How We Used eBPF + Rust to Observe AI Systems Without Instrumenting a Single Line of Code

#devops #rust #observa #ai

Production observability for AI systems is broken.
We fixed it by moving below the application layer.

Why Traditional Observability Completely Fails for AI Workloads

Modern AI systems don’t behave like classical web services.

They are:

Highly asynchronous
GPU-bound
Framework-heavy (PyTorch, TensorRT, CUDA, ONNX)
Opaque once deployed

Yet we still observe them using:

HTTP middleware
Language-level tracing
Application instrumentation This creates three fatal problems:

❌ Problem 1: Instrumentation Bias

You only see what the developer remembered to instrument.

❌ Problem 2: Runtime Overhead

AI inference latency is measured in microseconds. Traditional tracing adds milliseconds.

❌ Problem 3: Blind Spots

Once execution crosses into:

CUDA
Kernel drivers
Syscalls
GPU scheduling

👉 Your observability stops existing.

The Radical Idea: Observe AI Systems From the Kernel

Instead of instrumenting applications, we observe reality.

That means:

Syscalls
Memory allocations
Network traffic
GPU interactions
Thread scheduling And we do it using eBPF.

What Is eBPF (In One Precise Paragraph)

eBPF (extended Berkeley Packet Filter) allows you to run sandboxed programs inside the Linux kernel, safely and dynamically, without kernel modules or reboots.

Key properties:

Runs at kernel-level
Zero userland instrumentation
Verified for safety
Extremely low overhead (~nanoseconds)

This makes it perfect for AI observability.

Why Rust Is the Only Sane Choice Here

Writing kernel-adjacent code is dangerous.

Rust gives us:

Memory safety
Zero-cost abstractions
Strong typing across kernel/user boundary
No GC pauses
We use:
aya for eBPF
no_std eBPF programs
Async Rust in userland

Architecture Overview

┌─────────────┐
│ AI Service  │
│ (Python)    │
└──────┬──────┘
       │
       ▼
┌───────────────────┐
│ Linux Kernel      │
│                   │
│  eBPF Programs    │◄───── Tracepoints
│                   │       Kprobes
└──────┬────────────┘
       │ Ring Buffer
       ▼
┌───────────────────┐
│ Rust Userland     │
│ Collector         │
└──────┬────────────┘
       ▼
┌───────────────────┐
│ AI Observability  │
│ Pipeline          │
└───────────────────┘

Step 1: Tracing AI Inference Without Touching Python

We attach eBPF programs to:

sys_enter_mmap
sys_enter_ioctl
sched_switch
tcp_sendmsg

This gives us:

Model load times
GPU driver calls
Thread contention
Network inference latency Example: eBPF Program (Rust)

#[kprobe(name = "trace_ioctl")]
pub fn trace_ioctl(ctx: ProbeContext) -> u32 {
    let pid = bpf_get_current_pid_tgid() >> 32;
    let cmd = ctx.arg::<u64>(1).unwrap_or(0);

    EVENT_QUEUE.output(&ctx, &IoctlEvent { pid, cmd }, 0);
    0
}

No Python changes.
No framework hooks.
No SDK.

Step 2: Detecting GPU Bottlenecks Indirectly (But Reliably)

We can’t run eBPF on the GPU.

But we can observe:

CUDA driver syscalls
Memory pressure patterns
Context switches per inference We discovered a powerful signal:

Inference latency spikes correlate strongly with kernel-level context switching density

This is something no APM tool shows you.

Step 3: AI-Specific Metrics You’ve Never Seen Before

Using kernel data, we derive new metrics:

🔬 Kernel-Derived AI Metrics

Inference syscall density(Model inefficiency)
GPU driver contention(Multi-model interference)
Memory map churn(Model reload bugs)
Thread migration rate(NUMA misconfiguration)

These metrics predict: