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

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:

• Latency regressions
• OOM crashes
• GPU starvation before they happen

Step 4: Feeding the Data Into AI Observability

We stream events via:

• Ring buffers
• Async Rust
• OpenTelemetry exporters

Then we:

• Correlate kernel events with inference IDs
• Build flamegraphs below the runtime
• Detect anomalies using statistical baselines

Performance Impact (The Real Question)
Method(Overhead)
Traditional tracing(5–15%)
Python profiling(10–30%)
eBPF (ours)(< 1%)

Measured under sustained GPU inference load.

Why This Changes Everything

This approach:

• Works for any language
• Works for closed-source models
• Works in production
• Survives framework upgrades

It’s observability that cannot lie.

When You Should Not Use This

Be honest in your dev.to post (this increases trust):

❌ If you don’t control the host
❌ If you’re on non-Linux systems
❌ If you need simple dashboards only

The Future: Autonomous AI Debugging at Kernel Level

Next steps we’re exploring:

• Automatic root-cause detection
• eBPF-powered AI guardrails
• Self-healing inference pipelines
• WASM-based policy engines

Final Thought

You can’t observe modern AI systems from the application layer anymore.
Reality lives in the kernel.