What is eBPF in API observability?

eBPF is a Linux kernel technology that allows small, sandboxed programs to run safely in the kernel when selected system or application events occur. These programs are verified by the kernel and attached to hooks such as system calls, network events, tracepoints, and supported user-space functions. This allows teams to extend and instrument kernel behavior without changing the kernel source code or loading traditional kernel modules.

Enterprises use eBPF for observability, networking and load balancing, security enforcement, performance profiling, application tracing, and other operating-system-level functions.

For API observability, eBPF-based tools collect network and runtime signals from selected hooks without requiring changes to application code. Depending on the probes, protocols, encryption, and tool capabilities, eBPF can provide:

• HTTP or gRPC methods, paths, status codes, and duration measurements for supported traffic
• Network connection, socket, TCP, and DNS activity
• Process, container, and workload context associated with network traffic
• Communication between internal services
• Visibility into supported API traffic that bypasses registered gateways, helping teams identify potential shadow or undocumented APIs

eBPF adds infrastructure-level evidence, but its coverage depends on the protocol, encryption, runtime environment, attached probes, and observability tool.

Why does an API gateway provide only a partial view of API activity?

An API gateway reports on the traffic it processes. Its dashboard is authoritative for managed routes and commonly includes request counts, response codes, latency, authentication results, route usage, and rate-limit activity. However, it cannot describe every interaction in a distributed system.

A good chunk of the traffic can remain outside a gateway’s view when:

• Services communicate directly without passing through the gateway
• A workload exposes an unregistered or shadow API
• A deprecated endpoint remains accessible through an old route or direct address
• Traffic passes through a different gateway or network boundary
• A failure occurs during DNS resolution, connection establishment, or another lower-level operation

This limitation becomes more significant in fragmented gateway environments. By 2028, Gartner predicts that more than 75% of organizations will use two or more API gateways. Gartner also warns that uncoordinated multigateway environments can lead to API sprawl and shadow APIs. These gaps carry material risk: Gartner reports that an average API breach exposes at least ten times more data than an average security breach.

eBPF-based observation extends visibility to supported activity on Linux hosts and nodes. It can show which processes communicated, which network paths they used, and what happened below the application layer, including for connections that bypass a managed gateway. This evidence complements gateway analytics, which still provides consumer, authentication, quota, route, and policy context.

How does eBPF collect observability data?

eBPF-based observability tools load programs into the Linux kernel, where they are checked by the kernel verifier, and attach them to selected hook points such as tracepoints, kernel probes, user-space probes, and networking hooks. When the corresponding event occurs, such as when a process opens a connection, data passes through a socket, or a system call is invoked, the program can collect selected event data and make it available to a user-space component through BPF maps or event buffers for further processing.

A typical collection process has four stages:

1. Load and verify the program. A user-space loader submits the eBPF program to the Linux kernel. Before accepting it, the kernel verifier checks the program’s instructions, memory access, control flow, and other safety constraints.
2. Attach it to a hook. The tool attaches the loaded program to supported hook points such as tracepoints, kernel probes, user-space probes, socket hooks, or networking hooks.
3. Collect the data. When a hook is triggered, the program can capture permitted information such as timestamps, connection details, process identifiers, and byte counts. It can store measurements in BPF maps or submit event records through buffers.
4. Export the telemetry. A user-space collector reads the captured information, processes or correlates it, and exports it to an observability platform as metrics, logs, or traces.

Under the hood, eBPF tools use mechanisms such as tracepoints, kprobes, uprobes, maps, and ring buffers. The Linux kernel documentation describes how programs are checked, while the eBPF project overview explains the broader execution and data-collection model.

The verifier is an important safety control, but it does not remove every operational risk. Production deployments still require compatible kernels, appropriate permissions, tested programs, resource controls, and monitoring.

How does gateway monitoring compare with eBPF observability?

Gateway monitoring and eBPF answer different questions.

A gateway is the better source for policy decisions made at that gateway. eBPF is useful for finding activity outside the managed path and for examining host-level behavior. A complete implementation usually correlates both sources rather than selecting one and discarding the other.

How can eBPF help identify shadow and deprecated APIs?

A shadow API is an API that operates outside the organization's approved inventory or management process. A deprecated API, sometimes called a zombie API, is an older interface that remains deployed or continues to receive traffic after it should have been retired.

Configuration-based inventories can miss these interfaces because they describe declared state. OpenAPI documents, service registries, and gateway configurations show what teams have recorded. Runtime observation shows which connections and requests actually occur.

An eBPF-based discovery system can observe supported API traffic on monitored hosts and compare the resulting endpoints with gateway routes, specifications, catalogs, and ownership records. Differences between runtime activity and declared inventory become candidates for investigation. Repeated traffic to an old version, for example, is evidence that retirement is incomplete; it is not by itself proof that the endpoint is unsafe or has no business owner.

This use case corresponds to API9:2023 Improper Inventory Management in the OWASP API Security Top 10. OWASP recommends maintaining an inventory of API hosts and versions and defining retirement plans. Runtime discovery can support that work, but ownership and lifecycle decisions still require governance data and human review.

How does eBPF compare with other observability methods?

No single collection method provides every type of telemetry.

These methods work best together. An eBPF-based tool can reveal network activity and supported API requests on monitored hosts, including traffic that bypasses a gateway or continues to reach an old endpoint. This runtime evidence can help teams find shadow or deprecated APIs, but it must be compared with gateway, catalog, lifecycle, and ownership data.

Application instrumentation supplies information that eBPF-based collection usually cannot, including business context, custom spans, application-specific attributes, and explicit trace relationships. The OpenTelemetry eBPF Instrumentation documentation explains the capabilities and limits of this form of automatic instrumentation.

What is the performance cost of eBPF observability?

eBPF can collect data efficiently because programs execute at selected event hooks and can filter or aggregate information before sending it to user space. Just-in-time compilation allows supported programs to run as native machine instructions. A node-level collector can also avoid the need to deploy a separate proxy or language agent for every workload.

However, eBPF observability is not cost-free, and there is no universal overhead percentage. Resource use depends on:

• The number and frequency of observed events
• The amount of data copied from kernel space
• Whether payloads, stack traces, or only metadata are collected
• Sampling and aggregation settings
• Map and buffer sizes
• Protocol parsing and user-space processing
• Workload and kernel characteristics

The appropriate way to evaluate overhead is to benchmark the selected product with representative traffic. Measure application latency, throughput, CPU, memory, dropped events, and telemetry volume before and after enabling each collection feature. Published measurements from a different tool or workload should not be treated as a performance guarantee.

What security information can eBPF provide?

Depending on the probes, product, and data used for correlation, eBPF-based observability can provide or help identify security-relevant evidence such as:

• New or unexpected connections between workloads
• Communication with unapproved destinations
• Traffic to endpoints absent from the API inventory
• TCP resets, connection failures, and unusual connection patterns
• Process, container, and network identity associated with a flow
• Use of plaintext transport where encrypted transport is expected

This evidence can improve asset discovery, incident investigation, and detection engineering. It does not automatically determine whether a user was authorized to access a particular object, whether a business operation was fraudulent, or whether an application correctly enforced its access-control rules. Those conclusions require identity, policy, and application context that may not be present at kernel observation points.

Encrypted traffic also requires careful treatment. Network-level metadata remains observable, but HTTP paths, headers, and bodies are not generally available from encrypted packets alone. Some tools use user-space probes at supported TLS libraries or runtimes to observe data before encryption or after decryption. Coverage therefore varies by language, runtime, library version, and deployment configuration.

Where can eBPF be deployed, and what are its limits?

eBPF-based observability can run on compatible Linux hosts, including bare-metal servers, virtual machines, and Kubernetes worker nodes, provided the operator has permission to load and attach the required programs.

In Kubernetes, the collector commonly runs as a node-level agent deployed through a DaemonSet. This places an agent on each eligible node, where it loads the required eBPF programs and collects events from configured workloads.

Compatibility depends on the kernel version and configuration, available hook types, processor architectures supported by the tool, required privileges, and platform security policies. Fully managed gateways, many serverless services, and other environments that do not provide access to the underlying host may not permit eBPF deployment. In these cases, teams must rely on provider telemetry, gateway data, application instrumentation, or another collection method.

For ordinary socket-based traffic, eBPF tools can collect network-level signals such as connection addresses, ports, byte counts, and timing without depending on the application’s programming language. Higher-level interpretation is not automatic. Identifying HTTP routes, gRPC calls, database operations, encrypted request details, or trace relationships depends on the tool’s probes, protocol parsers, and support for the relevant runtime, libraries, and encryption method.

How does APIwiz use eBPF for API observability?

eBPF provides runtime evidence, but that evidence needs API context before teams can act on it. APIwiz Observe uses eBPF-powered logging to collect supported runtime signals from monitored hosts without requiring application code changes or deploying a sidecar alongside every pod. It brings this activity into a shared view with API inventory, gateway, environment, version, domain, consumer, and dependency information.

This helps teams:

• Reduce instrumentation effort. Teams can collect supported network and API signals without adding an SDK to every service or deploying a sidecar alongside every pod.
• Keep resource utilization low. A node-level collector avoids running a sidecar for every pod, helping keep collection overhead low.
• Find gaps in the API inventory. Observed endpoints can be compared with catalog and gateway records to surface potential shadow or undocumented APIs.
• Manage deprecated APIs. Runtime usage shows whether older versions still receive traffic and which consumers may be affected by retirement.
• Investigate incidents faster. Centralized, indexed API logs can be searched and filtered alongside traces, latency, errors, and workflow information.
• Add operational context. Platform teams can relate runtime activity to API ownership, versions, environments, consumers, and dependencies.
• Support security and compliance investigations. Runtime records provide evidence for examining policy deviations, unexpected traffic, and activity involving unmanaged APIs.

For API platform teams, this reduces the work required to reconcile gateway dashboards, service logs, and catalog records. Operations teams gain context for troubleshooting, while security and governance teams can prioritize active risks using current traffic and ownership information.

In its Commercial Bank of Qatar case study, APIwiz reports that traffic hooks deployed across the bank’s data plane helped create a centralized inventory and identify shadow and deprecated APIs during the initial discovery phase.

Coverage still depends on host access, probes, protocols, encryption, and runtime support. APIwiz does not guarantee visibility into every packet, payload, or API transaction, and eBPF does not replace gateway telemetry or application instrumentation.

Key takeaways

eBPF gives observability tools a host-level view of supported network, system, and application events without requiring changes to application code or a sidecar for every pod. For API teams, this runtime evidence can reveal internal service communication, network failures, and activity involving potential shadow, undocumented, or deprecated APIs.

eBPF complements existing observability methods. Gateway telemetry provides route, consumer, authentication, quota, and policy context, while application instrumentation supplies business events, custom spans, and application-specific details. Coverage and resource consumption depend on host access, kernel compatibility, permissions, probes, event volume, protocols, encryption, runtime support, and tool capabilities.

APIwiz Observe combines eBPF-powered logging with API inventory, ownership, lifecycle, gateway, consumer, and dependency information. This helps platform, operations, security, and governance teams investigate issues and act on runtime findings with the appropriate API context. 

Book a demo with APIwiz to see how eBPF-powered observability can help your teams discover and catalog shadow APIs, investigate runtime activity, and bring unmanaged endpoints under governance.

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FAQs about eBPF and API observability

What is eBPF in simple terms?

eBPF is a Linux kernel technology that runs small, verified programs inside the kernel when selected events occur. Observability tools use it to collect network and process data without changing the source code of every monitored application.

Does eBPF monitor everything on a Linux host?

The Linux kernel provides tracepoints and other hook types across networking, filesystems, system calls, process scheduling, and additional subsystems. An eBPF observability tool attaches programs only to the hooks relevant to the activity it needs to inspect. Coverage therefore depends on the programs the tool loads, the available kernel hooks, and its configuration. eBPF does not automatically monitor every event on the host.

Can eBPF inspect encrypted API traffic?

It can observe network metadata such as addresses, ports, timing, and transferred bytes without decrypting the traffic. To inspect HTTP paths, headers, or bodies, the tool must observe the data before encryption or after decryption and support the relevant TLS library or application runtime.

Does eBPF slow down API performance?

An eBPF-based observability tool adds some overhead. The amount depends on event volume, probe selection, payload capture, sampling, aggregation, and the collector implementation. Test the selected tool under a representative load rather than relying on a universal percentage.

Can eBPF help identify shadow APIs?

It can help. An eBPF-based product can observe supported API traffic on monitored hosts and compare discovered endpoints with gateway configurations, specifications, and catalogs. The comparison identifies possible shadow APIs for validation; eBPF alone does not determine their ownership or approved status.

How does eBPF coverage differ across API gateway hosting models?

eBPF can observe supported activity from on-premises and self-managed cloud gateways running on compatible Linux hosts when sufficient access is available. It cannot run inside fully managed gateway services that do not expose their underlying hosts, so teams must use the provider’s gateway analytics. Managed Kubernetes platforms may also restrict custom eBPF programs.

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