Beyond Traditional APM: IyziTrace’s OpenTelemetry and AI Approach

Beyond Traditional APM: IyziTrace’s OpenTelemetry and AI Approach

APM tools make it easier to monitor application performance. IyziTrace brings together an OpenTelemetry-based approach, connected telemetry data, and AI-assisted analysis to help teams investigate issues in a shared context. It’s 2:05 p.m. Response times on the payment page are increasing. The application team is looking at traces, the infrastructure team at CPU and memory charts, and the database team at query times. Each team sees no clear problem on its own screen. Meanwhile, users are unable to complete their transactions. In situations like this, the problem is often not a lack of data. The data is scattered across different tools, formats, and team dashboards. Teams need a shared context in which they can investigate the same event together. Traditional APM tools provide a strong foundation for monitoring application performance. Today, many APM platforms also support logs, metrics, traces, and AI features. That’s why IyziTrace’s difference cannot be explained through a feature list alone. It comes down to the standards used to collect data, how different signals are connected, and how AI fits into day-to-day operations. Observability: Shared context and collaboration APM focuses on tracking application performance. Observability takes a broader approach, helping teams understand what is happening in a system by examining signals from applications and infrastructure together. When teams work across separate tools and screens, an issue can quickly turn into the question, “Whose problem is it?” An observability approach helps teams ask instead: “What happened, and how did it affect users?” This approach has three practical aspects:

• Shared evidence, shared language. When the necessary context and instrumentation are in place, application, infrastructure, and database teams can investigate the same event through the same trace. The discussion is grounded in shared data rather than disconnected charts.
• Shared ownership. The team that builds a service also has a role in deciding how it should be observed. Observability becomes part of the development process.
• Learning instead of blame. After an incident, teams focus on why the system produced that outcome and how they can detect it earlier next time, rather than on who made a mistake. This culture does not develop on its own. Data needs to be accessible and connected. When teams use different agents, data models, and screens, it can be harder to create a shared view. Why OpenTelemetry matters IyziTrace uses OpenTelemetry (OTel) to collect and transport telemetry data. OpenTelemetry is a vendor-neutral, open-source framework and set of standards for traces, metrics, and logs. It is developed under the Cloud Native Computing Foundation (CNCF). This approach helps organizations build telemetry generation without tying it tightly to the agent of a particular observability platform. The destination for the data can be determined through configuration, giving organizations more control over how telemetry is produced and transported. OpenTelemetry can offer organizations several advantages:
• It can help reduce platform dependency. Instrumentation and data transport can be built around open standards. However, dashboards, queries, alerts, and data models specific to a platform may still need to be addressed during a migration.
• Data flows can be configured. With suitable Collector configurations and processors, organizations can mask sensitive fields, filter unnecessary data, and adjust sampling rates.
• Different technologies can share context. When context propagation between services is configured correctly, a request can be followed in the same trace as it passes through components built with different technologies.
• There are multiple instrumentation options. Automatic instrumentation is available for common languages and components. Its coverage depends on the language, libraries, and application design; application-specific workflows may require additional instrumentation. Older systems may require manual instrumentation, bridge components, or custom integrations. OpenTelemetry does not mean every system can be monitored without additional work, or that migrating platforms will always be effortless. The right approach depends on the application and infrastructure components involved. More data is not the goal; connected data is Having many charts in a platform does not guarantee that an issue will be resolved faster. What matters is whether traces, logs, and metrics can be connected during an investigation. Let’s return to the payment example. In a sample scenario, if the required instrumentation and context propagation are configured, a slow transaction’s trace might point to a delay while obtaining a database connection. Log records carrying the same trace context might show warnings related to the connection pool. Metrics from the same time window might show that a batch job, which started at 2:00 p.m., increased database connection usage. These findings can help teams assess the source of the issue together. In this example, they may point the investigation toward the batch job rather than the payment service code. The team could then consider changing the batch job’s schedule or separating connection pools. Examining related signals instead of relying on a single chart helps guide the investigation and bring teams together around shared evidence. AI can speed up the analysis process The quality of AI analysis depends on the quality of the data it receives. Data collected in a consistent format and connected within the same context can help an analysis consider relevant findings together. In IyziTrace, AI supports the investigation process. In the payment example, it could summarize possible causes by considering findings related to the connection pool, the batch job’s start time, and the affected traces. The analysis does not replace the team’s decision; it is intended to help shorten the time it takes to find evidence and choose the next step. Customers can use their own model or API key for AI analysis. This keeps model selection and the related usage costs under the organization’s control. SaaS and on-premises deployment options can be considered according to the organization’s needs. With the right integrations and permissions, AI can also support operational workflows. For example, through the Model Context Protocol (MCP), it can interact with an organization’s tools to help open a ticket or notify the relevant team. The actions it can take are determined by the organization’s security and approval rules. AI agents need to be observed, too As organizations build their own AI applications and agents, it becomes important to evaluate both their performance and their behavior. Response time alone is not enough to assess an AI agent. IyziTrace’s AI Agent Probe approach helps test AI agents against criteria such as accuracy, consistency, security, personal data leakage, and harmful content generation. These checks can be connected to development and CI/CD processes, allowing teams to assess whether a new release meets defined quality thresholds before deployment. With this approach, quality can be assessed during development, rather than only after an issue appears in production. Evaluate the difference with your own scenarios IyziTrace does not claim to offer isolated features that no traditional APM platform has. Its difference lies in its OpenTelemetry-based data collection approach, the ability to investigate signals in a shared context, and AI analysis that organizations can use with their own preferences. A POC can help answer questions such as:
• How much of the existing application and infrastructure data can be collected?
• What integration work is required for older and newer technologies?
• Can traces, logs, and metrics be examined together during an investigation?
• Can different teams investigate the same event in a shared view?
• How much time do teams currently spend finding the source of an issue?
• How portable are the instrumentation and platform-specific components?
• Does AI usage align with the organization’s security and model preferences?
• How does the total cost change when licensing and operating costs are considered together? The fairest comparison is not simply a side-by-side review of product names or feature tables. Identify the workflows teams actually use on their current platform, then compare those same scenarios during a POC. Conclusion Traditional APM tools provide an important foundation for monitoring application performance. IyziTrace builds on that foundation with OpenTelemetry-based data collection, connected telemetry investigation, AI analysis under the organization’s control, and AI Agent Probe. A platform’s value is measured not only by how many features it has, but by how quickly teams can understand a real issue and work together to resolve it. Evaluate it with your own scenarios. Identify your critical workflows, then work with our team to plan a POC that tests IyziTrace under the same conditions as your current setup.