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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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Monitor Kong with our new Datadog integration
2016-06-17 · via Datadog | The Monitor blog

This is a guest post from Shashi Ranjan, Backend Engineer at Mashape.

Mashape is excited to announce our partnership with Datadog. Through an integration with Kong, the most widely used open source API management platform, everyone in our community can now monitor Kong’s usage and performance metrics with Datadog. Kong features a plugin-oriented architecture which allows you to set up authentication, transformations, security, and manage traffic controls.

With the Datadog plugin you can log API metrics like request count, request size, response status and latency to the local Datadog Agent. Additionally, Datadog’s Agent has been updated to collect Kong’s connection and database details.

Configuration

Datadog Agent configuration

To connect Kong with the Datadog Agent follow these instructions. Once Agent is configured and running, metrics will begin flowing to Datadog immediately.

Kong Plugin

To collect the full set of metrics available for monitoring Kong, you’ll also want to configure the Kong plugin, which is straightforward. You can add it on top of an API (or Consumer) by executing the following request on your Kong server:

$ curl -X POST http://kong:8001/apis/{api}/plugins \

--data "name=datadog" \

--data "config.host=127.0.0.1" \

--data "config.port=8125" \

--data "config.timeout=1000" \

ParameterDescription
api(Part of the URL.) The id or name of the API that this plugin configuration will target.
nameThe name of the plugin to use, in this case: datadog.
consumer_id optionalThe CONSUMER ID that this plugin configuration will target. This value can only be used if authentication has been enabled so that the system can identify the user making the request.
config.host optionalDefault 127.0.0.1. The IP address or host name to send data to.
config.port optionalDefault 8125. The port to send data to on the upstream server.
config.metrics optionalThe metrics to be logged, by default all are logged. Available values are described at Metrics.
config.timeout optionalDefault 10000. Timeout in milliseconds when sending data to the upstream server.

Metrics

Once the Datadog Agent and Kong plugin are set up, the following metrics will be available in Datadog for visualization, alerting, and correlation with any part of your stack.

NameDescription
kong.< api_name > .request.countThe count of the requests made to the API
kong.< api_name >.request_sizeThe request’s body size in bytes
kong.< api_name >.response_sizeThe response’s body size in bytes
kong.< api_name>.latencyThe time interval between the request started and response received from the upstream server
kong.< api_name >.< http_status_code >.countThe number of times each status code has been returned
kong.< api_name >.user.uniquesThe number of users that have made a request to the API
kong.< api_name >.< consumer_id >.countThe number of requests each user has made.
kong.connections_acceptedTotal number of accepted client connections.
kong.connections_activeCurrent number of active client connections including Waiting connections.
kong.connections_handledTotal number of handled connections. (Same as accepts unless resource limits were reached).
kong.connections_readingCurrent number of connections where Kong is reading the request header.
kong.connections_waitingCurrent number of idle client connections waiting for a request.
kong.connections_writingCurrent number of connections where nginx is writing the response back to the client.
kong.table.countTotal number of tables in the database.
kong.table.itemsNumber of items in each table of the database.
kong.total_requestsTotal number of client requests.

Datadog + Kong just makes sense

The popularity of Datadog made it a natural fit as a Kong plugin. Through a suite of API logging metrics and tools built for small cloud teams to enterprise, Datadog has you covered.

We’re excited to be partnering with Datadog and know our relationship will provide developers better API management. Try the plugin today and experience the best of Kong and Datadog.

Editor’s note: if you already use Kong but not Datadog, you can get a free Datadog account here.