ilert seamlessly connects with your tools using out pre-built integrations or via email. ilert integrates with monitoring, ticketing, chat, and collaboration tools.
We have transformed our incident management process with ilert. Our platform is intuitive, reliable, and has greatly improved our team's response time.
ilert is a low maintenance solution, it simply delivers [...] as a result, the mental load has gone.
Tim Dauer
VP Tech
We even recommend ilert to our own customers.
Maximilian Krieg
Leader Of Managed Network & Security
We are using ilert to fix our problems sooner than our customers are realizing them. ilert gives our engineering and operations teams the confidence that we will react in time.
Dr. Robert Zores
Chief Technology Officer
ilert has proven to be a reliable and stable solution. Support for the very minor issues that occured within seven years has been outstanding and more than 7,000 incidents have been handled via ilert.
Stefan Hierlmeier
Service Delivery Manager
The overall experience is actually absolutely great and I'm very happy that we decided to use this product and your services.
Timo Manuel Junge
Head Of Microsoft Systems & Services
The easy integration of alert sources and the reliability of the alerts convinced us. The app offers our employee an easy way to respond to incidents.
For the last 12 months, our team has concentrated on elevating product features by integrating generative AI. By seamlessly weaving AI into the fabric of the service, we have enhanced the efficiency and responsiveness of incident management processes and pioneered a new approach to handling crises. A powerful set of new functions is designed to simplify the preparational phase of incident management, speed up the response process, and take off the burden of manual work from on-call teams in the post-incident phase.
In this blog post, we've compiled all of the AI-assisted features, aiming to help you understand their potential for your incident management process. If you're curious to learn more or have questions about ilert's AI potential, we're happy to see you at our webinar "AI and Incident Management," which will take place on April 30 and will be led by ilert Founder and CEO Birol Yildiz. Reserve your seat.
Additionally, we have published a free guide on how to leverage GenAI & LLMs in incident management processes.
Prepare: Leverage AI for On-Call Scheduling
AI assistance in an on-call schedule creation
We at ilert believe that incidents are inevitable. Fast-paced and ambitious teams must constantly deploy and introduce changes to their service, which is a fertile ground for breakdowns and mistakes. So, the best strategy here is "forewarned is forearmed."
On-call scheduling is a critical component of effective incident management, as it ensures that there is always a designated responder available to address and mitigate issues at any time, day or night. A robust on-call schedule provides a structured, reliable method for mobilizing the right personnel with the necessary expertise to swiftly tackle emerging problems.
Establishing an effective on-call schedule poses several difficulties, primarily due to the challenges of balancing organizational needs with individual employee well-being. One of the most significant hurdles is coordinating the diverse availability of team members while ensuring that all critical skills and roles are covered 24/7. There are also technical complexities, like careful orchestration of schedule layers where different teams can be involved, shift rotation and proper hand-off, and administrative burdens related to the introduction of new team members or the departure of existing ones.
Keeping all these complications in mind, we have introduced a new approach to on-call scheduling. All manual adjustments that have to be done manually can now be automatically set up with the help of a simple chat interface. Provide all the information necessary for a new schedule in a natural descriptive way, and let ilert AI handle the rest.
Respond: Let AI Communicate Incidents Details
Reducing manual work during IT incidents is crucial to ensure that engineers can focus their efforts where they are most needed: on investigating and resolving the issue. In a high-pressure environment, every second counts, and the cognitive load on engineers is significantly increased. They need to rapidly assimilate information, apply their expertise to diagnose the problem and implement a solution. Having to concurrently manage communications—such as updates to stakeholders, coordination messages among team members, or instructions for a workaround—can severely detract from their primary task. Searching for the right words or crafting updates requires mental bandwidth that could otherwise be dedicated to problem-solving.
That's why we introduced ilert AI for incident communication, too. ilert's AI-powered assistance swiftly crafts precise incident-related messages. Upon the occurrence of an incident, simply provide a brief technical summary into the designated fields and let ilert's AI take over from there. It will generate a concise summary of the incident and craft a message for the status page, indicating which services have been impacted based on the information you've provided.
Furthermore, ilert's AI can also provide updates during the incident. Detailed status descriptions are no longer necessary; your team can focus solely on resolving the issue, confident that clear, user-friendly communications will be sent out to users.
Learn: Create Postmortems Automatically
Dedicating time to learning after IT incidents is paramount for organizational growth and resilience. This period allows for conducting thorough postmortems, a critical process where teams dissect what happened, identify the root causes of the incident and understand the effectiveness of the response. The primary importance of postmortems lies in their ability to transform incidents into learning opportunities. By meticulously analyzing each event, teams can uncover weaknesses in their systems and processes and develop actionable insights to prevent similar incidents from reoccurring. Furthermore, postmortems contribute to building a culture of transparency and continuous improvement, where mistakes are openly shared and used as stepping stones for enhancement rather than being stigmatized. This dedicated time for reflection and learning is not just about fixing what went wrong but about reinforcing an organization's resilience, paving the way for more robust and reliable IT operations in the future.
To cover this last incident management phase, we have introduced AI assistance in postmortem creation. Gather timeline data and communications from chat tools, like Slack and Microsoft Teams, alongside comments made in ilert, review all associated alerts and their specifics, and export a file for potential future enhancements — this is our postmortem feature in a nutshell.
What's next
Some more great AI news will come in the following months. As most of the AI features are in the public beta stage, we appreciate your feedback and ideas on improving them. Feel free to share your thoughts in the Roadmap or email us at support@ilert.com. Also, remember to join our webinar, where we will dive deeply into AI in the incident management realm.
ilert's integration catalog now includes a new addition for InfluxDB—an open-source time series database.
What is InfluxDB?
InfluxDB is an open-source time series database designed to handle high write and query loads in a time-efficient manner. It's specifically built to store and analyze time-stamped data, such as metrics and events, making it a critical tool for monitoring applications, Internet of Things applications, real-time analytics, and more. The database is distinguished by its high-performance data storage, easy scalability, and a straightforward query language called InfluxQL, which simplifies the process of working with time series data. InfluxDB supports a wide array of data types and offers features like data retention policies, continuous queries, and real-time alerts, making it a versatile choice for managing large volumes of time-sensitive data across various industries.
What can you expect from the ilert integration for InfluxDB?
This integration allows you to send alerts to the ilert incident management platform and notify team members through various channels, including push notifications, SMS, voice calls, and more. Alerts will be escalated until acknowledged. All notifications are actionable, enabling you to modify the alert status directly within the channel where you received it.
Linking InfluxDB with ilert platform equips your team with a comprehensive toolset for managing the entire incident lifecycle—from acknowledging alerts to conducting post-incident analysis with the support of AI-driven postmortems. Additionally, you'll have the capability to arrange on-call duties, inform your clients and stakeholders about critical issues through status pages, and leverage various ChatOps features for Slack and Microsoft Teams to resolve incidents.
Discover a step-by-step guide on establishing a connection between InfluxDB and ilert in our documentation.
The concept of observability has become a cornerstone for ensuring system reliability and efficiency in modern software engineering and operations. Observability, beyond its traditional scope of logging, monitoring, and tracing, can be intricately defined through the lens of incident response efficiency—specifically by examining the time it takes for teams to grasp the full context and background of a technical incident.
Optimizing Time to Understanding
This nuanced view introduces the critical metric of Time to Understanding (TTU), a dimension that serves as a pivotal link in the chain of incident management metrics, including Time to Acknowledge (TTA) and Time to Resolve (TTR). TTU emerges as a metric by quantifying the duration from when an alert is received to when a team fully comprehends the incident's scope, impact, and underlying causes. In the complex alerting context, TTU not only bridges the interval between initial alert acknowledgment (TTA) and the commencement of resolution efforts (TTR) but also plays a transformative role in refining alert management strategies. By optimizing TTU, organizations can significantly enhance their operational resilience, minimize downtime, and streamline their path to incident resolution. It is important to understand, however, that optimizing for TTU completely differs based on the underlying infrastructure and architecture of the maintained software product.
The Impact of Microservice Architectures on Time to Understanding
Software deployments often favor monolithic architectures due to their simplicity, where the application is built as a single, unified unit. This approach made understanding the system's functionality and debugging issues more straightforward, as all components operated within a single codebase and runtime environment. However, when development teams and application complexity grow, the limitations of monolithic architectures, such as scalability and deployment speed, push organizations towards microservices architectures. Microservices, which break down the application into smaller, independently deployable services, offer greater flexibility and scalability. Yet, this fragmentation introduces a chaotic nature to the system's understanding, as the interdependencies and interactions across numerous services can obscure the overall picture, making it challenging—almost impossible—for even large development teams to grasp the full extent of how everything works together.
Where OpenTelemetry Сomes into Play
Microservice architectures can significantly hinder observability, turning the management and troubleshooting of services into a daunting task. OpenTelemetry emerges as a design to address these challenges by providing a unified and standardized framework for collecting, processing, and shipping telemetry data (metrics, logs, and traces) from each microservice. By implementing OpenTelemetry, organizations can gain a comprehensive view of their microservice landscape, enabling them to track the flow of requests across service boundaries, understand the interactions and dependencies among disparate services, and identify performance bottlenecks or failure points with precision. This enhanced level of observability cuts through the chaotic nature of microservice architectures, facilitating a deeper understanding of system behaviors and operational dynamics.
The Three Pillars of OpenTelemetry
Metrics, the first pillar of OpenTelemetry, represent a crucial component for monitoring and understanding system performance at scale. They are designed to be lightweight and easy to store, even at high volume, making them ideal for capturing a high-level overview of system health and behavior over time. By aggregating numerical data points—such as request counts, error rates, and resource usage—metrics provide a simplified, yet comprehensive, snapshot of the operational state. However, this process of aggregation, while beneficial for scalability and manageability, can inadvertently obscure detailed information about infrequent or outlier events, concealing potential issues within the system.
Logs, while still considered "young" in the OpenTelemetry Protocol (OTLP) framework, play a critical role in diagnosing and understanding issues within microservices architectures. Logs offer a very detailed explanation of problems, capturing events in a structured or unstructured format that developers can analyze to pinpoint the root causes of issues. However, the utility of logs comes with its challenges; due to the potentially high volume of logs generated, especially in complex and distributed systems, their storage and management can become difficult. These challenges demand efficient log aggregation and management solutions to ensure that logs remain accessible and useful for troubleshooting without overwhelming the system's resources.
Tracing, the third pillar of OpenTelemetry, serves as a hybrid between metrics and logs, offering a uniquely rich and detailed view into the system's behavior by capturing very dense information, even more than traditional logs. A trace encapsulates the journey of a single request through the system, decomposed into multiple spans, where each span represents a distinct "unit of work" or operation within the service architecture. These spans collectively form a detailed timeline of the request's path, pinpointing where time is spent and where errors may occur. Despite the wealth of data traces provided, it's noteworthy that the vast majority (99.9%) of this data is never actively viewed, underscoring the selective nature of tracing data consumption.
The Heart of OpenTelemetry: The Collector
Next to the OpenTelemetry instruments, such as libraries and SDKs that help developers publish OTLP data, mentioned in the three pillars above, from within application code—the OpenTelemetry Collector marks the heart of the framework.
The Collector (OTelC) is not only a data exporter; it introduces advanced capabilities critical for managing telemetry data efficiently in distributed systems. It adeptly handles cardinality, an essential feature for maintaining data usability while preventing overwhelm in monitoring systems by reducing dimensionality where necessary. This flexibility allows OTelC instances to be chained, providing a scalable solution for preprocessing telemetry data—by filtering, sampling, and processing—before it reaches the backend. By intelligently managing what data is transmitted, including the removal of noisy, sensitive, or otherwise unnecessary information, OTelC ensures that only pertinent, high-quality data is forwarded, thereby optimizing performance and compliance.
Crucially, with OTelC positioned close to the data sources, it dramatically decreases the amount of traffic required to travel over the network, which is especially beneficial in cloud environments where data transfer costs can accumulate. This proximity allows for efficient traffic management and load reduction, ensuring high-volume telemetry data does not saturate network resources.
Moreover, OpenTelemetry instruments (SDKs, libraries) are relieved from the burden of traffic and load considerations, allowing developers to focus on instrumentation without worrying about the impact on data transfer volumes. With OTelC, managing OTLP cardinality and enhancing data efficiency becomes seamless, negating the need for invasive changes within the application code itself. Thus, code integrity is preserved while comprehensive observability is ensured.
The latter also fits well in a microservice environment where usually the dev teams themselves take care of deployment and runtime of their services and may use their own OTel collector pipelines to fine-tune their OTLP data streams without having to alter and redeploy their services.
What Standalone OpenTelemetry is Missing
While OpenTelemetry excels in collecting and exporting telemetry data in a distributed service environment, it does not include functionalities for storing this data; instead, it relies on external storage solutions to archive and manage the collected information.
Additionally, OpenTelemetry itself does not provide dashboards, which are required for visualizing data trends and insights. Instead, it requires integration with other tools to analyze and display the data.
Notifications, essential for alerting teams to system issues in real time, are also beyond the scope of OpenTelemetry's capabilities, necessitating supplementary alerting mechanisms.
Finally, OpenTelemetry does not natively support proactive testing of applications through simulated traffic or user interactions, an important aspect of understanding and ensuring system performance and reliability under various conditions.
Consequently, while OpenTelemetry is a powerful tool for observability, it must be complemented with additional systems and strategies to cover these critical areas fully.
A Top-of-the-line Observability Stack with OpenTelemetry and ilert
To address OpenTelemetry’s limitations and thereby create a top-tier observability stack, its capabilities can be significantly enhanced by integrating it with specialized tools.
For data storage and visually intuitive dashboards that aid in rapid data analysis and insights, Honeycomb.io complements OpenTelemetry by offering scalable, high-powered analytics. As well as incorporating a tool like Checkly, which specializes in proactive testing and validation of web services, closing the loop on comprehensive system monitoring. To amplify the effectiveness of alerting mechanisms, ilert can be integrated with Honeycomb and Checkly, ensuring notifications are timely, actionable, and can escalate through the correct channels such as voice calls or Microsoft Teams channel updates.
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