New Relic OpenTelemetry integration guide helps you instrument services fast and cut debugging time
Use this New Relic OpenTelemetry integration guide to send traces, metrics, and logs fast. Set OTLP endpoints, define clear service names, run the OpenTelemetry Collector, and turn on auto-instrumentation. We also cover sampling, Kubernetes setup, and validation steps so you can measure latency and errors within minutes.
Modern apps need clean telemetry to be fast and stable. OpenTelemetry gives you one open standard for data. New Relic turns that data into clear charts and traces. Put them together, and you get quick end-to-end visibility without lock-in. Follow the steps below to start strong and avoid noisy, costly data.
Why pair OpenTelemetry with New Relic
One pipeline for traces, metrics, and logs
Fast auto-instrumentation across popular languages
Vendor-neutral data model to reduce lock-in
Flexible cost control with sampling and filtering
Connected views in New Relic (APM, logs, errors, dashboards)
New Relic OpenTelemetry integration guide
This New Relic OpenTelemetry integration guide focuses on quick wins, safe defaults, and clean data. Use it to go from zero to useful traces in under an hour.
Step 1: Plan your telemetry model
Decide on names and tags before you send data.
service.name: Use a stable, human-friendly app name (example: checkout-service)
deployment.environment: dev, staging, or prod
service.version: app version (example: 1.4.2)
Common attributes: region, customer_tier, team
Add these as resource attributes so they show up on every span, metric, and log.
Step 2: Choose your ingest path
You have three solid options:
Direct from SDK to New Relic OTLP endpoint (simple, great for small apps)
Auto-instrumentation agent (fastest start, zero code changes)
OpenTelemetry Collector (best for fleets, sampling, transforms, and routing)
Use direct export for quick pilots. Use the Collector when you need filtering, tail-based sampling, or multiple destinations.
Step 3: Configure endpoints and authentication
Set the standard OTLP variables used by most SDKs and agents.
Endpoint (US): https://otlp.nr-data.net
Endpoint (EU): https://otlp.eu01.nr-data.net
Protocol: gRPC (4317) or HTTP/protobuf (4318)
Header: set your New Relic license key as an API key header
Typical environment variables:
OTEL_EXPORTER_OTLP_ENDPOINT = your region endpoint with port
OTEL_EXPORTER_OTLP_PROTOCOL = grpc or http/protobuf
OTEL_EXPORTER_OTLP_HEADERS = api-key=YOUR_NEW_RELIC_LICENSE_KEY
OTEL_RESOURCE_ATTRIBUTES = service.name=checkout-service,service.version=1.4.2,deployment.environment=prod
Check New Relic docs for the latest region endpoints and header names.
Step 4: Turn on auto-instrumentation
Speed matters. Agents give you traces fast with no code edits.
Java: use the OpenTelemetry Java agent (add -javaagent and OTEL_ variables)
.NET: install the OpenTelemetry .NET packages and enable ASP.NET/HTTP instrumentation
Node.js: use @opentelemetry/auto-instrumentations-node
Python: run your app with opentelemetry-instrument
Go: add SDK and instrumentation packages in code (Go has no global agent)
Start with HTTP, gRPC, database, and messaging instrumentation. Add custom spans later for business steps.
Step 5: Use the OpenTelemetry Collector (optional but powerful)
Run the Collector as a sidecar, DaemonSet, or gateway to manage traffic.
Receivers: otlp (traces/metrics/logs)
Processors: batch, attributes (add or drop tags), tail_sampling (reduce cost)
Exporters: otlp to New Relic endpoint
This adds resilience, buffers spikes, and lets you try tail-based sampling for high-value traces.
Step 6: Add custom spans and attributes
Auto-instrumentation shows the path. Custom spans show the “why.”
Name spans after actions users care about (CreateOrder, ChargeCard)
Add attributes like order_id, user_id (hash or redact as needed)
Record errors with error.type, error.message
Propagate W3C Trace Context across services and queues
With clean names and attributes, you can filter and find issues fast in New Relic.
Step 7: Sampling and cost control
Balance visibility and spend.
Start with head-based sampling at 10–20% in production
Use tail-based sampling in the Collector to keep slow/error traces
Raise sampling for new releases, drop it after stability
Batch and compress exports to reduce network cost
Keep 100% in staging to debug releases before they hit prod.
Step 8: Kubernetes and serverless tips
Kubernetes:
Run a Collector DaemonSet on each node for low-latency ingest
Enable resource detection so spans include k8s.cluster.name, namespace, pod
Set service.name and environment via Pod env vars
Serverless:
AWS Lambda: use the OpenTelemetry Lambda layer, set OTLP endpoint and headers
Keep payload sizes small; use batch and short timeouts
Step 9: Validate end-to-end
Confirm data flow before broad rollout.
Run a test request that hits multiple services
Open Distributed Tracing in New Relic and search for service.name
Check for missing spans between hops (context propagation issues)
Verify attributes and environment tags appear as filters
Look for export errors in SDK or Collector logs
Troubleshooting and quick fixes
No data in New Relic: verify API key header, endpoint, and open ports (4317/4318)
Missing service.name: set OTEL_RESOURCE_ATTRIBUTES or agent flags
Broken trace links: ensure W3C headers pass through load balancers and gateways
High cardinality: drop user-level IDs; prefer hashed or bucketed values
Clock drift: sync NTP; wrong timestamps can hide spans
TLS errors: update root certificates on hosts and containers
Dashboards and alerts that matter
Start with signals that point to real user pain.
p50/p95 latency per service and endpoint
Error rate and top error types
Throughput by region and customer tier
Cold start or GC pause metrics where relevant
SLOs: latency and availability with burn-rate alerts
Use span attributes to slice by version so you can spot bad releases fast.
Preparing for AI-driven operations
As platforms add AI-powered guidance, clean telemetry becomes even more valuable. Clear service names, stable attributes, and sampled-but-representative traces help any intelligent feature explain issues and suggest fixes. Invest in data quality now to get better insights later, without changing your code again.
Good observability is a habit. Revisit your tags, sampling, and dashboards with each major release. Use runbooks that link traces to logs and rollbacks. Keep the Collector config in version control and review it like application code.
When you follow this New Relic OpenTelemetry integration guide, you get faster tracing, cleaner data, and lower noise. You also keep your options open with open standards. Start small, validate, and expand across services—your incident response time will drop, and your team will feel the difference.
(Source: https://techcrunch.com/2026/02/24/new-relic-launches-new-ai-agent-platform-and-opentelemetry-tools/)
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FAQ
Q: What is the New Relic OpenTelemetry integration guide and what can I accomplish with it?
A: This New Relic OpenTelemetry integration guide shows how to send traces, metrics, and logs quickly by setting OTLP endpoints, defining clear service names, running the OpenTelemetry Collector, and enabling auto-instrumentation. Following the steps lets you validate telemetry and measure latency and errors within minutes.
Q: How should I plan service names and resource attributes before sending telemetry?
A: Decide on stable, human-friendly service.name values and add deployment.environment and service.version as resource attributes so they appear on every span, metric, and log. Include common attributes like region, customer_tier, and team to make filtering and dashboards more consistent.
Q: What ingest paths can I use to export telemetry to New Relic?
A: The guide outlines three options: direct SDK export to the New Relic OTLP endpoint for simple or small apps, auto-instrumentation agents for zero-code starts, and the OpenTelemetry Collector for fleets, sampling, transforms, and routing. It recommends direct export for quick pilots and the Collector when you need filtering, tail-based sampling, or multiple destinations.
Q: Which OTLP endpoints, protocols, ports, and environment variables should I configure?
A: Configure the regional OTLP endpoints (US: https://otlp.nr-data.net, EU: https://otlp.eu01.nr-data.net) and use gRPC (4317) or HTTP/protobuf (4318) as the protocol. Set your New Relic license key as an API key header and use environment variables like OTEL_EXPORTER_OTLP_ENDPOINT, OTEL_EXPORTER_OTLP_PROTOCOL, OTEL_EXPORTER_OTLP_HEADERS=api-key=YOUR_NEW_RELIC_LICENSE_KEY, and OTEL_RESOURCE_ATTRIBUTES to declare service.name and other resource attributes.
Q: How do I enable auto-instrumentation for common languages?
A: Use the OpenTelemetry Java agent with -javaagent and OTEL_ variables for Java, install OpenTelemetry .NET packages and enable ASP.NET/HTTP instrumentation for .NET, and use @opentelemetry/auto-instrumentations-node for Node.js. For Python run your app with opentelemetry-instrument and for Go add the SDK and instrumentation packages in code since Go has no global agent; start with HTTP, gRPC, database, and messaging instrumentation and add custom spans later.
Q: When should I run the OpenTelemetry Collector and what benefits does it provide?
A: Run the Collector as a sidecar, DaemonSet, or gateway when you need fleet-level control, buffering, transforms, or advanced sampling like tail-based sampling. The Collector provides receivers (otlp), processors (batch, attributes, tail_sampling), and exporters (otlp to New Relic) to add resilience, buffer spikes, and support filtering and routing.
Q: What sampling and cost-control practices does the guide recommend?
A: Start with head-based sampling at roughly 10–20% in production and use tail-based sampling in the Collector to retain slow or error traces. Raise sampling for new releases and drop it after stability, batch and compress exports to reduce network cost, and keep 100% sampling in staging for debugging.
Q: How can I validate my telemetry pipeline and troubleshoot common issues?
A: Run a test request that touches multiple services, open Distributed Tracing in New Relic and search for service.name, check for missing spans and verify attributes and environment tags, and inspect SDK or Collector logs for export errors. Common quick fixes include verifying the API key header and endpoints and open ports 4317/4318, setting OTEL_RESOURCE_ATTRIBUTES if service.name is missing, ensuring W3C trace context headers pass through gateways, dropping or hashing user-level IDs to reduce cardinality, syncing NTP for clock drift, and updating root certificates for TLS errors.