For example, monitoring a containerized system might require a unique configuration different from the setup for an on-premises application; examples help make this clear, unlike docs that solely contain reference information that you must consolidate yourself.

15. Robust support options

Reliable support options are critical to enterprise users when answers can't be found in the documentation or a technical problem occurs. The level of support available can vary substantially between observability solutions, ranging from no formal support for many open-source systems to dedicated account managers and named technical contacts when purchasing a commercial solution at scale.

Community resources such as forums and chat channels are also helpful for quickly addressing ad-hoc queries about specific areas of platform functionality.


There's a wealth of observability platforms and monitoring solutions claiming to help DevOps teams collect and utilize their data. However, many of these solutions lack the deep insights, versatility, and robust support demanded by enterprise users. It's critical you evaluate potential choices to ensure they include the capabilities you require for your operations.

We've explored how characteristics such as comprehensive data collection, automatic discovery of monitoring targets, and dynamic alerting options provide timely visibility into what's happening in your DevOps portfolio.

Complete monitoring coverage using a unified platform such as Site24x7 offers invaluable information to inform future changes to your DevOps loop, helping you sustain maximum development velocity and reliability.

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