# datadog Published articles for datadog. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Manage Cursor costs with Datadog Cloud Cost Management DevFeed: [Manage Cursor costs with Datadog Cloud Cost Management]() Original publisher: [Read original article]() Author: Dom Nguyen; Doug Gunter Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [cursor](), [finops](), [ai-coding]() Tags: [ai](), [ai-coding](), [analysis](), [cloud-cost-management](), [configuration](), [datadog](), [features](), [filter](), [finops](), [monitor](), [monitors](), [product](), [software]() ### AI overview This tutorial explains how Datadog Cloud Cost Management helps teams analyze Cursor spending by user, model, usage mode, and billing group. It covers identifying cost drivers, detecting unexpected changes, correlating spend with usage, and using monitors, budgets, and dashboards to manage AI coding costs alongside cloud and SaaS spending. ### Source excerpt Analyze Cursor spend by user and model, catch unexpected cost changes, and manage AI coding costs alongside your cloud and SaaS spend. ## Monitor TAS and gang scheduling for AI training in Kubernetes DevFeed: [Monitor TAS and gang scheduling for AI training in Kubernetes]() Original publisher: [Read original article]() Author: David Lentz; Kathy Lin Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kubernetes](), [kueue](), [distributed-training](), [GPU](), [Monitoring](), [datadog]() Tags: [ai](), [batch](), [containers](), [datadog](), [distributed-training](), [gpu](), [gpu-monitoring](), [kubernetes](), [kueue](), [monitoring](), [scheduler]() ### AI overview This article explains why Kubernetes scheduling is insufficient for distributed AI training workloads and how topology-aware scheduling and gang scheduling address hardware placement and simultaneous startup requirements. It discusses implementing these capabilities with Kueue and the Coscheduling plugin, and monitoring and troubleshooting them with Datadog GPU Monitoring. ### Source excerpt Learn how Datadog helps you correlate Kueue, Coscheduling, GPU, and training framework signals to validate gang scheduling and topology-aware scheduling. ## Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report DevFeed: [Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report]() Original publisher: [Read original article]() Author: Yanbing Li Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [agent observability](), [observability ai agents](), [incident](), [MCP Server](), [observability pipelines](), [OpenTelemetry](), [Security](), [Artificial Intelligence]() Tags: [agent-observability](), [ai-agents](), [datadog](), [incident-response](), [mcp-server](), [observability](), [observability-pipelines](), [opentelemetry]() ### AI overview Datadog says it was named the Company to Beat for observability platforms in Gartner's August 2026 AI Vendor Race research and a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The article presents Datadog's unified observability and security platform, including autonomous incident investigation, AI agent and LLM application observability, an MCP Server for querying telemetry, and Observability Pipelines with OpenTelemetry support. ### Source excerpt Datadog has been recognized as the Company to Beat for observability platforms in the August 2026 Gartner® AI Vendor Race research. ## How engineering teams can measure AI coding ROI with cost attribution and outcome metrics DevFeed: [How engineering teams can measure AI coding ROI with cost attribution and outcome metrics]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [ai-coding](), [Ground truth / benchmark quality](), [coding](), [Finance]() Tags: [ai](), [ai-coding](), [analysis](), [blog](), [coding](), [cost](), [datadog](), [engineering](), [governance](), [harness](), [incident](), [lifecycle](), [measurement](), [metrics](), [numbers](), [production](), [team](), [tokens](), [tooling](), [workflow]() ### AI overview A Harness panel discussion outlines ways engineering organizations can measure AI coding ROI. The panel recommends attributing costs to specific work, combining throughput metrics with qualitative evidence, and using governance and process changes to interpret results. ### Source excerpt Harness panelists share practical strategies for measuring AI coding ROI, from granular cost attribution and lifecycle metrics to budgeting, governance. | Blog ## WorkOS Applied AI Showcase July 28 2026 - Moving to an AI-native Organization DevFeed: [WorkOS Applied AI Showcase July 28 2026 - Moving to an AI-native Organization]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-14T13:36:57Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Low-Code / Internal Tools](), [AI-assisted coding](), [Containers](), [MCP](), [Command-line interface](), [incident](), [Security](), [datadog](), [Pull Request](), [Slack](), [GitHub]() Tags: [agent](), [agents](), [ai](), [applied-ai](), [cli](), [coding](), [coding-agents](), [containers](), [datadog](), [demo](), [event](), [github](), [incident](), [internal-tools](), [mcp](), [organization](), [pull-request](), [slack](), [talks](), [team]() ### AI overview WorkOS's Applied AI Showcase in New York presented five internal tools--Horizon, Blog Bot, the CLI, Wallaby, and Atlas--through live demos and talks. The article explains how the Applied AI team helps non-engineering functions build AI-powered workflows and systems. ### Source excerpt WorkOS is becoming an AI-native organization. The Applied AI Showcase in NYC on July 28th 2026 showed exactly how with live demos and talks from the Applied AI team. ## AI SRE: Cut MTTR in Half with Autonomous Incident Resolution DevFeed: [AI SRE: Cut MTTR in Half with Autonomous Incident Resolution]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:34:38Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [site-reliability-engineering](), [incident](), [Artificial Intelligence](), [monitor](), [dashboards](), [Traces](), [archive search](), [Pull Request](), [codex](), [cursor]() Tags: [agent](), [ai](), [codex](), [cursor](), [dashboards](), [datadog](), [incident](), [monitor](), [monitoring](), [on-call](), [sre](), [traces](), [workflow]() ### AI overview This developer article describes an autonomous incident-resolution workflow built in Port. It argues that incident response time is largely spent reconstructing context from alerts, logs, dashboards, traces, ownership data, recent changes, and runbooks. The proposed agent-based workflow aims to use that full context to triage, diagnose, and fix production incidents, with a reported 50% reduction in MTTR. The supplied text ends during a discussion of why simply routing alerts to coding agents can fail. ### Source excerpt See how an SRE agent workflow cuts MTTR in half, using full context to triage, diagnose, and fix incidents autonomously, all the way to a RCA. ## Automate Incident Intake with AI SRE Runbooks DevFeed: [Automate Incident Intake with AI SRE Runbooks]() Original publisher: [Read original article]() Author: Ryan Taylor Published: 2026-08-10T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [SRE](), [incident](), [Automation](), [Slack](), [zoom](), [Grafana](), [datadog](), [Artificial Intelligence]() Tags: [ai](), [automation](), [build](), [datadog](), [grafana](), [harness](), [incident](), [integrations](), [jira](), [open](), [services](), [slack](), [sre](), [workflow](), [zoom]() ### AI overview This article explains how Harness AI SRE runbooks automate incident intake and early response. Triggered by alerts, manual actions, or incident changes, a runbook can create tickets, open Slack channels, start Zoom bridges, set incident fields, and record actions in the incident timeline. ### Source excerpt Automate incident intake with Harness AI SRE runbooks: auto-create tickets, open Slack channels, start Zoom bridges, and cut response time to seconds. | Blog ## Audit Log Drains now support Datadog, Splunk, and Panther DevFeed: [Audit Log Drains now support Datadog, Splunk, and Panther]() Original publisher: [Read original article]() Author: Nate McGrady Published: 2026-08-07T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [SIEM, Security](), [Streaming](), [Amazon S3](), [SIEM, Security, Observability]() Tags: [amazon-s3](), [datadog](), [documentation](), [enterprise](), [integration](), [logs](), [migration](), [migration-guide](), [s3](), [streaming]() ### AI overview Vercel Audit Log Drains can stream team audit events and metadata to Datadog, Splunk, or Panther, in addition to custom HTTPS endpoints and Amazon S3. The feature is available on Enterprise plans and replaces Custom SIEM Log Streaming. ### Source excerpt Audit Log Drains now stream your team's audit events into Datadog, Splunk, and Panther, joining the existing custom HTTPS endpoint and Amazon S3 destinations. An Audit Log Drain forwards every event from your team's Activity Log, plus additional audit metadata, to the destination you choose. They're available on Enterprise plans. To create one, go to Drains in your team settings. Click Add Drain, choose Audit Log as the data type, and pick a destination. Audit Log Drains replace Custom SIEM Log Streaming. If you already stream audit logs to a SIEM, follow the migration guide to move your integration over. Learn more about Drains in the documentation. Read more ## Durable Digest: July highlights DevFeed: [Durable Digest: July highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [releases](), [distributed-systems](), [Cloud Run](), [Langgraph](), [datadog](), [API]() Tags: [announcements](), [api](), [cloud](), [cloud-run](), [datadog](), [distributed-systems](), [explore](), [langgraph](), [releases]() ### AI overview Temporal's July Durable Digest summarizes product releases and updates, including pre-release Serverless Workers for GCP Cloud Run, public-preview LangGraph and LangSmith integrations, generally available billing and metrics APIs, Datadog and Vantage integrations, and improvements to the Worker Status UI. ### Source excerpt Highlights from July include major product releases that make it easier to build and operate durable applications, and enhanced visibility into your Workers. ## Improved cost transparency with usage-based billing DevFeed: [Improved cost transparency with usage-based billing]() Original publisher: [Read original article]() Author: Markus Sample Published: 2026-06-25T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [API](), [cloud cost management](), [Cloud](), [observability](), [Monitoring]() Tags: [api](), [cloud](), [cloud-cost-management](), [cost](), [datadog](), [metrics](), [observability](), [product-news](), [usage-based-billing]() ### AI overview Temporal announces general availability of the Temporal Cloud Billing API and Billable Action Count metric, providing more granular cost attribution, usage data, reporting, and optimization signals. ### Source excerpt Learn how the Temporal Cloud Billing API and Billable Action Count metric give teams clearer cost attribution, usage trends, and optimization signals. ## AI-Driven Development Should Focus on Product Value, Not Just Faster Shipping DevFeed: [AI-Driven Development Should Focus on Product Value, Not Just Faster Shipping]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-06-10T12:27:59Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [AI Development](), [product analytics](), [session replay](), [experiments](), [datadog](), [Software Engineering](), [ITMO]() Tags: [agentic-ai](), [ai](), [analytics](), [datadog](), [product-analytics](), [session-replay](), [software-engineering]() ### AI overview The article argues that AI-assisted development should be judged by the value it creates for customers and businesses, not simply by shipping speed. It contrasts new products with existing products that have customers, legacy constraints, churn concerns, and growth goals, and suggests combining product analytics, session replay, and experiments to inform roadmaps. ### Source excerpt The world is getting weird. ## Port + Datadog: The Missing Layer in Self-Healing Infrastructure DevFeed: [Port + Datadog: The Missing Layer in Self-Healing Infrastructure]() Original publisher: [Read original article]() Author: Vic Agudelo Published: 2026-06-09T12:37:14Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [observability](), [Monitoring](), [incident](), [Architecture & Design](), [Security]() Tags: [ai-agents](), [datadog](), [incident](), [infrastructure](), [monitoring](), [observability]() ### AI overview The article presents Port as an organizational context layer for Datadog and other observability tools. It argues that monitoring detects incidents accurately but lacks ownership, deployment, runbook, security, and service-history context needed for automated resolution. Port's Context Lake integrates Datadog data with this organizational knowledge to support faster triage and self-healing infrastructure. ### Source excerpt Datadog sees everything. It knows latency is spiking on shopping-cart-api-v2. It knows the error rate crossed the SLO threshold 47 seconds ago. What it doesn't always know is who owns this service, what changed in the last deploy, whether there's an open CVE tied to this repo, what the runbook says to do, or whether this is the third time this week the same scaling pattern has been triggered. ## Temporal Cloud and Datadog integration adds pre-built metrics dashboards and standard-metrics monitoring DevFeed: [Temporal Cloud and Datadog integration adds pre-built metrics dashboards and standard-metrics monitoring]() Original publisher: [Read original article]() Author: Dustin Cote Published: 2026-05-19T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [datadog](), [observability](), [dashboards](), [monitor](), [Cloud](), [reliability](), [API](), [API keys](), [Latency](), [Replication]() Tags: [announcements](), [api](), [cloud](), [cost](), [dashboard](), [datadog](), [latency](), [metrics](), [monitoring](), [observability](), [reliability](), [replication](), [setup]() ### AI overview Temporal Cloud and Datadog now offer an integration with a pre-built metrics dashboard, simplified setup, and more than 30 metrics for monitoring workflow performance, system health, and reliability. The metrics are classified as standard metrics in Datadog, which may reduce costs compared with custom metrics ingestion. ### Source excerpt Monitor Temporal Cloud with Datadog metrics. Gain deeper visibility into workflow performance and reliability. ## GraphOS Router APM Dashboard Templates for Datadog DevFeed: [GraphOS Router APM Dashboard Templates for Datadog]() Original publisher: [Read original article]() Author: Matthew Ratzke Published: 2025-10-07T07:00:47Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphOS](), [datadog](), [Application Performance Management (APM)](), [observability](), [telemetry](), [tracing](), [GraphQL]() Tags: [announcement](), [apm](), [datadog](), [graphos](), [graphql](), [instrumentation](), [observability](), [telemetry](), [tracing]() ### AI overview Apollo is launching APM dashboard templates for Datadog that provide platform and SRE teams with observability views for GraphOS Router performance. The templates map Router spans and metrics into Datadog, expose GraphQL errors, support router-to-subgraph drill-down, and correlate performance with deployments and versions. ### Source excerpt Today we're launching APM dashboard templates for Datadog, so platform and SRE teams can get best practices observability into GraphOS Router performance in just minutes. Previously teams would need to determine the important information to monitor, what telemetry is available, and then how to configure the dashboards. This could take hours or days of an engineers time to initially set up and refine over time. ## Setting up Redpanda observability in Datadog DevFeed: [Setting up Redpanda observability in Datadog]() Original publisher: [Read original article]() Author: Kavya Shivashankar Published: 2025-08-27T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [Setting up Redpanda observability in Datadog](), [observability](), [datadog agent](), [dashboards](), [Docker Compose](), [Streaming](), [Raft](), [Cloud]() Tags: [anomaly-detection](), [configure-redpanda-on-datadog](), [dashboards](), [datadog](), [datadog-agent](), [datadog-agent-for-redpanda](), [datadog-anomaly-detection-redpanda](), [datadog-dashboards-for-redpanda](), [docker](), [integration](), [monitor-redpanda-with-datadog](), [observability](), [prometheus](), [raft](), [redpanda-cluster-monitoring](), [redpanda-connect](), [redpanda-datadog-integration](), [redpanda-docker-setup](), [redpanda-logging-with-datadog](), [redpanda-metrics-in-datadog](), [redpanda-performance-monitoring](), [setting-up-redpanda-observability](), [setting-up-redpanda-observability-in-datadog](), [streaming]() ### AI overview This guide explains how to integrate Datadog with Redpanda to collect, visualize, and analyze cluster metrics and broker logs. It covers Datadog dashboards, alerts, anomaly detection, the Datadog Agent, and a Docker-based single-node setup. ### Source excerpt A comprehensive guide to integrating the Datadog Agent with Redpanda for data collection, metric scraping, and tapping into Datadog's powerful dashboards. ## Enabling platform engineering with Temporal: Five practical use cases DevFeed: [Enabling platform engineering with Temporal: Five practical use cases]() Original publisher: [Read original article]() Author: Cornelia Davis Published: 2025-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Platform Engineering](), [Automation](), [incident](), [Incident response](), [certificates](), [Kubernetes](), [CI/CD](), [datadog]() Tags: [certificates](), [ci-cd](), [datadog](), [engineering](), [how-to](), [idp](), [incident](), [incident-response](), [infrastructure](), [kubernetes](), [platform](), [platform-engineering]() ### AI overview This article presents five practical use cases for using Temporal to automate platform engineering workflows, including incident response, certificate rotation, infrastructure management, CI/CD, and internal developer platform workflows. It explains how fault-tolerant workflows can replace fragile scripts and manual processes with durable automation, human approvals, handoffs, and auditable execution. ### Source excerpt Learn how platform engineering teams automate incident response, certificate rotation, infrastructure management, CI/CD, and IDP workflows with Temporal. ## Announcing the Apollo GraphOS Operator in Public Preview DevFeed: [Announcing the Apollo GraphOS Operator in Public Preview]() Original publisher: [Read original article]() Author: Josh Lambert Published: 2025-08-05T06:00:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [Kubernetes](), [API Platform](), [Amazon API Gateway](), [GitOps](), [Deployment](), [observability]() Tags: [announcement](), [api](), [api-management](), [api-platform](), [apollo](), [argo-cd](), [datadog](), [declarative](), [deployment](), [gitops](), [graphos](), [graphql](), [kubernetes](), [observability](), [operator](), [preview]() ### AI overview Apollo announced the Apollo GraphOS Operator in public preview. The Kubernetes operator is designed to help teams manage GraphQL deployments, including development environments, progressive production rollouts, rollbacks, monitoring, and declarative or GraphOS Studio-based management. ### Source excerpt Simplifying GraphQL API Management on Kubernetes As GraphQL adoption accelerates within organizations, so too does the need for a scalable, reliable API platform that empowers your developers and accelerates innovation. Today, we're excited to introduce in public preview the Apollo GraphOS Operator: the best way to manage GraphQL deployments within Kubernetes. ## OpenTelemetry, Kubernetes, and Fir: Putting it All Together DevFeed: [OpenTelemetry, Kubernetes, and Fir: Putting it All Together]() Original publisher: [Read original article]() Author: Ken W. Alger Published: 2025-07-09T15:00:13Z Content type: article Language: en Sources: [Heroku]() Topics: [OpenTelemetry](), [Kubernetes](), [Heroku](), [observability](), [telemetry](), [Cloud](), [Microservice]() Tags: [alternatives](), [buildpacks](), [cloud-infrastructure](), [datadog](), [engineering](), [fir](), [heroku](), [jaeger](), [kubernetes](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [sdks](), [telemetry]() ### AI overview This article explains how Heroku's Fir cloud platform uses Kubernetes for automated scaling, self-healing, and resource management while exposing a Heroku interface to users. It also describes how OpenTelemetry standardizes telemetry collection and enables distributed tracing, performance diagnosis, and routing data to different monitoring backends without vendor lock-in. ### Source excerpt Modern cloud-native architectures are composed of multiple microservices running across dynamic environments. Effectively diagnosing performance issues, bottlenecks, or failures requires comprehensive observability. For this, many organizations look to OpenTelemetry, which provides a standardized approach to capturing and analyzing telemetry data. Fir is Heroku's next generation cloud platform, designed to offer more modern cloud-native capabilities with [...] The post OpenTelemetry, Kubernetes, and Fir: Putting it All Together appeared first on Heroku. ## How We Export Postgres Logs to Third-Party Services DevFeed: [How We Export Postgres Logs to Third-Party Services]() Original publisher: [Read original article]() Author: Roman Zaynetdinov Published: 2025-06-24T16:27:45Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Logging](), [OpenTelemetry](), [log management](), [Kubernetes](), [observability](), [Protocol (disambiguation)]() Tags: [architecture](), [blog](), [blog-post](), [company](), [config](), [configuration](), [datadog](), [implementation](), [kubernetes](), [logging](), [logs](), [metrics](), [observability](), [opentelemetry](), [platform](), [postgres](), [product](), [tcp](), [third-party]() ### AI overview The article explains how Postgres logs are exported to third-party observability platforms. Because the OpenTelemetry Collector could not scrape Kubernetes pod logs by labels and NeonVM contains multiple processes, the implementation uses a push-based design: Postgres sends selected logs through TCP syslog to an OpenTelemetry Collector, which enriches them with Kubernetes metadata and forwards them to Datadog or another destination. ### Source excerpt We've been supporting metrics export to Datadog for a few months, and we just extended this capability to any OLTP-compatible third party - but it has taken us much longer to ship any sort of Postgres log export. You might wonder: why did it take so long? The question is valid, a... ## Partitioning a large table in PostgreSQL with Rails DevFeed: [Partitioning a large table in PostgreSQL with Rails]() Original publisher: [Read original article]() Published: 2025-04-28T00:00:00Z Content type: tutorial Language: en Sources: [Aha! Engineering Blog]() Topics: [PostgreSQL](), [Rails](), [Database](), [Amazon RDS](), [backups](), [Amazon S3](), [etl](), [Disaster Recovery](), [datadog]() Tags: [active-record](), [amazon-rds](), [amazon-s3](), [backups](), [datadog](), [disaster-recovery](), [etl](), [latency](), [migration](), [partitioning](), [performance](), [postgresql](), [rails]() ### AI overview This article explains how Aha! partitioned a very large PostgreSQL audit table used with Rails. It covers the migration strategy, operational and performance problems caused by billions of rows, and an archival process that moves audits older than 12 months to Amazon S3. ### Source excerpt Keeping a reliable history of changes is essential for our users. They need to know when a record was updated, who made the change, and why. They could be anxiously awaiting a notification about a dependency that is blocking their work. Maybe they w ## Last9 + ClickHouse: Delivering Seamless Observability, Minus the Chaos DevFeed: [Last9 + ClickHouse: Delivering Seamless Observability, Minus the Chaos]() Original publisher: [Read original article]() Author: Aditya Godbole, CTO of Last9 Published: 2025-04-14T15:02:14Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [log management](), [tracing](), [distributed-systems](), [Microservice](), [DevOps](), [Cloud]() Tags: [clickhouse](), [cloud](), [datadog](), [devops](), [distributed-systems](), [logs](), [metrics](), [microservices](), [observability](), [prometheus](), [telemetry](), [traces]() ### AI overview Learn how Last9 uses ClickHouse to unify logs, metrics, and traces into a centralized observability platform designed to make telemetry analysis faster, more cost-effective, and scalable. ### Source excerpt Learn how Last9 uses ClickHouse to unify logs, metrics, and traces into a single observability platform that's fast, cost-efficient, and built for scale. ## Key Takeaways from Chainguard Assemble 2025 DevFeed: [Key Takeaways from Chainguard Assemble 2025]() Original publisher: [Read original article]() Published: 2025-03-27T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [chainguard libraries](), [supply-chain-security](), [chainguard containers](), [open-source-security](), [chainguard os](), [chainguard vms](), [Open Source](), [Security]() Tags: [2025](), [assemble-2025](), [chainguard](), [chainguard-assemble](), [chainguard-containers](), [chainguard-libraries](), [chainguard-os](), [chainguard-vms](), [datadog](), [open-source](), [oz-pearlman](), [secure-software](), [software-development](), [software-supply-chain-security]() ### AI overview Chainguard Assemble 2025 brought together security and engineering professionals to discuss secure software development, software supply chain security, and open source software. Chainguard highlighted Chainguard Containers, Chainguard Libraries, Chainguard VMs, and Chainguard OS, while also announcing partnerships and providing access to keynote and selected session recordings. ### Source excerpt Chainguard announced Chainguard Libraries, Chainguard VMs, and a new partnership with Datadog at Assemble, our inaugural event for security and developer pros. ## Chainguard and Datadog's New Partnership: Actionable Insights and Observability Come Together DevFeed: [Chainguard and Datadog's New Partnership: Actionable Insights and Observability Come Together]() Original publisher: [Read original article]() Published: 2025-03-25T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [observability](), [dashboards](), [container images](), [Containers](), [Vulnerabilities](), [supply-chain-security](), [Security & compliance, Cloud security]() Tags: [2025](), [assemble-2025](), [chainguard](), [chainguard-containers](), [container-images](), [containers](), [dashboards](), [datadog](), [observability](), [partnership](), [shift-left](), [software-supply-chain](), [vulnerabilities]() ### AI overview Chainguard and Datadog announced a partnership that provides joint customers with a Chainguard dashboard in Datadog. The dashboard gives teams visibility into deployed Chainguard container images, infrastructure performance, replacement progress, and containers with the highest vulnerability counts, helping them prioritize risk reduction. ### Source excerpt Chainguard and Datadog announced a new partnership at Chainguard Assemble 2025. Learn more about what this partnership enables for customers. ## Building a Datadog Alternative: Alerts, Dashboards, and Retention Policies DevFeed: [Building a Datadog Alternative: Alerts, Dashboards, and Retention Policies]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-02-26T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [observability](), [dashboards](), [datadog]() Tags: [build](), [dashboards](), [datadog](), [i-built-this](), [observability](), [retention](), [scale]() ### AI overview A tutorial on building a Datadog alternative by adding alerts, dashboards, and retention policies to an observability stack. ### Source excerpt Build a Datadog alternative part 2: add alerts, dashboards, and retention policies. Complete your observability stack today. [Next page]()