How eBPF Enables Deep Observability Without Sidecars

While sidecars solved the visibility issue, it came at a price that most teams didn’t fully anticipate or account for. More containers, more latency, and more overhead, all just to see what’s going on inside the system. eBPF is all about re-balancing this trade-off. As Kubernetes begins to scale, observability cannot be based on per-pod instrumentation anymore. The eBPF model moves that visibility to the Linux kernel and gives you real-time deep insights without impacting your application workload.

Observability without the trade-off

eBPF (extended Berkeley Packet Filter) essentially enables programs to be safely and efficiently run within the Linux Kernel. As opposed to sidecars or agents, it works by hooking into kernel events, networking, system calls, and processes. eBPF operates within the kernel, so you can observe all activities on a node without having to add instrumentation to your application. You don’t have to change applications, insert proxies, or redeploy to gain visibility. For teams running Kubernetes at scale, this eliminates a major friction point.

eBPF is added as an observability layer on top of a platform instead of building it into every service.

Why sidecars don’t work at scale

Sidecars worked well in the past and made it very simple to deploy logging, tracing, and security without making changes to the code. Service meshes leaned heavily into this model, and for good reason. That being said, however, as you scale up, it gets expensive. Every sidecar takes up CPU and memory, and when you multiply that across hundreds or thousands of pods, the overhead becomes hard to ignore. There’s also overhead on the operations side for managing versions, configurations, and the added complexity of compatibility. So while it works, it doesn’t scale very cleanly.

eBPF changes this. Instead of distributing observability across every pod, it’s centralized in the kernel, where a small set of eBPF programs can observe everything going on across each node, driving down both cost and complexity. 

Network tracing, minus the proxy

eBPF does not introduce proxies or packet mirroring to observe your network. Both introduce overhead, either through additional hops or duplicated data flows. eBPF works by tracing packets within the kernel itself. This means teams can observe service-to-service communication within the application for latency, connections, and flow, without needing to reroute.

In Kubernetes, where east-west traffic is the dominant form of traffic, being able to see what the microservices are doing without changing deployments or introducing extra infrastructure is a big deal. 

Performance monitoring at source

eBPF also has a really powerful use case for system-level performance since it hooks into kernel events and has a very granular view into CPU, memory, disk I/O, and system calls in real-time. This makes it great for investigating the source of a performance issue, whether that source is bad code, resource contention, or infrastructure restraints.

Instead of inferring problems from high-level metrics, teams can observe the actual behavior of the system.

This transition from approximation to seeing things firsthand is what makes eBPF so valuable to production systems. 

Secure visibility that’s hard to hide from

Security is where true kernel-level visibility really stands out. With eBPF, you can monitor system calls and process behavior in real time and detect potential security threats as they happen, such as privilege escalation, suspicious process behavior, or unauthorized access. Additionally, since visibility comes from the kernel, it is significantly harder to evade than if it were at the application layer.

Application-level attackers may be able to slip by certain logs and some traditional observability tools. However, kernel-level monitoring makes those actions more visible as there is nowhere to hide. That visibility is especially important in fluid environments like Kubernetes, where services start and stop regularly. 

Efficiency and scalability

The biggest issue with the sidecar-based model is that it does not scale well. The more services that exist, the more sidecars you need. eBPF eliminates that connection, offering much higher efficiency, even for large-scale deployments. Where sidecar-based models require hundreds or thousands of per-service proxies, eBPF can deliver similar visibility with a far smaller footprint at the kernel level.

Additionally, you don’t have to manage sidecar injection or version compatibility and you don’t have to debug configuration on a per-service basis. Observability becomes embedded in the platform.

Real-time insights, no lag

Another advantage of eBPF is how quickly it delivers insights. Traditional observability pipelines often involve collecting data from many different sources and sending it to a central location before processing, which can result in a significant delay between an issue actually occurring and becoming observable. In many cases, by the time you see the problem, it has already caused damage. By capturing that data at the kernel level, you get near real-time visibility into what’s going on across your environment. 

That speed makes a big difference, not only when you are debugging an issue but when you need to mitigate a security threat before the issue spreads.

A shift toward kernel-native observability

In conclusion, eBPF offers a new approach to observability. Instead of instrumenting every service, teams are moving toward platform-level visibility. Capture the signal once, at the lowest level, and apply it across the entire system. This approach is an excellent fit for cloud-native architectures, where services can be ephemeral and distributed. It reduces duplication and overhead, while also providing the user with a more complete view of system activity.

So while sidecars allowed for cloud-native observability, they were not the ideal long-term solution. eBPF offers a cleaner alternative, one that delivers deep visibility without the overhead of per-service instrumentation.