Automatic request tracing and latency breakdown for every endpoint —
from API gateway to database and back. No SDKs, no agents,
no instrumentation code
Distributed traces + application performance monitoring that works
from the kernel
Identify the slowest endpoints and break down exactly where the latency is. Optimize the endpoints that matter most to your users.
Track endpoint latency against SLA targets in real time. Get alerted before latency SLA breaches affect customers.
Trace requests through your entire service mesh. See exactly where a third-party service is degrading and which service is responsible.
Compare endpoint performance across releases. Catch regressions immediately after deployment with automatic before/after comparison.
Track the quality and stability of external API calls. Know when a third-party service is degrading before your users notice.
Predict when endpoints will hit capacity limits based on traffic growth trends. Plan scaling proactively, not reactively.
Replace the patchwork of monitoring, logging, tracing, and incident management tools with a single AI-powered platform.
Every service needs its own SDK pulled in, configured, and kept up to date — a checklist item for every new service your team ships.
Attach at the kernel level once, and every request across every service is captured instantly — no SDKs, no per-service setup.
If an endpoint wasn't wired up with the SDK, it simply doesn't show up in traces — including the ones nobody remembers adding.
eBPF sees every request at the kernel level, so endpoints are discovered and traced automatically — instrumented or not.
Traces break the moment a header isn't passed correctly between services — one missed middleware and your trace goes dark.
Traces are reconstructed directly from observed traffic — no headers to propagate, no middleware to configure correctly.
You see how long a request took overall, but not where the time actually went — network, app logic, or the database.
Every request is broken down by phase, so you know exactly whether the slowdown is in the network hop, app logic, or the database call.
Without a separate error reporting SDK wired into every service, errors slip by silently until someone notices the symptoms downstream.
Errors are captured and classified automatically as they happen — no SDK to install, no error reporting pipeline to maintain.
Every SDK you run adds its own CPU and memory tax on top of your application — a cost that adds up across every service and every pod.
Running in kernel space means tracing costs almost nothing — no SDK competing for resources, no measurable tax on your services.
Every new service or deploy means remembering to add the SDK — skip it once, and that service is invisible until someone notices.
The moment a new service starts sending traffic, it's visible — no install step, no deploy checklist item to remember.
Keeping SDK versions in sync across dozens of services is its own ongoing project — with breaking changes to track and upgrades to schedule.
There's no SDK version to track or upgrade — ApexData handles everything on our end, so your team never touches it again.
Not "low instrumentation." Zero. No SDKs, no agents, no sidecars, no code changes. eBPF captures everything at the kernel level — complete APM coverage from day one.
Deploy ApexData and see every endpoint within minutes. No rollout plan, no configuration needed. Complete APM coverage from day one.
eBPF runs in kernel space — no CPU or memory overhead on your application pods. Application performance is completely unaffected.
Endpoints, traces, errors, throughput, and latency — all in one place. Stop switching between APM, logging, and tracing tools.
APM
Discovery
Latency
Traces
Errors
Throughput
Logs
Performance
SLA
Microservices
Release
Third-Party
Capacity
No more tab-switching
Traditional Stack
ApexData
Zero config
Why ApexData
