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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Ingest OpenTelemetry traces and metrics with the Datadog Agent
Prashant Jain, Michael Gerstenhaber · 2022-05-16 · via Datadog | The Monitor blog
Prashant Jain

Prashant Jain

Michael Gerstenhaber

Michael Gerstenhaber

OpenTelemetry is a Cloud Native Computing Foundation (CNCF) initiative that provides open, vendor-neutral standards and tools for instrumenting services and applications. Many organizations use OpenTelemetry’s collection of APIs, SDKs, and tools to collect and export observability data from their environment to their preferred backend.

As part of our ongoing commitment to OpenTelemetry, we are proud to have contributed our distributed tracing libraries to the CNCF community. We also offer multiple solutions to ensure that OpenTelemetry users have the flexibility to easily send their observability data to Datadog.

Today, we are pleased to expand on our commitment to OpenTelemetry by announcing official support for OpenTelemetry Protocol (OTLP) in the Datadog Agent, enabling you to reliably ingest traces and metrics from applications that have been instrumented with OpenTelemetry libraries. OTLP is a specification for encoding and transmitting telemetry data between sources, intermediaries (e.g., collectors), and backends. With native support for OTLP, the Datadog Agent now enables you to get deep visibility into your OpenTelemetry-instrumented applications without updating your code or migrating away from your existing workflows.

Send OpenTelemetry traces directly to the Datadog Agent

If you’ve instrumented your applications with OpenTelemetry libraries, you can export OTLP traces directly to the Datadog Agent (version 7.35+) through gRPC or HTTP, without installing a separate OpenTelemetry Collector. The snippet below shows how you can update your Datadog Agent configuration file (datadog.yaml) to enable the Agent to ingest OpenTelemetry traces over gRPC:

otlp_config:

receiver:

protocols:

grpc:

You can also configure trace ingestion by setting environment variables. See our documentation for Kubernetes (including Helm) and other configuration examples.

Once you’re collecting your OTLP traces, you can start visualizing and monitoring that data with Datadog APM. You can also use this method to collect OTLP-formatted metrics.

Because the Datadog Agent can also collect application profiles, network data, infrastructure metrics from 1,000+ integrations, and other telemetry from your environment, you can get rich context around your OTLP traces and gain a better understanding of your systems and applications. You can also connect traces with logs to get a more complete picture of your stack.

Alternatively, you can export OTLP traces and metrics to our platform by using the Datadog exporter and the OpenTelemetry Collector. This option helps streamline your workflows, for example, if you want to use the OpenTelemetry Collector to export telemetry data to multiple backends.

You can send OTLP traces and metrics to Datadog through two methods: directly with the Datadog Agent or by using the Datadog exporter and the OpenTelemetry Collector.

Observability with Datadog APM and OpenTelemetry

Whether you ingest OpenTelemetry data with the Datadog Agent or the Datadog exporter, Datadog APM will enable you to get deep visibility into your applications by:

  • Querying all your traces in real time to troubleshoot business-critical application performance issues

  • Visualizing dependencies to understand how data flows through your architecture by inspecting the Request Flow Map and Service Map

  • Understanding key insights from each service with the APM Service Page, which provides a centralized view of telemetry, SLOs, relevant incidents, deployment-tracking data, and more

  • Automatically detecting performance anomalies, faulty deployments, outliers, and root causes of critical failures with Watchdog, Datadog’s AI engine

Opening more paths to observability

Datadog is dedicated to ensuring that our users get deep visibility into their services and applications, regardless of whether they’re using our open source APM libraries, OpenTelementry SDKs, or other OpenTelemetry-compatible instrumentation methods. We are excited to continue working alongside the rest of the OpenTelemetry community to shape the future of open instrumentation by providing flexible, extensible solutions for collecting telemetry data from services and applications.

Read our documentation to learn more about the Datadog Agent’s support for OpenTelemetry. Or, if you’re new to Datadog, get started with a 14-day free trial.