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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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Datadog's commitment to OpenTelemetry and the open source community
2023-01-31 · via Datadog | The Monitor blog

The OpenTelemetry (OTel) project is an open source initiative with the goal of providing vendor-neutral standards and tools that enable users to collect telemetry from any source in their environment and send it to any backend. A core tenet of Datadog is to provide a single, unified platform for customers to easily collect and monitor all of their observability data, regardless of where it comes from. From the start, OTel’s value proposition has generated a lot of excitement and support at Datadog.

We also see our excitement for a universal, open source telemetry standard reflected in our customers and the community, as use of OTel has increased rapidly. Our goal is for Datadog to be the best observability platform for OpenTelemetry. We contributed the original instrumentation libraries to the project and are working hard to continue improving Datadog’s support for OTel and the broader open source observability community. In this post, we’ll outline some of the recent efforts we’ve made to provide a first-class product and customer experience for OTel users, and then look at some of the investments currently underway.

Over the past six months, we’ve built a dedicated team to develop Datadog’s support for OpenTelemetry. Since then, we have:

  • Announced general availability for the Datadog Exporter for the OpenTelemetry Collector, which has stable support for OTLP traces and metrics and alpha support for logs.
  • Announced general availability of OTLP ingest in the Datadog Agent, enabling Datadog customers to use OTel SDKs while benefiting from the Datadog Agent’s ecosystem of integrations.
  • Released the Datadog Processor for the OpenTelemetry Collector, providing accurate trace metrics for OTel users.
  • Rolled out W3C trace context support to almost all Datadog APM tracers (with the rest on the way), allowing Datadog and OpenTelemetry instrumentation to collaborate in a mixed environment.
  • Added support for the OpenTelemetry Collector Host Metrics receiver, with plans to continue expanding our infrastructure monitoring support for OTel users.
  • Improved our support for OTel histograms through data quality and visualization improvements.
  • Enabled seamless integration between Datadog RUM and OTel-instrumented backends.

In addition to these, we are currently preparing to roll out:

  • Support for OTel runtime metrics.
  • Additional improvements to our OTel product and feature support that are under active development, such as 128-bit trace IDs and support for the OpenTelemetry API in Datadog tracers.

In addition to building OTel support into the platform, Datadog will continue developing our own instrumentation and innovating across the observability space with products such as App and API Protection, Dynamic Instrumentation, our Continuous Profiler, and Data Streams Monitoring. However, we will also continue investing heavily in the interoperability and accessibility of these products for OpenTelemetry users. This means continuing to provide easy and stable on-ramps for our customers exploring and investing in OTel for their instrumentation needs.

Finally, our support for OpenTelemetry doesn’t stop where our product does. From its start, we have been actively engaged with the OpenTelemetry community. We will continue to ramp up that engagement and contribute back upstream to help improve the project for everyone. Beyond OpenTelemetry, Datadog is committed to open source and open standards. We have consistently looked for ways that we can give back to the community, from releasing projects including Stratus Red Team and glommio, to our Datadog for open source program, to our upstream contributions across the ecosystems we participate in.

We believe OTel will play a large role in the future of observability. We are excited to continue working with the community to help develop the project and make sure Datadog customers can take advantage of the full value that OTel provides.

Update February 2, 2023

Update from Abhishek Singh, VP of Product, APM

The initial intent of this post was to provide an update on the work being done by the Datadog OpenTelemetry team. It’s become clear this post has been interpreted as a response to a recent GitHub pull request. Given that, I’d like to reaffirm that Datadog is committed to supporting OpenTelemetry. As part of that commitment, we’ll continue to improve our communication within the ecosystem. As our OTel roadmap evolved based on customer feedback, we should have closed the loop in the receiver pull request. We apologize for not doing this. As a matter of policy, we will never attempt to compel a community member to withdraw a code contribution. This applies not just to OpenTelemetry but to any and all open source ecosystems we participate in.

While we are not able to officially support the receiver, it is for the community to decide what gets merged, not us. We are excited about the future of OTel, and you can expect more regular updates from us in the future.