Operations | Monitoring | ITSM | DevOps | Cloud

Web applications emit a wealth of metadata and user interaction information that’s critical to understanding user behavior. However, parsing this data to find what is most relevant to your product analytics project can be challenging—what one product analyst might find useful, another might consider unnecessary noise.

Custom resources are critical components in Kubernetes production environments. They enable users to tailor Kubernetes resources to their specific applications or infrastructure needs, automate processes through operators, simplify the management of complex applications, and integrate with non-native applications such as Kafka and Elasticsearch.

While overprovisioning Kubernetes workloads can provide stability during the launch of new products, it’s often only sustainable because large companies have substantial budgets and favorable deals with cloud providers. As highlighted in Datadog’s State of Cloud Costs report, cloud spending continues to grow, but a significant portion of that cost is often due to inefficiencies like overprovisioning.

Kubernetes offers the ability to scale infrastructure to accommodate fluctuating demand, enabling organizations to maintain availability and high performance during surges in traffic and reduce costs during lulls. But scaling comes with tradeoffs and must be done carefully to ensure teams are not over-provisioning their workloads or clusters. For example, organizations often struggle with overprovisioning in Kubernetes and wind up paying for resources that go unused.

As organizations increasingly turn to Kubernetes to support their cloud-native applications, it has become critical for teams to analyze and respond to the dense telemetry data related to this orchestration layer.

Azure AI Search is Microsoft Azure’s managed search service. In addition to tackling traditional search use cases, Azure AI Search also includes AI-powered features to make it a fully capable document catalog, search engine, and vector database. AI Search is highly interoperable—it can use models created in Azure OpenAI Service, Azure AI Studio, or Azure ML.

As teams adopt Kubernetes at greater scale, they face increased complexity in keeping their growing list of workloads and services up and running. Achieving the visibility and context needed to detect and resolve incidents quickly is difficult amid a constant flood of telemetry data and alerts. Furthermore, Kubernetes expertise often remains siloed in DevOps and infrastructure teams.

As federal, state, and local government agencies work to modernize their digital infrastructure and applications, managing costs effectively remains a constant challenge. Federal directives like Cloud Smart indicate the need for public sector IT organizations to track and optimize their cloud spends. However, as an organization’s IT environment grows in complexity, it becomes difficult to correlate cost data and extract useful insights.

LLMs like GPT-4, Claude, and Llama are behind popular tools like intelligent assistants, customer service chatbots, natural language query interfaces, and many more. These solutions are incredibly useful, but they are often constrained by the information they were trained on. This often means that LLM applications are limited to providing generic responses that lack proprietary or context-specific knowledge, reducing their usefulness in specialized settings.

On the October episode of This Month in Datadog, Jeremy Garcia (VP of Technical Community and Open Source) covers unified Error Tracking, Security Operational Metrics, and a new Datadog Serverless feature for retrying or redriving failed AWS Step Functions executions directly from Datadog. Later in the episode, Shri Subramanian (Group Product Manager) spotlights Datadog LLM Observability’s native integration with Google Gemini. Also featured are our blog posts Operator vs.