Operations | Monitoring | ITSM | DevOps | Cloud

As your organization scales, managing observability resources and usage becomes increasingly important. More users and teams mean more dashboards, tags, API keys, and costs to manage. The job of keeping track of these resources and ensuring that they’re compliant can quickly grow in complexity.

In collecting and analyzing trillions of events each day, Datadog ingests a massive amount of data. We spend substantially to process and store this data in the cloud, and teams across the organization are committed to optimizing the return on this investment. To this end, our FinOps analysts have always tracked the costs of delivering our services and identified opportunities for savings.

A growing number of engineering organizations have adopted or are trialing agentic AI-based coding tools and LLMs in an effort to increase their teams’ development velocity. If you’re a developer, this means you’ve likely had to try out different agentic tools and models and determine how to best incorporate them into your existing workflows.

Managing the cost of foundation models is a critical challenge as AI adoption surges, particularly for teams using powerful models like Anthropic's Claude Opus and Claude Sonnet. Growing teams generate larger prompt volumes and escalating model complexity, making it difficult to have clear visibility, accountability, and control of cloud AI spending.

In Microsoft-based environments, Windows event logs capture critical security events like user logins, privilege escalations, and system changes. These logs are vital for compliance and investigations. However, they’re natively formatted in XML, a verbose and deeply nested structure that is hard to search without preprocessing and inefficient to store.

Serverless platforms like Azure Functions and Azure Container Apps make it easier to scale your applications without managing infrastructure. But successful serverless apps require thoughtful planning. They must be designed to account for cold starts, unpredictable scaling behavior, and ephemeral compute lifecycles, all while ensuring secure data handling and end-to-end observability across highly distributed components.

To improve efficiency and reduce cloud spending, teams frequently schedule pods on Kubernetes nodes dynamically, based on available resources. However, this practice has also introduced a new security challenge: The workloads maintained by a development team are now spread between Kubernetes nodes, exposing more hosts and increasing the blast radius when user credentials are compromised.

Asynchronous communication patterns are commonly used in distributed systems, especially in those that rely on events or messages to coordinate activity. Rather than responding to direct API calls like in a traditional request-response architecture, services in an asynchronous system produce, route, or consume events and messages independently.

Mobile applications offer increased flexibility to both users and developers. Users can access content on a wide range of devices, operating systems, and network types, while developers can leverage touch screens and orientation-based layouts to create more responsive features. However, all of these factors create new testing challenges. To ensure a good user experience (UX), developers have to test their apps across many device models and platforms, which can become costly and time-consuming.

Modern infrastructure is built and deployed faster than ever, but increased speed can elevate risk. Developers who work on cloud-native applications often use infrastructure as code (IaC) to define cloud resources in configuration files, which are then shared across teams and deployed automatically. Although this approach is efficient, undetected misconfigurations in IaC can quickly introduce security risks into production environments.