Operations | Monitoring | ITSM | DevOps | Cloud

Google Cloud Next is the biggest event of the year for the Google Cloud community, showcasing the latest and greatest offerings from Google Cloud and hundreds of its partners. As a long-time Google Cloud partner and recipient of three Google Cloud Partner of the Year awards in 2025, Datadog was there in full force, delivering several speaking sessions and running a booth on the expo floor where we met with thousands of attendees. In case you missed it, don’t worry.

Datadog dashboards provide a unified view of your applications, infrastructure, logs, and other observability data—making it easy to monitor health, investigate issues, and share insights across teams. While native Datadog widgets support a broad range of visualization types, some use cases call for more customized representations, particularly when you’re working with unconventional data formats, external sources, or specific transformations.

Hallucinations occur when a large language model (LLM) confidently generates information that is false or unsupported. These responses can spread misinformation that jeopardizes safety, causes reputational damage, and erodes user trust. Augmented generation techniques, such as retrieval-augmented generation (RAG), aim to reduce hallucinations by providing LLMs with relevant context from verified sources and prompting the LLMs to cite these sources in their responses.

To gain adequate visibility into your distributed applications, you need to observe those applications at different levels of granularity. This means that you need to be able to query collected telemetry data both at the level of the whole application and at the level of selected components. Thanks to the power of Datadog tagging, you can already do this by aggregating your metrics within any scope of your choosing.

Security, operations, and development teams rely more and more on the ability to efficiently query logs. As these teams monitor the health, performance, and usage of their systems and investigate incidents, delving into log data can often be a matter of urgency.

As your organization scales its Datadog footprint, you want to understand what’s driving cost changes and promote cost awareness. But to take meaningful action, you need more than a monthly bill—you need real-time, contextualized cost data tied to services and teams. Without this visibility, it’s hard to assign ownership, prevent cost overruns, or identify which changes are affecting spend.

Before we started using Datadog Real User Monitoring (RUM), we relied on frontend logging to gather data about the user experience. Logs gave us some helpful information about exceptions and errors but didn't provide any insight into issues directly related to the user’s perspective.

Businesses deploying LLM workloads increasingly rely on LLM proxies (also known as LLM gateways) to simplify model integration and governance. Proxies provide a centralized interface across LLM providers, govern model access and usage, and apply compliance safeguards for smoother operations and reduced complexity—making LLM usage more consistent and scalable.

Finding the right integrations, libraries, and open source tooling to extend a product has long been a challenge for developers. While Datadog has a vast offering of monitoring and observability solutions, many teams need to customize their setup in some way—whether by extending the Datadog Agent, integrating with third-party services, or using SDKs to interact with the Datadog API.