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LOGICMONITOR BLOG

Consolidation and Modernization in Enterprise Observability

Learn how enterprises consolidate observability tools and unify infrastructure with Internet visibility to reduce costs and accelerate issue resolution.

6–9 minutes
Denton Chikura

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Consolidating observability tools and unifying inside-out infrastructure monitoring with outside-in Internet visibility is the architectural foundation enterprises need for AI-driven autonomous operations.

  • Enterprises are mandating OpenTelemetry for all new observability deployments to break vendor lock-in, reduce costs, and enable flexible instrumentation.

  • Centralized observability teams are emerging to govern tool selection, define standards, and eliminate the inefficiencies of uncoordinated adoption.

  • Internet Performance Monitoring has become as critical as APM, filling the visibility gap between internal systems and real-world user experience.

  • Evaluate your current tooling landscape now: consolidation today determines whether your organization can credibly pursue Autonomous IT tomorrow.

Organizations are seeing measurable benefits from investing in observability, including faster issue resolution, cost reduction, and improved business outcomes. But challenges remain: rising costs, tool fragmentation, and the need for more comprehensive monitoring of Internet dependencies and user experience. These are the challenges driving change, and the best practices enterprises are adopting to address them.

Rising concern over observability costs

Observability is expensive. In 2022, it emerged that a single Datadog customer had accumulated a $65 million observability bill. That disclosure triggered broader industry awareness of how quickly observability costs can escalate.

Jeremy Burton wrote, “Some observability tool vendors say organizations should allocate up to 30% of their total infrastructure cost to monitoring and understanding the state of their IT system. That’s just nuts.”

MELT myopia driving higher costs

One of the root causes of higher costs is the belief in the three pillars of observability (now MELT: infrastructure Metrics, Events, Logs, and code Traces) and the assumption that everything needs to be monitored this way. Storage vendors, in particular, love logs because they drive significant storage needs.

Mature IT teams are starting to abandon the tools-first approach for monitoring. Instead, they’re adopting an approach that begins by determining the best method to monitor specific applications or infrastructure components based on criticality and the value of monitoring data.

The challenge of multiple APM tools

Many IT leaders face the challenge of managing multiple APM tools, each with its own features, dashboards, and data sources. This fragmentation leads to inefficiencies as teams spend valuable time correlating data across different platforms to identify and resolve issues.

Research confirms what many IT leaders already know: monitoring tool sprawl is widespread, with more than half of IT leaders citing reliance on multiple tools with siloed views as a core challenge. The variety is often driven by application team preferences. In most large enterprises, there’s a mix of observability tools spanning the top APM platforms.

SRE Report, “How many monitoring or observability tools does your organization use?”

The future of APM: tool consolidation, OpenTelemetry, and cost savings

Organizations are increasingly looking to consolidate their observability and monitoring tools to gain insights faster and improve collaboration between teams.

One of the most compelling reasons for consolidating APM tools is the potential for cost savings. By reducing the number of tools and standardizing on a single platform, organizations can lower licensing costs, reduce training expenses, and streamline maintenance efforts.

OpenTelemetry (OTel) has emerged as a transformative force in observability. As an open-source project, it provides a unified set of APIs, libraries, agents, and instrumentation to capture and export telemetry data (metrics, logs, and traces) from applications. Adoption is accelerating as organizations recognize its potential to standardize data collection and reduce vendor lock-in.

This is the first major trend: organizations are making OTel a requirement for all new observability deployments. It has the potential to break vendor lock-in, enable consolidation of monitoring data, and give teams more flexibility in how they instrument and analyze their environments.

Some teams consider open-source technologies as an alternative. While open-source is effective for many use cases, in observability, many teams quickly discover that the effort to build, configure, integrate, and maintain a full open-source stack can exceed the total cost of an optimized commercial platform.

Centralized observability teams

The increased complexity of observability and the growing scrutiny of spend and platform adoption are driving the centralization of observability decisions. This is the second major trend: enterprises are creating central operations teams that own observability, or architecture teams that define standards, processes, vendors, and governance.

These central teams start by completing an inventory of applications, vendors, tools, reports, and critical needs. From there, they define broad priorities, standards, and best practices. This trend is accelerating across the industry as organizations recognize that uncoordinated tool adoption creates more problems than it solves.

The growing need for Internet and experience visibility

While APM tools are an established component of observability, there’s increased recognition that they’re systems-centric. They lack visibility into the dozens of factors that impact applications in a world where services are digital, cloud-centric, increasingly distributed, API-driven, and dependent on third-party providers, each of which relies on the performance and availability of the global Internet.

This need is amplified by the recognition that what matters most is the real-world digital user experience, wherever in the world the user happens to be. For services exposed via APIs, what matters is the experience the consuming system encounters across Internet paths and dependencies.

Modern enterprises recognize that customer experience is digital experience. Even most offline processes are supported by digital systems that define the quality of the experience.

For a rental company, what matters isn’t that the cluster reservation system has an average CPU utilization of 72%, but that the customer in line at the rental counter doesn’t keep hearing, “Sorry, my computer is slow” after waiting 20 minutes.

This is the third major trend: the requirement for Internet Performance Monitoring (IPM) as an essential component of the observability stack. EMA research published findings stating, “Internet Performance Monitoring tools have become just as important as application performance management, if not more so.”

With the growing number of outages caused by elements of the Internet stack and Internet-centric dependencies, the cost of inaction is also becoming clearer. As Internet-driven outages grow in frequency and impact, IT decision-makers face a choice between absorbing those disruptions or investing in IPM to navigate them proactively.

The formula for complete observability

The combination of these trends has led to a common pattern in mature enterprises: unifying inside-out infrastructure observability with outside-in Internet and experience visibility on a single platform.

APM provides an inside-out, system-centric view of applications and infrastructure. Internet Performance Monitoring delivers the outside-in perspective: what users are actually experiencing across Internet paths, cloud dependencies, CDNs, DNS, and third-party services. When these views are connected in one system, teams get the full picture from user to code.

Organizations adopting this unified approach are achieving operational efficiencies, increased uptime, and measurable improvements in user experience that directly impact the business. This also results in better alignment between IT and business strategy and greater recognition of the value IT operations teams bring. Companies like SAP, IKEA, Akamai, and leading financial services institutions are finding success with this model.

Consolidation is the foundation for Autonomous IT

The trends above point to a clear trajectory. Consolidation isn’t just about reducing cost or simplifying vendor management. It’s the prerequisite for what comes next: Autonomous IT, where AI can detect issues, reason about root cause, and take governed action across the full environment.

Here’s the core principle: you cannot automate across a gap in your data. If your infrastructure monitoring lives in one tool, your Internet and experience visibility in another, and your logs in a third, no AI layer can reliably correlate signals, determine impact, or act with confidence. Every blind spot between tools is a place where automation breaks down and humans get pulled back in.

Consolidation eliminates those gaps. When inside-out infrastructure telemetry and outside-in Internet performance data flow through one pipeline, into one context graph, with one intelligence layer interpreting it all, AI can reason across the full path from user to code. That’s what makes it possible to move from reactive alerting to proactive detection, from manual triage to automated root cause analysis, and from ticket-driven remediation to governed, autonomous workflows.

This is the real payoff of the consolidation and modernization work described in this article. Standardizing on OpenTelemetry, centralizing governance, and unifying APM with Internet Performance Monitoring aren’t just operational hygiene. They’re the architectural foundation that makes AI-driven operations credible, trustworthy, and effective in production. Without that foundation, Autonomous IT remains a slide deck aspiration. With it, teams can start operating with the speed, coverage, and confidence that modern digital businesses require.

This is exactly what LogicMonitor’s platform delivers. LM Envision provides hybrid infrastructure observability across cloud, network, servers, containers, and applications. Internet Performance Monitoring extends visibility beyond the firewall to show real user experience, Internet health, synthetic monitoring, and global reachability. And Edwin AI ties it all together with intelligence that reduces noise, identifies root causes, prioritizes by business impact, and supports governed action.

Unify your observability stack and unlock the path to Autonomous IT.

LogicMonitor combines infrastructure monitoring, Internet Performance Monitoring, and AI-powered intelligence on one platform. See how consolidation accelerates resolution and reduces cost across your environment.

FAQs

What is Internet Performance Monitoring and why does it matter for observability?

Internet Performance Monitoring (IPM) provides outside-in visibility into the Internet paths, CDNs, DNS, and third-party services that impact user experience. Traditional APM tools focus on internal systems, leaving blind spots in the dependencies that increasingly cause outages. IPM fills that gap by showing what users actually experience across global Internet infrastructure.

How does OpenTelemetry help reduce observability costs?

OpenTelemetry standardizes telemetry data collection through open-source APIs and instrumentation, eliminating vendor lock-in. Organizations can consolidate monitoring data across tools, choose the best backend for their needs, and avoid paying premium rates for proprietary data collection agents. This flexibility directly lowers licensing and maintenance costs.

Why is tool consolidation a prerequisite for AI-driven IT operations?

AI cannot reliably correlate signals or determine root cause when data is fragmented across disconnected tools. Consolidating observability onto a single platform creates the unified data context that AI needs to detect issues, reason about impact, and take governed action across the full environment, from user to code.

By Denton Chikura

Technical Writer

Denton Chikura is a technical writer and longtime observability advocate focused on helping site reliability engineers and engineering teams discover the tools and capabilities that strengthen internet resilience. He works at the intersection of monitoring, performance, and infrastructure to make complex systems more understandable and usable, bridging the gap between deep technical detail and real‑world operations. His goal is to help teams build faster, detect issues earlier, and recover smarter, ultimately making the internet a better, more reliable place for everyone.

Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.

© LogicMonitor 2026 | All rights reserved. | All trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.

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