By Shahar Azulay, CEO and Co-Founder, groundcover

In 2025, observability will be transformed by the capabilities of eBPF (extended Berkeley Packet Filter), a technology that enables immediate, low-overhead visibility into system and application performance directly at the kernel level. This shift promises to redefine how organizations approach observability, making high-fidelity, real-time data collection more accessible and cost-effective. With eBPF's capacity for granular visibility into operating systems without altering application code, it will become a cornerstone technology in observability platforms, removing barriers traditionally associated with the implementation of observability solutions. This ease of use and high level of insight sets a new tone, paving the way for observability data that is simple to capture and leverage, irrespective of team size, skill level, or operational complexity.

The implications of eBPF-driven observability extend beyond mere accessibility. As the demand for effective observability grows in the face of increasingly complex, distributed architectures, the ability to access data instantly and from within the kernel layer itself provides unparalleled levels of detail. eBPF's lightweight nature makes it possible to run high-frequency, in-depth monitoring without introducing significant latency or overhead, enabling organizations to capture system-level events with pinpoint accuracy. This capability will reshape the expectations for observability tools, making high-resolution data capture and system transparency a norm rather than an exception.

Alongside eBPF, AI's integration into observability platforms will be the key to transforming raw data into meaningful insights. The synergy between eBPF and AI in observability is profound: while eBPF allows for high-speed, detailed data collection, AI provides the tools to interpret this data in ways that were previously time-consuming and labor-intensive. Leveraging machine learning models, observability platforms can streamline data processing, identifying patterns, predicting anomalies, and drawing connections across data sources in real time. As a result, AI-driven insights are expected to accelerate the transition from mere data collection to actionable intelligence, providing immediate value without the need for teams to sift through extensive logs or metrics.

This evolution in observability will bring about a new era of proactive, rather than reactive, monitoring. With AI's ability to detect subtle anomalies and recognize patterns, teams will be able to preemptively address issues before they escalate into user-impacting problems. Predictive maintenance, driven by AI insights, will allow for continuous improvement, minimizing system downtimes and enabling businesses to maintain optimal performance at all times. As a result, the role of observability will shift from a passive tool for post-mortem analysis to an active driver of system reliability and business continuity.

For organizations, these advancements mean that observability will no longer require specialized expertise or a heavy reliance on in-house resources. With eBPF providing seamless access to data and AI automating the process of insight generation, observability platforms will become increasingly accessible even to smaller teams and less technically specialized organizations. This democratization of observability aligns with a broader industry trend toward solutions that are both comprehensive and user-friendly, requiring minimal manual configuration or tuning. By reducing the technical barriers, eBPF and AI will open doors for organizations of all sizes to benefit from robust observability, empowering them to make data-informed decisions that directly impact both system performance and business outcomes.

As part of this observability revolution, the convergence of eBPF and AI will drive down the costs traditionally associated with observability. By reducing the need for extensive human oversight and tuning, as well as by automating processes such as anomaly detection, root-cause analysis, and trend identification, observability platforms can deliver value more efficiently and cost-effectively. Additionally, with eBPF reducing the need for extensive, redundant data collection, organizations will save on data storage and processing costs. This cost efficiency is critical as observability solutions can constitute a significant portion of infrastructure expenses. In 2025, observability platforms that harness eBPF and AI are likely to be viewed as essential investments, delivering returns in the form of optimized performance, reduced downtime, and improved end-user experiences.

In summary, the combination of eBPF and AI represents a transformative shift for observability. By making data collection seamless and scalable and by converting that data into insights autonomously, observability platforms in 2025 will become more accessible, proactive, and cost-effective. As eBPF eliminates the traditional technical complexities of data collection and AI translates data into actionable intelligence, organizations will be better positioned to proactively manage their systems and optimize their operations, ensuring they remain agile, resilient, and competitive in an increasingly complex digital landscape.

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ABOUT THE AUTHOR

Shahar Azulay, CEO and Co-Founder of groundcover, the company reinventing the cloud-native application performance monitoring domain with eBPF, is a serial R&D leader. Shahar brings experience in the world of cybersecurity and machine learning having worked as a leader in companies such as Apple, DayTwo, and Cymotive Technologies. Shahar spent many years in the Cyber division at the Israeli Prime Minister's Office and holds three degrees in Physics, Electrical Engineering and Computer Science from the Technion Israel Institute of Technology as well as Tel Aviv University. Shahar strives to use technological learnings from this rich background and bring it to today's cloud native battlefield in the sharpest, most innovative form to make the world of dev a better place.