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

Why Traditional Observability Completely Fails for AI Workloads ## The Radical Idea: Observe AI Systems From the Kernel ## What Is eBPF (In One Precise Paragraph) ## Why Rust Is the Only Sane Choice Here ## Architecture Overview ## Step 1: Tracing AI Inference Without Touching Python ## Step 2: Detecting GPU Bottlenecks Indirectly (But Reliably) ## Step 3: AI-Specific Metrics You’ve Never Seen Before ## Step 4: Feeding the Data Into AI Observability ## Why This Changes Everything ## When You Should Not Use This ## The Future: Autonomous AI Debugging at Kernel Level ## Final Thought Production observability for AI systems is broken. We fixed it by moving below the application layer. Modern AI systems don’t behave like classical web services. Yet we still observe them using: ❌ 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: 👉 Your observability stops existing. Instead of instrumenting applications, we observe reality. eBPF (extended Berkeley Packet Filter) allows you to run sandboxed programs inside the Linux kernel, safely and dynamically, without kernel modules or reboots. This makes it perfect for AI observability. Writing kernel-adjacent code is dangerous. Async Rust in userland We attach eBPF programs to: No Python changes. No framework hooks. No SDK. We can’t run eBPF on the GPU. Inference latency spikes correlate strongly with kernel-level context switching density This is something no APM tool shows you. 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: We stream events via: Performance Impact (The Real Question) Method(Overhead) Traditional tracing(5–15%) Python profiling(10–30%) eBPF (ours)(< 1%) Measured under sustained GPU inference load. It’s observability that cannot lie. 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 Next steps we’re exploring: You can’t observe modern AI systems from the application layer anymore. Reality lives in the kernel. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: ┌─────────────┐ │ AI Service │ │ (Python) │ └──────┬──────┘ │ ▼ ┌───────────────────┐ │ Linux Kernel │ │ │ │ eBPF Programs │◄───── Tracepoints │ │ Kprobes └──────┬────────────┘ │ Ring Buffer ▼ ┌───────────────────┐ │ Rust Userland │ │ Collector │ └──────┬────────────┘ ▼ ┌───────────────────┐ │ AI Observability │ │ Pipeline │ └───────────────────┘ Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ┌─────────────┐ │ AI Service │ │ (Python) │ └──────┬──────┘ │ ▼ ┌───────────────────┐ │ Linux Kernel │ │ │ │ eBPF Programs │◄───── Tracepoints │ │ Kprobes └──────┬────────────┘ │ Ring Buffer ▼ ┌───────────────────┐ │ Rust Userland │ │ Collector │ └──────┬────────────┘ ▼ ┌───────────────────┐ │ AI Observability │ │ Pipeline │ └───────────────────┘ CODE_BLOCK: ┌─────────────┐ │ AI Service │ │ (Python) │ └──────┬──────┘ │ ▼ ┌───────────────────┐ │ Linux Kernel │ │ │ │ eBPF Programs │◄───── Tracepoints │ │ Kprobes └──────┬────────────┘ │ Ring Buffer ▼ ┌───────────────────┐ │ Rust Userland │ │ Collector │ └──────┬────────────┘ ▼ ┌───────────────────┐ │ AI Observability │ │ Pipeline │ └───────────────────┘ COMMAND_BLOCK: #[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 } Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: #[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 } COMMAND_BLOCK: #[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 } - Highly asynchronous - Framework-heavy (PyTorch, TensorRT, CUDA, ONNX) - Opaque once deployed - HTTP middleware - Language-level tracing - Application instrumentation This creates three fatal problems: - Kernel drivers - GPU scheduling - Memory allocations - Network traffic - GPU interactions - Thread scheduling And we do it using eBPF. - Runs at kernel-level - Zero userland instrumentation - Verified for safety - Extremely low overhead (~nanoseconds) - 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 - sys_enter_mmap - sys_enter_ioctl - sched_switch - tcp_sendmsg - Model load times - GPU driver calls - Thread contention - Network inference latency Example: eBPF Program (Rust) - CUDA driver syscalls - Memory pressure patterns - Context switches per inference We discovered a powerful signal: - Latency regressions - OOM crashes - GPU starvation before they happen - Ring buffers - OpenTelemetry exporters - Correlate kernel events with inference IDs - Build flamegraphs below the runtime - Detect anomalies using statistical baselines - Works for any language - Works for closed-source models - Works in production - Survives framework upgrades - Automatic root-cause detection - eBPF-powered AI guardrails - Self-healing inference pipelines - WASM-based policy engines