Hire OpenTelemetry Engineers
for end-to-end observability

From auto-instrumentation to custom Collector pipelines, our OpenTelemetry engineers give your distributed systems full-signal visibility — traces, metrics, and logs — without vendor lock-in.

Discuss your observability project

50+

services instrumented

10+

languages supported

25+

DevOps & observability engineers

Core Capabilities

What we build
with OpenTelemetry

Distributed Tracing Pipelines

End-to-end trace propagation across microservices

Auto and manual instrumentation across Java, Python, Go, and Node.js services — with W3C TraceContext propagation, tail-based sampling strategies, and export to Jaeger, Grafana Tempo, or any OTLP backend.

OTel Collector Architecture

Centralised telemetry routing & processing

Production Collector deployments with receivers, processors, and exporters configured for fan-out to multiple backends — Prometheus, Grafana Cloud, Datadog, or your own storage — with batching, retry, and attribute enrichment built in.

Unified Signals — Metrics, Logs & Traces

Single instrumentation layer for all telemetry

Correlate traces, metrics, and structured logs from a single OTel SDK — linking a slow trace to a CPU spike to a log error in one click, giving on-call engineers instant root cause context without context-switching between tools.

How It Works

From dark services to
full-signal clarity

Observability
Audit

We map your service topology, identify uninstrumented services, and assess existing telemetry gaps — defining a sampling strategy and signal priority before touching a single line of code.

SDK
Instrumentation

Auto-instrumentation agents are deployed for quick wins; custom spans and attributes are added for business-critical paths — covering HTTP, gRPC, database calls, message queues, and async jobs across all services.

Collector
Pipeline Build

Our DevOps engineers deploy and configure the OTel Collector — setting up receivers, tail-sampling processors, and multi-backend exporters — then validate the pipeline with load testing to confirm data fidelity.

Dashboard &
Alerting Setup

Trace data is wired into Grafana or Jaeger UI; metrics feed Prometheus dashboards; SLO-based alerts fire on latency, error rate, and saturation — giving every on-call engineer a complete picture before they even look at logs.

Hire OpenTelemetry Engineers

Observability specialists ready
to join your team

Scale your instrumentation coverage with dedicated OpenTelemetry engineers who ship production-grade telemetry pipelines from day one.

Hire OpenTelemetry Engineers

Auto-instrumentation for Java, Python, Node.js & .NET

OTel Collector deployment with tail-based sampling

Multi-backend fan-out (Grafana, Datadog, Jaeger, Zipkin)

W3C TraceContext propagation across polyglot stacks

SLO dashboards & error-budget alerting in Grafana

AI + OpenTelemetry

Telemetry that doesn't just
report — it predicts

AI root cause
analysis

LLM-assisted trace analysis correlates slow spans, error clusters, and metric anomalies — surfacing probable root causes in plain language so on-call engineers spend minutes on resolution, not hours on investigation.

Intelligent
sampling

ML-driven tail-based sampling in the OTel Collector retains 100 % of error and slow traces while aggressively dropping healthy, high-volume traffic — reducing storage costs without losing diagnostic signal.

Span-level
anomaly detection

Baseline latency models built on OTel span histograms automatically flag p99 regressions per endpoint — catching performance degradations before they breach SLOs or affect real users.

Generative
runbook suggestions

When an alert fires, AI analyses the attached trace and metric context to draft a step-by-step runbook suggestion — reducing mean time to resolution and capturing institutional knowledge automatically.

FAQ

Frequently Asked
Questions

OpenTelemetry is a CNCF project that provides vendor-neutral APIs, SDKs, and the Collector for generating and exporting telemetry data — traces, metrics, and logs. By instrumenting once with OTel, you avoid lock-in: you can send data to Jaeger, Zipkin, Prometheus, Datadog, Grafana Tempo, or any OTLP-compatible backend without changing your application code.

OpenTelemetry has stable SDKs for Java, Python, Go, JavaScript/Node.js, .NET, Ruby, PHP, Rust, C++, and Swift. Auto-instrumentation agents are available for Java, Python, Node.js, and .NET — meaning you can get traces and metrics from popular frameworks (Spring Boot, Django, Express, ASP.NET) with zero code changes.

The OTel Collector is a vendor-agnostic proxy that receives telemetry from your services, processes it (sampling, enrichment, filtering), and exports it to one or more backends. While you can export directly from SDKs, the Collector is recommended for production: it decouples your services from backend configuration, enables tail-based sampling, and handles batching and retry logic centrally.

OpenTelemetry propagates trace context (trace ID + span ID) across service boundaries via standard headers — W3C TraceContext and B3 are both supported. Each service creates child spans that link back to the parent, forming a complete trace tree. This lets you follow a single user request across dozens of microservices, databases, and queues in a single waterfall view.

OpenTelemetry complements rather than replaces Prometheus. The OTel Collector can scrape Prometheus endpoints and convert them to OTLP, or you can export OTel metrics directly to a Prometheus-compatible backend. For teams adopting a unified telemetry pipeline, OTel can be the single instrumentation layer that feeds both Prometheus/Grafana for metrics and Jaeger/Tempo for traces.