GitLab is a DevSecOps platform that helps engineering teams automate software delivery. Using GitLab, teams can easily collaborate on projects and quickly deliver application code with robust CI/CD, security, and testing features. Datadog’s GitLab integration enables you to monitor your GitLab instances alongside the rest of your infrastructure by collecting GitLab metrics, logs, and service checks.

With the growing utilization of AI, modern business applications rely more and more on machine learning (ML) models. But the complexity of these models poses significant challenges to data scientists, engineers, and MLOps teams seeking to maintain and optimize performance.

Classless Inter-Domain Routing (CIDR) is the dominant IP addressing scheme in the modern web. By enabling network engineers to create subnets that encapsulate a set range of IP addresses, CIDR facilitates the flexible and efficient allocation of IPs in virtual private clouds (VPCs) and other networks.

When you first onboard to a monitoring and security platform, it can be difficult to know where to start. Which services should you monitor? What thresholds should you set? How often should you alert your team, and where’s the best starting point for investigations?

As a new Datadog customer, your top priority is figuring out how to maximize the platform’s potential and deliver value to your organization quickly and seamlessly. But with a plethora of options and configurations available at your disposal, it can be overwhelming to determine where to begin. With Datadog, you don’t need to be an expert in observability or monitoring to get up and running efficiently.

Testing enables you to proactively identify and resolve issues before they break critical functionality in your application, which is essential to ensuring an optimized user experience (UX). However, if you don’t know how users are actually interacting with your application, key user journeys may go untested. This lack of visibility can lead to a proliferation of unoptimized features in your UI, causing users to drop off before completing important actions.

Rising container usage has fueled a growing reliance on container orchestration systems such as Kubernetes, EKS, and ECS. As organizations increasingly opt to run these systems in the cloud, their cloud spend tends not only to grow but also to become more opaque due to the dynamic complexity of these environments. Typically, various services, teams, and products share cluster resources, and as nodes are added and removed, those resources continuously shift.

The dynamic nature of cloud costs can make it difficult to fully understand your cloud spend and embrace cost ownership at all levels of your organization. To establish cost governance, FinOps teams need a complete view of cloud costs, including allocation by team, service, and product. And DevOps teams need to detect, investigate, and quickly mitigate unexpected costs to minimize overruns, even as they continue to build features and operate their services.

Datadog provides you with a comprehensive and highly customizable platform for monitoring the performance and security of your applications. Through Datadog components deployed in your environment—including the Agent, tracing libraries, and Observability Pipelines workers—you can easily configure monitoring across your hosts and services, regardless of the particular technology you’re using.

Developer, SRE, IT, and security teams often perform complex and error-prone processes in response to disruptions and changes in their systems. Relying on these processes requires a significant amount of time switching between tools to gather the relevant context needed for remediation, domain expertise, and the manual execution of tasks for incident management—which can significantly prolong disruptions and downtime.