eBPF research shows kernel tech powering modern AI

The eBPF Foundation has released new research that describes how eBPF is becoming a strategic layer in modern infrastructure across cloud, security, and AI workloads.

The report, titled "eBPF for the Infrastructure Platform", examines how kernel-level programmability now underpins a wide range of systems. It focuses on cloud native environments, on-premises data centres, and AI-driven platforms.

eBPF is a technology that runs custom programs inside the operating system kernel. These programs inspect and control events such as network traffic, system calls, and application behaviour.

The foundation says eBPF is now embedded in many products and services that infrastructure teams use every day. It features in hyperscale networking stacks, cloud security offerings, observability tools, and AI pipelines.

The new study describes how eBPF is reshaping categories such as observability, network security, runtime security, FinOps, large language model (LLM) observability, and virtualised networking. It states that teams at both large cloud providers and smaller enterprises increasingly standardise on eBPF as a common building block.

These teams use eBPF for deeper visibility into workloads. They also use it for performance optimisation, replacing older agents and in-kernel modules with more flexible mechanisms.

"eBPF has moved far beyond its early adopter phase," said Bill Mulligan, eBPF Foundation Governing Board Member from Isovalent. "Today, it underpins some of the most advanced infrastructure platforms in the world. This report highlights not only how far the technology has come, but also how its trajectory is reshaping security, networking, observability, and AI infrastructure across industries."

Replacing legacy tools

The report argues that eBPF is displacing traditional agents, sidecar proxies, and custom kernel modules in several domains. It says that eBPF programs run inside the kernel and incur less overhead than user-space agents in many scenarios.

These programs handle tasks such as packet filtering, traffic routing, and system monitoring. They can change behaviour without changes to kernel source code or system restarts.

According to the research, eBPF-based approaches are now common in cloud networking and microservices architectures. They also appear in security products that enforce policies at the kernel level.

The study highlights examples from companies such as Meta, Netflix, and Cloudflare. These companies were among the early adopters of eBPF for production workloads at large scale. The report uses these cases to illustrate patterns that now spread across the broader market.

AI and observability focus

The research devotes significant attention to AI and LLM workloads. It states that large compute clusters running training and inference jobs require detailed telemetry. This telemetry tracks latency, resource usage, and error conditions across many nodes.

eBPF programs can capture granular events at the kernel boundary. This allows observability systems to build a detailed view of AI workloads without extensive code changes in applications.

The report says this approach supports workload optimisation and resource efficiency for AI clusters. It links these outcomes to FinOps practices that focus on cost visibility and optimisation in cloud environments.

LLM observability is another focus area. The study notes that teams now instrument token generation, model serving, and network paths that support LLM APIs. eBPF-based telemetry feeds into dashboards and alerting systems.

Developer tooling trend

The report states that improvements in developer tooling are broadening the eBPF user base. Early eBPF development often required deep kernel knowledge. Today, teams can use software development kits in multiple languages, integrated development environment plug-ins, and debugging tools.

CO-RE, or Compile Once-Run Everywhere, is one of the approaches described. It allows developers to write eBPF programs that run on different kernel versions. This reduces deployment friction across heterogeneous infrastructure.

The study concludes that these tools reduce the barrier to entry for eBPF projects. It says adoption is now spreading beyond specialist kernel engineers into general infrastructure and platform teams.

Guidance for adopters

The foundation's research also includes practical steps for organisations that are assessing eBPF. It suggests that teams first evaluate open source projects that already use eBPF for networking, security, and observability.

The report names Cilium, Tetragon, and OpenTelemetry as examples of projects that incorporate eBPF. It also advises infrastructure leaders to review commercial distributions that include vendor support.

For some use cases, the study notes that organisations may write custom eBPF programs. These programs address specialised requirements or integrate with existing internal systems.

Platform layer shift

The report frames eBPF as a platform shift in infrastructure design. Earlier eBPF-based tools often focused on narrow, single-purpose solutions. Many of these emerged inside hyperscale technology companies.

The foundation now observes a move towards integrated platforms that use eBPF across functions. These platforms bring together networking, security, observability, and AI-aware telemetry into a single architecture. They adjust behaviour in response to changing application demands.