How eBPF Is Changing Observability

The Numbers That Made eBPF a Default, Not a Curiosity

A few data points that made the decision for most platform teams in the last two years:

• 80%+ of new CNCF-submitted observability projects since 2023 list eBPF as a primary collection mechanism -- Cilium graduated, Tetragon and Pixie went mainstream, Parca and Pyroscope built whole-fleet continuous profilers on it. If you started shopping for an observability tool in 2025, eBPF was almost unavoidable in the shortlist.
• Netflix, Meta, and Google run eBPF on every production host. Netflix's FlowLogs, Meta's Katran load balancer, and Google's internal network telemetry all sit on eBPF hooks -- not as experiments, as the load-bearing collection layer for millions of machines.
• 40-60% lower latency overhead than sidecar proxies for L7 service-mesh observability, per Cilium's published benchmarks replicated by Isovalent customers. On high-QPS services, swapping an Envoy sidecar for eBPF-backed Hubble typically reclaims 5-15% CPU per pod.
• 1-3% CPU for always-on network flow monitoring at line rate. That is roughly the overhead of having your observability on, not off. Traditional packet capture with tcpdump on a hot interface routinely costs 10x that and drops packets under load.
• Every Linux distribution shipped in the last four years has the verifier and JIT enabled by default. EBPF is not a kernel patch you have to argue for -- it is the same piece of infrastructure that already filters every packet through the Linux bridge your containers sit on.

Put together, those numbers describe a technology that stopped being optional. If you are building an observability stack in 2026 and ignoring eBPF, you are leaving a category of data -- network flows, syscall-level traces, kernel latency, runtime security events -- either uncollected or collected the expensive way. This guide is about what eBPF actually buys you, which tools are production-ready vs still maturing, and where it still doesn't replace what OpenTelemetry does at the application layer.

A Thirty-Second Recap of What eBPF Is

eBPF (extended Berkeley Packet Filter) lets you run small, sandboxed programs inside the Linux kernel without patching kernel source or loading a kernel module. You compile restricted C to eBPF bytecode, the kernel's verifier statically proves it cannot crash, loop forever, or read random memory, and then the JIT compiles it to native machine code. The program attaches to a hook -- a kprobe, tracepoint, XDP ingress point, cgroup hook, or socket filter -- and runs there every time the kernel hits that hook. It reads and aggregates data into BPF maps (hash tables, ring buffers, per-CPU arrays) that user-space tools read without stopping the program.

The original BPF from 1992 was a packet-filter DSL for tcpdump. eBPF turned it into a general-purpose in-kernel virtual machine. Think of it as a safe extension language for the kernel -- the same way V8 lets browsers run untrusted JavaScript -- and the rest of this article follows naturally.

How eBPF Works

Understanding eBPF's architecture explains both its power and its constraints.

The eBPF Execution Pipeline

1. Write the program. eBPF programs are typically written in restricted C and compiled to eBPF bytecode using LLVM/Clang. Higher-level frameworks like libbpf, BCC, and bpftrace abstract this.
2. Verify. The kernel's eBPF verifier statically analyzes the bytecode to guarantee safety: no infinite loops, no out-of-bounds memory access, no unsafe kernel pointer dereferences. If verification fails, the program is rejected.
3. JIT compile. Verified bytecode is compiled to native machine code (x86, ARM) for near-native execution speed.
4. Attach to a hook. The program attaches to a kernel hook point: kprobes (kernel function entry/exit), tracepoints (predefined stable hooks), XDP (network packet ingress), cgroup hooks, and more.
5. Communicate via BPF maps. eBPF programs share data with user-space applications through BPF maps -- key-value stores that live in kernel memory. Maps can be hash tables, arrays, ring buffers, or per-CPU structures.

// Simplified eBPF program: count TCP connections by destination port
#include 
#include 

struct {
    __uint(type, BPF_MAP_TYPE_HASH);
    __uint(max_entries, 1024);
    __type(key, __u16);    // destination port
    __type(value, __u64);  // connection count
} tcp_connections SEC(".maps");

SEC("kprobe/tcp_v4_connect")
int trace_connect(struct pt_regs *ctx) {
    struct sock *sk = (struct sock *)PT_REGS_PARM1(ctx);
    __u16 dport;
    bpf_probe_read_kernel(&dport, sizeof(dport), &sk->__sk_common.skc_dport);
    dport = __builtin_bswap16(dport);

    __u64 *count = bpf_map_lookup_elem(&tcp_connections, &dport);
    if (count) {
        __sync_fetch_and_add(count, 1);
    } else {
        __u64 init = 1;
        bpf_map_update_elem(&tcp_connections, &dport, &init, BPF_ANY);
    }
    return 0;
}

Watch out: eBPF programs run in kernel context. While the verifier prevents crashes, poorly written programs can still cause performance degradation if they execute on a hot path (like every network packet). Always benchmark eBPF programs under production-like load before deploying.

eBPF for Observability: The Major Tools

You don't need to write raw eBPF programs to benefit from this technology. Several mature projects wrap eBPF into user-friendly observability tools.

Cilium: Network Observability

Cilium is a CNCF graduated project that uses eBPF for Kubernetes networking, security, and observability. Its observability component, Hubble, gives you:

• Layer 3/4 and Layer 7 network flow visibility without sidecars
• DNS query monitoring (who is resolving what, and how long it takes)
• HTTP, gRPC, and Kafka protocol-aware metrics
• Network policy enforcement and audit logging
• Service-to-service latency maps without any application instrumentation

# Install Cilium with Hubble observability on Kubernetes
cilium install --set hubble.enabled=true \
  --set hubble.relay.enabled=true \
  --set hubble.ui.enabled=true

# View real-time network flows
hubble observe --namespace production --protocol http

# Get HTTP status code breakdown per service
hubble observe --namespace production --protocol http -o json | \
  jq '{src: .source.labels["app"], dst: .destination.labels["app"], status: .l7.http.code}'

The key advantage over traditional service meshes (like Istio with Envoy sidecars) is performance. eBPF operates at the kernel level, so there's no proxy hop. Cilium claims 40-60% lower latency overhead compared to sidecar-based approaches for L7 observability.

Pixie: Auto-Telemetry and Profiling

Pixie (now part of New Relic) uses eBPF to capture full-body HTTP requests and responses, database queries, and application profiles -- all without code changes. It stores telemetry data locally on each node (no data leaves the cluster by default), which is a strong selling point for security-conscious teams.

• Auto-instrumented HTTP, MySQL, PostgreSQL, Cassandra, Redis, and Kafka traces
• Continuous CPU profiling with flamegraphs
• Full request/response body capture for debugging
• PxL scripting language for custom queries

# PxL script: show slowest HTTP endpoints
import px

df = px.DataFrame('http_events', start_time='-5m')
df = df[df['resp_status'] >= 200]
df.latency_ms = df['resp_latency'] / 1000000  # ns to ms
df = df.groupby(['req_path', 'req_method']).agg(
    p99_latency=('latency_ms', px.quantiles, 0.99),
    count=('latency_ms', px.count),
    error_rate=('resp_status', lambda x: px.mean(x >= 500)),
)
df = df[df['count'] > 10]
px.display(df.sort('p99_latency', ascending=False).head(20))

Falco: Security Observability

Falco, a CNCF incubating project, uses eBPF to detect anomalous behavior at runtime: unexpected process execution, unusual network connections, file access violations, privilege escalation attempts. It's observability focused on security rather than performance.

# Falco rule: detect shell spawned in a container
- rule: Terminal shell in container
  desc: Detect a shell being spawned in a container
  condition: >
    spawned_process and container and
    proc.name in (bash, sh, zsh, dash) and
    not proc.pname in (cron, supervisord)
  output: >
    Shell spawned in container
    (user=%user.name container=%container.name shell=%proc.name
     parent=%proc.pname image=%container.image.repository)
  priority: WARNING
  tags: [container, shell, mitre_execution]

bpftrace: Ad-Hoc Kernel Tracing

Bpftrace is the "awk for eBPF" -- a high-level tracing language for quick, one-liner investigations. It's not a production observability tool, but it's invaluable for debugging specific kernel-level issues.

# Count system calls by process
bpftrace -e 'tracepoint:raw_syscalls:sys_enter { @[comm] = count(); }'

# Histogram of read() latencies
bpftrace -e 'kprobe:vfs_read { @start[tid] = nsecs; }
  kretprobe:vfs_read /@start[tid]/ {
    @us = hist((nsecs - @start[tid]) / 1000);
    delete(@start[tid]);
  }'

# Trace DNS queries
bpftrace -e 'tracepoint:net:net_dev_xmit /args->len > 0/ { @bytes[comm] = sum(args->len); }'

Mature vs. Evolving Areas

EBPF observability isn't uniformly mature. Here's an honest assessment:

Area	Maturity	Key Tools	Notes
Network observability	Production-ready	Cilium/Hubble	L3/4 and L7 flow visibility is solid. Used by major cloud providers.
Security monitoring	Production-ready	Falco, Tetragon	Runtime threat detection is well-established.
Continuous profiling	Maturing	Pixie, Pyroscope, Parca	CPU profiling works well. Memory and off-CPU profiling are improving.
Application tracing	Maturing	Pixie, Odigos	Auto-instrumented traces from eBPF work for common protocols but lack the context depth of SDK-based tracing.
Kernel debugging	Production-ready	bpftrace, BCC	The go-to toolset for kernel and system-level debugging.
Log collection	Early	Experimental projects	eBPF-based log collection is not a significant use case yet.

Pro tip: eBPF-based tracing and OTel-based tracing are complementary, not competing. Use eBPF for infrastructure-level visibility (network flows, system calls, kernel events) and OTel for application-level visibility (business logic, custom spans, cross-service context). Together they cover the full stack.

Skills Required for eBPF Observability

Using eBPF tools like Cilium or Falco doesn't require deep eBPF knowledge -- they abstract it away. But building custom eBPF programs or debugging eBPF-based tools requires specific skills:

1. Linux kernel internals. Understanding system calls, the network stack, process scheduling, and memory management is essential. You don't need to read kernel source daily, but you need to know the major subsystems.
2. C programming. eBPF programs are written in restricted C. You need to be comfortable with pointers, structures, and low-level memory operations.
3. BPF toolchain. libbpf, CO-RE (Compile Once, Run Everywhere), BTF (BPF Type Format), and the verifier each have their own quirks and error messages.
4. Performance analysis. Brendan Gregg's work on performance analysis methodologies (USE method, flame graphs, latency heat maps) is foundational. His "BPF Performance Tools" book is the definitive reference.

For most teams, the right approach is to deploy packaged eBPF tools (Cilium, Falco, Pixie) and learn bpftrace for ad-hoc debugging. Writing custom eBPF programs is a specialist skill that most organizations only need for edge cases.

eBPF Tool and Platform Costs

Tool	License	Monthly Cost	Notes
Cilium + Hubble	Apache 2.0	$0 + infrastructure	Free. Isovalent (now Cisco) offers enterprise support.
Falco	Apache 2.0	$0 + infrastructure	Free. Sysdig offers managed Falco and additional features.
Pixie	Apache 2.0	$0 self-hosted	Now part of New Relic. Self-hosted is free. Cloud version included with New Relic.
Isovalent Enterprise	Commercial	Contact sales	Enterprise Cilium with Tetragon, advanced network policies, and support.
Groundcover	Commercial	$20-30/node	eBPF-based APM. Full traces, metrics, logs without code changes.
Odigos	Open source + Enterprise	$0 open source	Auto-instruments applications with eBPF and generates OTel traces.

The open-source eBPF ecosystem is strong. For most teams, Cilium + Falco covers network and security observability for free. Add Pixie or Groundcover if you want application-level auto-instrumentation without touching code.

Frequently Asked Questions

Conclusion

EBPF is the most significant shift in observability technology since distributed tracing. It enables telemetry collection at the kernel level with near-zero overhead and zero application code changes. For network observability and security monitoring, eBPF tools like Cilium and Falco are production-ready today. For application tracing and profiling, the technology is maturing rapidly.

The practical advice: start with Cilium for network visibility in Kubernetes. Add Falco for runtime security monitoring. Use bpftrace for ad-hoc debugging when you need to understand kernel-level behavior. And don't view eBPF as a replacement for OpenTelemetry -- view it as the missing layer that OTel doesn't cover. Together, they give you observability from kernel syscalls to business transactions, covering the entire stack.