Catch all your data directly from the kernel

Zero instrumentation with OBI

Remove the need for manually coding instrumentation or managing and updating language-specific agents. Start capturing full-fidelity telemetry, quickly, from any runtime or language, with near-zero overhead.

Service map

One instrumentation solution for all your services

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Catch everything, no code, no language agents

OBI instruments every service, even legacy or mixed stacks, automatically, right from the kernel. No SDK’s needed.

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Deploy in 1-click with one command

One Helm install drops a DaemonSet. No dev work, no CI tweaks. Your stack lights up in minutes.

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Predictable ultra-light footprint you can scale

OpenTelemetry-native, OBI runs at < 1 % CPU per node, so you can scale across any stack.

Instrument all your data with one simple install

Capture all data from the source

By hooking into the Linux kernel, OBI captures every request at the system level ,no matter the language, runtime, or source. That means instant visibility into services you can’t, or don’t want to modify. OBI captures the data’s full context,he building complete spans and RED metrics automatically. You get ready-to-use data for complete modern observability, without touching a line of code.

Complete telemetry, one install away

Drop OBI as a deamonset using a single Helm command and watch your stack light up. Deploy once, without changes, downtime CI tweaks, or service restarts. Works the same on EKS, GKE, OpenShift, bare-metal, or air-gapped setups. Fastest path from zero data to live dashboards for any service, with zero developer overhead.

Built for speed and for scale

OBI is OpenTelemetry-native, so no proprietary formats or lock-ins. Its eBPF Kernel-level executions are pre-verified and sandboxed. They JIT-compile to micro-seconds of work per event, so your high-traffic services stay fast and your overhead stays low, with < 1 % CPU per node. eBPF attaches safely to syscalls and network activity, tracking every request in real time with microsecond overhead.

From raw data to automatic insight

Minutes after install, spans and metrics surface as dashboards, a live service map, and drill-downs linking each trace to its logs and metrics. Smart alerts flag latency or error spikes, while index-free storage keeps costs low and searches fast for automatic observability with no coding overhead.

Scalable observability for your systems

Scalable observability for your systems

In-stream analysis

Continuous, real-time monitoring of AI interactions, detecting risks and performance issues before they impact users.

Infinite retention

Ensures historical AI data remains accessible for long-term trend analysis and deep troubleshooting without data loss.

DataPrime engine

Tracks token usage and suspicious resource consumption, helping teams prevent cost overruns while maintaining AI efficiency.

Remote, index-free querying

Lightning-fast searches without the overhead of indexing, ensuring real-time AI observability without unnecessary storage costs.