Why Do Companies Change Their APM Tools?

Why Do Companies Change Their APM Tools?

The decision to change an application performance monitoring (APM) tool is rarely driven by a single missing feature. System architecture, team workflows, costs, and security requirements change over time. At some point, organizations begin to ask whether the tool they use still meets their needs. That is not an easy decision. Over the years, teams build dashboards, alerts, integrations, and working habits around their platform. Moving to a new one means rebuilding some of them, migrating data, and helping the team adapt to a new tool. So the main question is not simply, “Is there a better tool?” It is: Does our current solution still meet the needs of our changing systems and teams? What prompts companies to reconsider their APM tools?

1. Costs become harder to predict A platform’s cost can grow faster than expected as usage increases and infrastructure changes. Some licensing models are based on monitored hosts or services; others depend on data volume, users, or add-on features. If licensing is tied to service count, host count, or data volume, a growing microservices estate can significantly increase costs. That does not mean the current tool is necessarily too expensive. The important questions are what the organization is paying for and how costs are likely to change as the system grows. Compare more than the license price: include migration, setup, operations, data retention, and support costs.
2. Incident investigation is spread across tools Imagine a latency alert on the payments service at 2 a.m. The application team checks traces, the infrastructure team opens metrics dashboards, and the database team reviews query logs. Because the data lives in different screens and contexts, teams spend time establishing when and where the incident began before they can investigate its cause. This is not an isolated situation. According to Grafana Labs’ 2024 survey, more than two-thirds of teams use at least four different observability technologies. In its 2025 survey, complexity was respondents’ top concern, while alert fatigue was the biggest obstacle to faster incident response. Using multiple tools is not automatically a problem. What matters is whether those tools give teams the context they need during an incident. Can they move easily from a trace to the related logs and metrics? Can different teams review the same evidence together? If the answer is no, the problem may be the connections between tools rather than the number of tools.
3. System architectures change Companies’ application environments evolve over time. New microservices, Kubernetes clusters, cloud services, and AI components come online, while older applications continue running in production. An APM tool’s value depends on how much of this environment it actually makes visible. Automatic instrumentation can provide a quick start for popular technologies, but coverage varies by application and component. Older systems may require additional instrumentation or integrations. That is why a supported-technology list is not enough. Before making a decision, test what data the platform actually collects from your own services and identify the gaps.
4. Operational needs expand Knowing that an alert fired is not enough to resolve an issue. Teams also want to understand its likely cause, which services are affected, and how users are experiencing it. That makes service dependencies, correlated traces, logs and metrics, critical user journeys, and AI-assisted analysis increasingly relevant. In Grafana Labs’ 2025 survey, faster root cause analysis and training-based alerts were among the leading observability capabilities respondents wanted from AI and machine learning. Many modern APM tools offer some of these capabilities. Comparing feature names alone can therefore be misleading. The better measure is which capabilities teams use in their daily workflows and how much those capabilities reduce incident resolution time.
5. Security and deployment requirements change Where data is processed, which systems receive it, and which model is used for AI analysis can directly affect tool selection, especially in regulated industries. SaaS or on-premises deployment, the need to use a customer’s own model or API key, and requirements for filtering telemetry data should all be part of the evaluation. Open standards such as OpenTelemetry make it easier to use telemetry with different tools. According to Grafana Labs’ 2025 survey, half of organizations increased their investment in OpenTelemetry for the second consecutive year. A move between platforms still requires teams to revisit dashboards, queries, alerts, and data models. Open standards do not eliminate migration costs, but they can reduce vendor dependence in the data collection layer and give organizations more options for managing dependencies. Is changing APM tools always the right decision? No. If the current APM tool meets the organization’s needs and teams use it effectively, changing simply because a new product is available is not a good reason to migrate. The decision should account for rebuilding dashboards and alerts, retaining historical data, reconnecting integrations, training teams, and running platforms in parallel. Even if a new solution meets a specific technical need, the total migration cost may outweigh the expected benefit. The goal is not to change tools at all costs. It is to compare the current solution with possible alternatives using the organization’s real workflows. How can you evaluate an APM migration fairly? A sound evaluation starts with understanding current usage:

• Which alerts, analyses, and workflows do teams use today?
• Which tools do they consult to find the source of a production issue?
• Can traces, logs, and metrics be reviewed together for the same incident?
• How much coverage do critical services and legacy applications have?
• What are the security, data-retention, and deployment requirements?
• What is the total cost when licensing, setup, and operations are considered together? Then test these scenarios in the new platform under conditions that are as similar as possible. Instead of decommissioning the current APM immediately, run a parallel proof of concept (POC) on selected services. This lets you compare data coverage, setup effort, analysis quality, team experience, and cost against the same incidents. Write down the success criteria before the POC begins. They might include time to find the cause of selected incidents, percentage of services covered, integration effort, and three-year total cost of ownership. Without predefined criteria, a POC can end up selecting whichever tool gave the best demo. Conclusion The decision to change an APM tool should not be based on a feature checklist or a short product demo. Evaluating costs, system architecture, team workflows, and security requirements together helps determine whether the current solution still fits the organization. IyziTrace gives a clear answer to one question in that evaluation: Which model receives your data when you use AI-assisted analysis? With IyziTrace, the customer chooses the model and API key. The platform is available as SaaS or can run in the customer’s own environment (on-premises). It also combines OpenTelemetry-based data collection with the ability to investigate traces, logs, and metrics in a shared context. Whether it can replace your current APM tool depends on the capabilities you use and the systems you run. The most reliable approach is to define your own scenarios and test the current tool and a potential alternative under the same conditions. That way, the migration decision is based on measurable results rather than assumptions. Contact us to plan a POC alongside your current APM tool. Sources
• Grafana Labs, 2024 Observability Survey: 5 key takeaways
• Grafana Labs, 2025 Observability Survey: key findings