eBPF for Kubernetes: The Complete Guide to Sidecar-less Observability in 2025
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eBPF for Kubernetes: The Complete Guide to Sidecar-less Observability in 2025
The observability landscape in Kubernetes is undergoing a revolutionary transformation. eBPF (extended Berkeley Packet Filter) is leading this change by enabling unprecedented visibility into system behavior without the traditional overhead of sidecar containers. This comprehensive guide explores how eBPF is reshaping Kubernetes monitoring, delivering performance gains of up to 80% while providing deeper insights than ever before.
Table of Contents
- Understanding eBPF: The Game-Changer for Observability
- Traditional Sidecar vs eBPF: A Performance Comparison
- eBPF Architecture in Kubernetes
- Implementing eBPF Observability with Pixie
- Network Observability with Hubble and Cilium
- Security Monitoring with Falco
- Performance Benefits and Resource Savings
- Production Deployment Strategies
- Troubleshooting and Best Practices
- Future of eBPF in Cloud Native
Understanding eBPF: The Game-Changer for Observability
eBPF represents a paradigm shift in how we observe and monitor systems. At its core, eBPF allows you to run sandboxed programs directly in the Linux kernel without changing kernel source code or loading kernel modules.
What Makes eBPF Revolutionary?
Traditional monitoring approaches require agents running in user space, intercepting and processing data with significant overhead. eBPF programs run at the kernel level, providing:
- Zero-overhead instrumentation: Direct kernel access without context switching
- Complete system visibility: Access to all system calls, network packets, and kernel functions
- Safety guaranteed: Built-in verifier ensures programs cannot crash the kernel
- Dynamic instrumentation: Add or remove monitoring without restarts
How eBPF Works
┌─────────────────────────────────────────────────────────────┐
│ User Space │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Application │ │ eBPF Tool │ │ Monitoring │ │
│ │ (Pod) │ │ (Pixie) │ │ Dashboard │ │
│ └─────────────┘ └──────┬──────┘ └─────────────┘ │
│ │ │
├──────────────────────────┼──────────────────────────────────┤
│ Kernel Space │
│ │ │
│ ┌───────────────────────▼───────────────────────┐ │
│ │ eBPF Virtual Machine │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │
│ │ │ Verifier│ │ JIT │ │ Maps │ │ │
│ │ └─────────┘ └─────────┘ └─────────┘ │ │
│ └───────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Kernel Hooks & Attach Points │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │
│ │ │ Kprobes │ │Tracepoints│ │ XDP │ │ │
│ │ └─────────┘ └─────────┘ └─────────┘ │ │
│ └─────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Traditional Sidecar vs eBPF: A Performance Comparison
The Sidecar Pattern: Benefits and Limitations
The sidecar pattern has been the standard for Kubernetes observability:
# Traditional sidecar deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-with-sidecar
spec:
template:
spec:
containers:
- name: app
image: myapp:latest
resources:
requests:
memory: "512Mi"
cpu: "500m"
- name: monitoring-sidecar
image: monitoring-agent:latest
resources:
requests:
memory: "256Mi" # Additional overhead
cpu: "250m" # Additional overhead
Sidecar Limitations:
- Resource overhead: Each pod requires additional CPU and memory
- Network latency: Inter-container communication adds delays
- Deployment complexity: Managing multiple containers per pod
- Limited visibility: Only sees what passes through the sidecar
eBPF Approach: Lightweight and Powerful
With eBPF, monitoring happens at the kernel level:
# eBPF-based monitoring (deployed once per node)
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: ebpf-agent
namespace: observability
spec:
template:
spec:
hostNetwork: true
hostPID: true
containers:
- name: ebpf-agent
image: ebpf-monitor:latest
securityContext:
privileged: true
resources:
requests:
memory: "100Mi" # Minimal overhead per node
cpu: "100m" # Not per pod!
Performance Comparison Metrics
interface PerformanceMetrics {
resourceOverhead: {
sidecar: {
cpuPerPod: "200-500m",
memoryPerPod: "256-512Mi",
totalFor100Pods: {
cpu: "20-50 cores",
memory: "25-50Gi"
}
},
eBPF: {
cpuPerNode: "100-200m",
memoryPerNode: "100-200Mi",
totalFor10Nodes: {
cpu: "1-2 cores",
memory: "1-2Gi"
}
}
},
latency: {
sidecar: "1-5ms per request",
eBPF: "<0.1ms (kernel-level)"
},
dataCompleteness: {
sidecar: "Application layer only",
eBPF: "Full stack: kernel to application"
}
}
eBPF Architecture in Kubernetes
Core Components
┌─────────────────────────────────────────────────────────────┐
│ Kubernetes Cluster │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Master Node │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌────────────┐ │ │
│ │ │ API Server │ │ Controller │ │ Scheduler │ │ │
│ │ └─────────────┘ └─────────────┘ └────────────┘ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Worker Node 1 │ │
│ │ ┌─────────────────────────────────────────────┐ │ │
│ │ │ eBPF Programs │ │ │
│ │ │ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │
│ │ │ │Network │ │Process │ │Storage │ │ │ │
│ │ │ │Monitor │ │Trace │ │I/O │ │ │ │
│ │ │ └────────┘ └────────┘ └────────┘ │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │
│ │ │ Pod 1 │ │ Pod 2 │ │ Pod 3 │ │ │
│ │ └────────────┘ └────────────┘ └────────────┘ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Worker Node 2 │ │
│ │ (Same structure) │ │
│ └─────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
eBPF Program Types for Kubernetes
// Example eBPF program for tracking HTTP requests
//go:build ignore
#include <linux/bpf.h>
#include <linux/ptrace.h>
#include <linux/tcp.h>
struct http_request_t {
u32 pid;
u32 tid;
u64 timestamp;
char method[8];
char path[128];
u16 status_code;
u64 latency_ns;
};
BPF_HASH(requests, u64, struct http_request_t);
BPF_PERF_OUTPUT(events);
int trace_http_request(struct pt_regs *ctx) {
u64 id = bpf_get_current_pid_tgid();
u32 pid = id >> 32;
u32 tid = id;
struct http_request_t req = {};
req.pid = pid;
req.tid = tid;
req.timestamp = bpf_ktime_get_ns();
// Extract HTTP method and path from syscall arguments
bpf_probe_read_user_str(&req.method, sizeof(req.method),
(void *)PT_REGS_PARM2(ctx));
bpf_probe_read_user_str(&req.path, sizeof(req.path),
(void *)PT_REGS_PARM3(ctx));
requests.update(&id, &req);
return 0;
}
Implementing eBPF Observability with Pixie
Installing Pixie in Your Cluster
# Install Pixie CLI
curl -fsSL https://withpixie.ai/install.sh | bash
# Authenticate (create account at withpixie.ai)
px auth login
# Deploy Pixie to your cluster
px deploy
# Verify installation
kubectl get pods -n px-operator
Pixie PxL Scripts for Deep Insights
# HTTP request latency monitoring with Pixie
import px
def http_latency_by_service():
df = px.DataFrame(table='http_events', start_time='-5m')
# Filter and aggregate
df = df[['service', 'latency_ns', 'status_code']]
df.latency_ms = df.latency_ns / 1000000
# Calculate percentiles
stats = df.groupby('service').agg({
'latency_ms': ['p50', 'p95', 'p99'],
'status_code': 'count'
})
# Add error rate
errors = df[df.status_code >= 400].groupby('service').agg({
'status_code': 'count'
})
stats['error_rate'] = errors['count'] / stats['count'] * 100
return stats
# CPU flame graphs for performance analysis
def cpu_flamegraph():
df = px.DataFrame(table='stack_traces', start_time='-1m')
# Filter by namespace
df = df[df.namespace == 'production']
# Generate flamegraph data
return df.groupby(['stack', 'container']).agg({
'count': 'sum',
'cpu_cycles': 'sum'
})
Real-world Pixie Use Cases
# Service performance dashboard
apiVersion: v1
kind: ConfigMap
metadata:
name: pixie-scripts
namespace: px-operator
data:
service-golden-signals.pxl: |
import px
def golden_signals():
# Get HTTP traffic data
df = px.DataFrame('http_events', start_time='-5m')
# Traffic (requests per second)
traffic = df.groupby(['service', 'timestamp']).agg({
'latency_ns': 'count'
})
# Latency percentiles
latency = df.groupby('service').agg({
'latency_ns': ['p50', 'p95', 'p99']
})
# Error rate
errors = df[df.status_code >= 400]
error_rate = errors.groupby('service').agg({
'status_code': 'count'
}) / df.groupby('service').agg({'status_code': 'count'})
# Saturation (CPU and memory)
resources = px.DataFrame('process_stats', start_time='-5m')
saturation = resources.groupby('service').agg({
'cpu_usage': 'mean',
'memory_usage': 'mean'
})
return {
'traffic': traffic,
'latency': latency,
'errors': error_rate,
'saturation': saturation
}
Network Observability with Hubble and Cilium
Installing Cilium with Hubble
# Install Cilium CLI
curl -L --remote-name-all https://github.com/cilium/cilium-cli/releases/latest/download/cilium-linux-amd64.tar.gz
sudo tar xzvfC cilium-linux-amd64.tar.gz /usr/local/bin
# Install Cilium with Hubble enabled
cilium install --set hubble.enabled=true --set hubble.ui.enabled=true
# Expose Hubble UI
kubectl port-forward -n kube-system svc/hubble-ui 12000:80
# Enable Hubble metrics
cilium hubble enable --ui
Network Policy Observability
# Network policy with observability annotations
apiVersion: cilium.io/v2
kind: CiliumNetworkPolicy
metadata:
name: api-allow-frontend
namespace: production
spec:
endpointSelector:
matchLabels:
app: api
ingress:
- fromEndpoints:
- matchLabels:
app: frontend
toPorts:
- ports:
- port: "8080"
protocol: TCP
# Enable detailed flow logging
enableLogging: true
labels:
visibility: "flow"
Hubble Flow Analysis
# Real-time flow monitoring
hubble observe --follow --namespace production
# Filter by verdict (dropped packets)
hubble observe --verdict DROPPED --namespace production
# Export flows for analysis
hubble observe --output json | jq '.flow | select(.verdict == "DROPPED")'
# Generate service dependency map
hubble observe --protocol tcp --print-node-name | \
jq -r '.flow | "\(.source.identity) -> \(.destination.identity)"' | \
sort | uniq -c
Advanced Network Metrics
# Hubble metrics configuration
apiVersion: v1
kind: ConfigMap
metadata:
name: hubble-metrics
namespace: kube-system
data:
metrics.yaml: |
metrics:
- name: dns
options:
- query
- ignoreAAAA
- name: drop
options:
- reason
- direction
- name: tcp
options:
- flags
- name: flow
options:
- sourceContext: pod
- destinationContext: pod
- name: http
options:
- method
- path
- status
Security Monitoring with Falco
Installing Falco with eBPF Probe
# Add Falco Helm repository
helm repo add falcosecurity https://falcosecurity.github.io/charts
helm repo update
# Install Falco with eBPF driver
helm install falco falcosecurity/falco \
--namespace falco --create-namespace \
--set ebpf.enabled=true \
--set falco.grpc.enabled=true \
--set falco.grpcOutput.enabled=true
Custom Security Rules
# Custom Falco rules for Kubernetes
- rule: Suspicious kubectl exec
desc: Detect kubectl exec commands
condition: >
spawned_process and container and proc.name = "kubectl" and
proc.args contains "exec"
output: >
Kubectl exec detected (user=%user.name command=%proc.cmdline
container=%container.id pod=%k8s.pod.name)
priority: WARNING
tags: [container, shell, mitre_execution]
- rule: Container Drift Detection
desc: Detect new processes not in original image
condition: >
spawned_process and container and
not proc.pname in (init, systemd, supervisor) and
proc.name != proc.pname
output: >
New process detected in container (proc=%proc.name parent=%proc.pname
container=%container.id image=%container.image.repository)
priority: NOTICE
tags: [container, process, drift]
- rule: Crypto Mining Detection
desc: Detect crypto mining activity
condition: >
spawned_process and container and
(proc.name in (crypto_miners) or
proc.cmdline contains "stratum+tcp" or
proc.cmdline contains "mining.pool")
output: >
Crypto mining detected (proc=%proc.name cmdline=%proc.cmdline
container=%container.id cpu=%proc.cpu)
priority: CRITICAL
tags: [cryptomining, resource_abuse]
Falco Response Actions
// Falco response webhook handler
package main
import (
"encoding/json"
"fmt"
"net/http"
"k8s.io/client-go/kubernetes"
)
type FalcoPayload struct {
Output string `json:"output"`
Priority string `json:"priority"`
Rule string `json:"rule"`
Time string `json:"time"`
OutputFields map[string]string `json:"output_fields"`
}
func handleFalcoAlert(w http.ResponseWriter, r *http.Request) {
var payload FalcoPayload
json.NewDecoder(r.Body).Decode(&payload)
switch payload.Rule {
case "Suspicious kubectl exec":
// Terminate the pod
deletePod(payload.OutputFields["k8s.pod.name"],
payload.OutputFields["k8s.ns.name"])
case "Crypto Mining Detection":
// Kill the process and alert
killProcess(payload.OutputFields["container.id"],
payload.OutputFields["proc.pid"])
sendAlert("Critical: Crypto mining detected", payload)
}
w.WriteHeader(http.StatusOK)
}
Performance Benefits and Resource Savings
Real-world Metrics Comparison
interface ObservabilityMetrics {
deployment: string;
podsMonitored: number;
method: 'sidecar' | 'eBPF';
resources: {
totalCPU: string;
totalMemory: string;
cpuPerPod: string;
memoryPerPod: string;
};
performance: {
ingestRate: string;
queryLatency: string;
dataGranularity: string;
};
}
const productionComparison: ObservabilityMetrics[] = [
{
deployment: "E-commerce Platform",
podsMonitored: 500,
method: "sidecar",
resources: {
totalCPU: "100 cores",
totalMemory: "200Gi",
cpuPerPod: "200m",
memoryPerPod: "400Mi"
},
performance: {
ingestRate: "50K events/sec",
queryLatency: "500ms",
dataGranularity: "Application metrics only"
}
},
{
deployment: "E-commerce Platform",
podsMonitored: 500,
method: "eBPF",
resources: {
totalCPU: "5 cores",
totalMemory: "10Gi",
cpuPerPod: "0m (node-level)",
memoryPerPod: "0Mi (node-level)"
},
performance: {
ingestRate: "500K events/sec",
queryLatency: "50ms",
dataGranularity: "Full-stack: kernel to app"
}
}
];
// Cost savings calculation
const calculateSavings = (sidecar: ObservabilityMetrics, ebpf: ObservabilityMetrics) => {
const cpuSavings = 95; // 95% reduction in CPU
const memorySavings = 95; // 95% reduction in memory
const costPerCPU = 0.048; // $/hour
const costPerGBMemory = 0.004; // $/hour
const monthlySavings = {
cpu: (100 - 5) * costPerCPU * 24 * 30,
memory: (200 - 10) * costPerGBMemory * 24 * 30,
total: 0
};
monthlySavings.total = monthlySavings.cpu + monthlySavings.memory;
return {
monthly: `$${monthlySavings.total.toFixed(2)}`,
yearly: `$${(monthlySavings.total * 12).toFixed(2)}`,
percentSaved: `${cpuSavings}%`
};
};
Performance Benchmark Results
┌─────────────────────────────────────────────────────────────┐
│ Performance Comparison │
├─────────────────────────────────────────────────────────────┤
│ Metric │ Sidecar │ eBPF │
├─────────────────────┼─────────────────┼──────────────────│
│ CPU Overhead │ 200m/pod │ 100m/node │
│ Memory Overhead │ 400Mi/pod │ 200Mi/node │
│ Network Latency │ +2-5ms │ <0.1ms │
│ Startup Time │ 30-60s │ <5s │
│ Data Collection │ App layer │ Full stack │
│ Kernel Events │ ❌ │ ✅ │
│ Zero-day Detection │ Limited │ Comprehensive │
│ Resource Scaling │ Linear (O(n)) │ Constant (O(1)) │
└─────────────────────────────────────────────────────────────┘
Production Deployment Strategies
Phased Rollout Plan
# Phase 1: Deploy eBPF monitoring alongside existing solution
apiVersion: v1
kind: Namespace
metadata:
name: ebpf-observability
---
# Phase 2: Deploy eBPF agents with limited scope
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: ebpf-agent
namespace: ebpf-observability
spec:
template:
spec:
nodeSelector:
ebpf-enabled: "true" # Start with labeled nodes
containers:
- name: ebpf-agent
image: ebpf-monitor:v1.0.0
env:
- name: MONITOR_NAMESPACES
value: "staging,development" # Start with non-prod
- name: METRICS_ENABLED
value: "true"
- name: TRACING_ENABLED
value: "false" # Enable gradually
Gradual Migration Strategy
interface MigrationPhase {
phase: number;
duration: string;
scope: string[];
validationCriteria: string[];
rollbackTriggers: string[];
}
const migrationPlan: MigrationPhase[] = [
{
phase: 1,
duration: "2 weeks",
scope: ["Development environment", "Non-critical services"],
validationCriteria: [
"eBPF agents stable for 48 hours",
"No kernel panics or system instability",
"Metrics parity with existing solution"
],
rollbackTriggers: [
"System instability",
"Missing critical metrics",
"Performance degradation"
]
},
{
phase: 2,
duration: "3 weeks",
scope: ["Staging environment", "Canary production services"],
validationCriteria: [
"Full metrics coverage",
"Alert parity achieved",
"Dashboard migration complete"
],
rollbackTriggers: [
"Alert failures",
"Data loss",
"Integration issues"
]
},
{
phase: 3,
duration: "4 weeks",
scope: ["All production services"],
validationCriteria: [
"Complete sidecar removal",
"Cost savings realized",
"Performance improvements measured"
],
rollbackTriggers: [
"Critical monitoring gaps",
"Compliance issues"
]
}
];
High Availability Configuration
# eBPF monitoring with HA and redundancy
apiVersion: v1
kind: ConfigMap
metadata:
name: ebpf-ha-config
namespace: ebpf-observability
data:
config.yaml: |
high_availability:
enabled: true
replication_factor: 3
data_persistence:
enabled: true
retention_days: 30
storage_class: fast-ssd
resource_limits:
max_memory_per_map: 512Mi
max_cpu_per_program: 100m
max_programs_per_node: 100
failover:
enabled: true
health_check_interval: 10s
failover_threshold: 3
security:
verify_programs: true
sign_programs: true
audit_logging: true
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: ebpf-agent-pdb
namespace: ebpf-observability
spec:
minAvailable: 80%
selector:
matchLabels:
app: ebpf-agent
Troubleshooting and Best Practices
Common Issues and Solutions
# Troubleshooting eBPF deployment
# 1. Check kernel compatibility
uname -r # Should be 4.9+ for basic eBPF, 5.2+ for advanced features
# 2. Verify eBPF support
ls /sys/kernel/debug/tracing/events/
# 3. Check BPF program loading
bpftool prog list
# 4. Monitor eBPF maps
bpftool map list
# 5. Debug program verification failures
dmesg | grep -i bpf
# 6. Check resource usage
cat /proc/sys/kernel/bpf_jit_enable # Should be 1
cat /proc/sys/net/core/bpf_jit_limit
Best Practices Checklist
apiVersion: v1
kind: ConfigMap
metadata:
name: ebpf-best-practices
namespace: ebpf-observability
data:
checklist.yaml: |
deployment:
- Use specific kernel version requirements
- Enable BPF JIT compilation
- Set appropriate resource limits
- Use signed eBPF programs
monitoring:
- Monitor eBPF program performance
- Track map usage and size
- Alert on verification failures
- Log all program loads/unloads
security:
- Run with minimal privileges
- Use LSM hooks for security policies
- Audit all eBPF operations
- Implement program signing
performance:
- Optimize map sizes
- Use per-CPU maps when possible
- Minimize kernel-userspace transfers
- Batch operations
operations:
- Implement gradual rollouts
- Maintain fallback mechanisms
- Document all custom programs
- Version control eBPF code
Performance Tuning
// Optimized eBPF program for high-performance monitoring
#include <linux/bpf.h>
#include <linux/ptrace.h>
// Use per-CPU arrays for better performance
BPF_PERCPU_ARRAY(latency_hist, struct latency_data, 1024);
// Efficient data structure
struct latency_data {
u32 count;
u64 total_ns;
u64 min_ns;
u64 max_ns;
} __attribute__((packed));
// Optimized latency tracking
SEC("kprobe/tcp_sendmsg")
int trace_tcp_sendmsg(struct pt_regs *ctx) {
u32 pid = bpf_get_current_pid_tgid() >> 32;
// Early exit for non-target processes
if (pid < 1000) return 0;
// Use per-CPU storage
u32 cpu = bpf_get_smp_processor_id();
struct latency_data *data = latency_hist.lookup(&cpu);
if (!data) return 0;
// Atomic updates
__sync_fetch_and_add(&data->count, 1);
return 0;
}
Future of eBPF in Cloud Native
Emerging Trends and Capabilities
interface FutureCapabilities {
feature: string;
status: 'available' | 'in-development' | 'planned';
impact: 'high' | 'medium' | 'low';
timeline: string;
}
const ebpfRoadmap: FutureCapabilities[] = [
{
feature: "Windows eBPF support",
status: "in-development",
impact: "high",
timeline: "2025 Q2"
},
{
feature: "eBPF-based service mesh",
status: "available",
impact: "high",
timeline: "Now (Cilium Service Mesh)"
},
{
feature: "ML-powered anomaly detection",
status: "in-development",
impact: "high",
timeline: "2025 Q3"
},
{
feature: "Cross-cluster eBPF federation",
status: "planned",
impact: "medium",
timeline: "2025 Q4"
},
{
feature: "eBPF for serverless",
status: "in-development",
impact: "high",
timeline: "2025 Q2"
}
];
Integration with AI/ML
# Future: AI-powered eBPF analysis
import tensorflow as tf
from pixie import DataFrame
class eBPFAnomalyDetector:
def __init__(self):
self.model = self.load_model()
def analyze_patterns(self, timeframe='-1h'):
# Collect eBPF data
df = DataFrame('syscall_events', start_time=timeframe)
# Feature extraction
features = self.extract_features(df)
# Predict anomalies
predictions = self.model.predict(features)
# Generate alerts
anomalies = self.identify_anomalies(predictions)
return {
'anomaly_count': len(anomalies),
'risk_score': self.calculate_risk(anomalies),
'recommendations': self.generate_recommendations(anomalies)
}
Conclusion
eBPF represents a fundamental shift in how we approach observability in Kubernetes environments. By moving monitoring from the application layer to the kernel level, organizations can achieve:
- 80-95% reduction in resource overhead
- 10x improvement in data collection performance
- Complete visibility from kernel to application
- Real-time security threat detection
- Simplified operational complexity
The transition from sidecar-based monitoring to eBPF is not just a technical upgrade—it's a strategic advantage that enables organizations to scale their Kubernetes deployments more efficiently while gaining deeper insights than ever before.
As we move into 2025 and beyond, eBPF will become the standard for cloud-native observability, offering unprecedented visibility with minimal overhead. Organizations that adopt eBPF-based solutions today will be better positioned to handle the challenges of tomorrow's distributed systems.
Next Steps
- Evaluate your current observability stack for resource usage and coverage gaps
- Start with a proof of concept using Pixie or Cilium in a development environment
- Measure the performance improvements and cost savings
- Plan a phased migration following the strategies outlined in this guide
- Join the eBPF community to stay updated on best practices and new capabilities
The future of Kubernetes observability is here, and it's powered by eBPF. The question isn't whether to adopt eBPF-based monitoring, but how quickly you can realize its benefits in your environment.