Once viewed as an advanced kernel technology reserved for hyperscale engineers, eBPF is now emerging as a foundational platform layer for modern infrastructure. That’s the core finding of a new research report released by the eBPF Foundation, which examines how kernel-level programmability is reshaping observability, security, networking, FinOps, and even AI and LLM operations across cloud-native and on-prem environments.

Based on interviews, industry analysis, and real-world use cases from companies including Meta, Netflix, and Cloudflare, the report argues that infrastructure teams are no longer treating eBPF as an experimental optimization tool. Instead, they are standardizing on eBPF as a strategic building block for visibility, performance, and system-level control.

From Specialized Tool to Infrastructure Standard

eBPF (extended Berkeley Packet Filter) allows programs to run safely inside the Linux kernel without requiring kernel changes. Early adoption centered on performance diagnostics and networking, particularly inside hyperscaler environments where deep system visibility was critical at scale.

The Foundation’s new research shows that this scope has widened dramatically. eBPF is now powering entire product categories across:

• Cloud and on-prem observability

• Network and runtime security

• FinOps and cost optimization

• Virtualized and cloud-native networking

• LLM and AI workload observability

Rather than layering agents, sidecars, and kernel modules on top of production systems, teams are increasingly using kernel-native eBPF programs to collect telemetry, enforce security policies, and optimize performance with lower overhead.

This shift matters because traditional infrastructure monitoring and security stacks were built for static systems. Today’s environments—driven by Kubernetes, ephemeral workloads, and distributed microservices—demand instrumentation that operates at the same speed and scale as the workloads themselves.

According to the report, eBPF’s ability to safely reprogram system behavior at runtime—without modifying kernel source code—has become a decisive advantage for infrastructure teams seeking both agility and stability.

eBPF’s Expanding Role in AI and LLM Operations

One of the most notable findings in the report is eBPF’s growing role in AI and large language model (LLM) infrastructure. As AI pipelines scale across massive GPU clusters, high-fidelity telemetry becomes essential for performance tuning, workload scheduling, and cost control.

The report highlights how eBPF is being used to:

• Capture low-level performance signals from GPUs, networking, and storage

• Improve inference efficiency and workload placement

• Enable real-time optimization in large-scale compute environments

This positions eBPF not just as an observability tool, but as a control layer for AI infrastructure—one that operates beneath traditional monitoring systems.

Bill Mulligan, a member of the eBPF Foundation’s governing board, describes the technology as having moved well beyond its early adopter phase. Today, he says, it underpins many of the most advanced infrastructure platforms in production and is actively reshaping how security, networking, observability, and AI systems are built.

Lowering the Barrier to Adoption

Historically, working with eBPF required deep kernel expertise. That barrier is now falling fast. The report points to a wave of developer-friendly tooling that is accelerating adoption across a broader audience of platform engineers and developers.

These advances include:

• SDKs in multiple programming languages

• CO-RE (Compile Once, Run Everywhere) frameworks

• IDE integrations and debugging tools

• Production-grade open source projects such as Cilium, Tetragon, and OpenTelemetry

Together, these tools are transforming eBPF from a specialist technology into a general-purpose infrastructure platform that can be adopted incrementally rather than all at once.

The report also offers practical guidance for organizations evaluating their first steps with eBPF—ranging from using community projects, to adopting commercial distributions, to building custom programs internally for differentiated use cases.

A Platform Shift for Modern Infrastructure

Perhaps the most significant conclusion of the research is this: eBPF is no longer solving isolated problems—it is becoming the connective tissue of modern infrastructure platforms.

Where early tools addressed narrow use cases at hyperscale, today’s eBPF-based platforms increasingly unify networking, security, observability, and AI-aware telemetry into integrated systems that adapt in real time to changing application behavior.

For enterprise teams navigating cloud-native complexity, this signals a broader platform transition—away from fragmented point tools and toward programmable, kernel-level infrastructure that is both more efficient and more responsive.

As Kubernetes, AI workloads, and distributed systems continue to raise the bar for performance and observability, the Foundation’s report makes a clear case: eBPF is fast becoming a core primitive of the modern infrastructure stack, not just a powerful optimization trick.