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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
Centralize your logs with Datadog and Fluent Bit
2019-10-08 · via Datadog | The Monitor blog

Fluent Bit is a lightweight, multi-platform tool that can collect, parse, and forward log data from several different sources. Because Fluent Bit has a small memory footprint (~450 KB), it is an ideal solution for collecting logs in environments with limited resources, such as containerized services and embedded Linux systems (e.g., IoT devices). If you already use Fluent Bit (v1.3.0+), you can start using our new output plugin to forward all of your collected logs to Datadog for visualization and analysis.

Monitor all of your logs with Fluent Bit and Datadog

As applications and environments grow in complexity, managing the flow of log data becomes increasingly difficult. Logs come from a wide range of sources—with different formats and destinations—so centralizing them becomes a critical part of monitoring application activity.

Once you’re centralizing your logs with Fluent Bit and Datadog, you can gain valuable insights by monitoring and correlating that data with application metrics and traces, all in one platform. Datadog’s output plugin helps you route logs from all your services, regardless of whether they’re running in containers, cloud environments, on-prem servers, or all of the above.

You can also archive your Fluent Bit logs if you need to store them long-term. And, if you need to investigate old events for an audit or postmortem, you can quickly pull archived logs back into your account.

Add the Datadog plugin to your Fluent Bit configuration

To start forwarding your Fluent Bit logs with Datadog’s new plugin, add a new output section to your Fluent Bit configuration file:

[OUTPUT]

Name datadog

Match *

Host http-intake.logs.datadoghq.com

TLS on

apikey <DATADOG_API_KEY>

dd_service web-server

dd_source apache

dd_tags project:fluent-bit, env:staging

As a part of its workflow, Fluent Bit will collect log data from one (or several) inputs and route them directly to Datadog through the available http-intake.logs.datadoghq.com logging endpoint. The TLS parameter allows Fluent Bit to send those logs over a secure, SSL-encrypted TCP connection.

The plugin also includes an API key parameter (which you can find in the settings section of your Datadog account) and parameters for linking your logs to a service and source so you can see the service and underlying technology that generated the logs in Datadog. Additionally, the tags parameter allows you to easily filter and aggregate log data.

By adding a service to Datadog’s output plugin, you can associate your logs with related traces (collected by the Datadog Agent) from the same application. This enables you to easily pivot from one datapoint to another when you need to troubleshoot an issue.

Datadog provides built-in log processing pipelines for many technologies (e.g., Apache, Docker, AWS Lambda). If your Fluent Bit configuration’s source parameter specifies that the logs come from one of these sources, Datadog’s pipeline will automatically extract key attributes, such as http.method, http.status_code, and timestamp. In the example below, we are filtering logs by source:apache so we can view all of the Apache logs routed by Fluent Bit.

View all of the logs collected by Fluent Bit in Datadog

Explore and analyze your logs in one place

You can use attributes from your logs to analyze trends in your applications. For example, if the Apache access logs you collect with Fluent Bit include processing time then you can monitor how long it takes for each of your instances to process requests. If you notice a spike in processing time for one of your servers, you can click on a datapoint to quickly view the logs that host generated within the same timeframe.

Analyze your Fluent Bit logs in Datadog

For services running in containerized environments, you can also pivot from your Fluent Bit logs to the Live Container view, where you can monitor resource metrics for each of your containers. You can filter by tags from your Fluent Bit configurations—such as cluster_name—in order to quickly drill down to the containers generating the logs, as seen in the example below.

Use the Live Container view to monitor the containers that generate your Fluent Bit logs

This helps you quickly view the status of all of your containers and identify which containers are consuming the most resources.

Route your logs to Datadog

With Datadog’s output plugin for Fluent Bit, you can instantly begin monitoring, analyzing, and storing all of your logs in Datadog. Check out our documentation to learn more about our Fluent Bit integration and how you can start routing your logs to Datadog. If you don’t have a Datadog account, you can sign up for a free 14-day trial.