Datadog announced new capabilities and integrations for Microsoft Azure, including support for Azure OpenAI Service, Azure Arc, Cloudcraft for Azure and Cloud Cost Management for Azure.
With added support for Azure Arc—along with Azure OpenAI Service, Cloudcraft and Cloud Cost Management—Datadog is an observability partner that equips Azure organizations to migrate confidently to the cloud, manage costs and monitor AI models.
“Organizations that are investing in generative AI applications built on Microsoft Azure demand access to reliable and accurate performance and cost data,” said Yrieix Garnier, VP of Product at Datadog. “Our new integration with Azure OpenAI Service helps organizations track token consumption so they can understand the primary cost drivers for OpenAI usage.”
“Datadog continues to support cloud observability on the Microsoft Azure cloud platform. Datadog’s latest suite of releases enhances enterprise-wide cloud visibility into Azure and augments Microsoft Cost Management data with observability data. Datadog is a trusted Microsoft partner in helping organizations plan, monitor and accelerate their cloud journeys,” said Tony Surma, CTO of US Partners at Microsoft.
Datadog’s newest capabilities for Azure include:
- Support for Azure Arc: Within the Datadog platform, customers can use tags and other critical metadata to baseline performance pre-migration and monitor Arc connection status post-migration.
- Cloudcraft by Datadog: Azure users can now design enterprise-wide architectural best practices and create real-time diagrams of cloud architectures. Cloudcraft’s drag-and-drop diagram designer, live environment scanner and automatic budget generator make it easy for teams to discover, plan and diagram their cloud architectures.
- Cloud Cost Management for Azure: In today’s macro-economic environment, cloud infrastructure teams need to present cost data to application teams in a meaningful and digestible way. Cloud Cost Management enables application teams to contextualize performance and cost, side-by-side, in a single dashboard. This drives optimum performance, while minimizing wasted resources.
- Integration with Azure OpenAI Service: Datadog’s new integration provides immediate insights into requests, latency and token consumption of Azure OpenAI Service. This enables customers to better optimize costs, troubleshoot issues and monitor performance of their AI-powered applications, including those that leverage ChatGPT.
The Latest
Monitoring your cloud infrastructure on Microsoft Azure is crucial for maintaining its optimal functioning ... In this blog, we will discuss the key aspects you need to consider when selecting the right Azure monitoring software for your business ...
All eyes are on the value AI can provide to enterprises. Whether it's simplifying the lives of developers, more accurately forecasting business decisions, or empowering teams to do more with less, AI has already become deeply integrated into businesses. However, it's still early to evaluate its impact using traditional methods. Here's how engineering and IT leaders can make educated decisions despite the ambiguity ...
2024 is the year of AI adoption on the mainframe, according to the State of Mainframe Modernization Survey from Kyndryl ...
When employees encounter tech friction or feel frustrated with the tools they are asked to use, they will find a workaround. In fact, one in two office workers admit to using personal devices to log into work networks, with 32% of them revealing their employers are unaware of this practice, according to Securing the Digital Employee Experience ...
In today's high-stakes race to deliver innovative products without disruptions, the importance of feature management and experimentation has never been more clear. But what strategies are driving success, and which tools are truly moving the needle? ...
The demand for real-time AI capabilities is pushing data scientists to develop and manage infrastructure that can handle massive volumes of data in motion. This includes streaming data pipelines, edge computing, scalable cloud architecture, and data quality and governance. These new responsibilities require data scientists to expand their skill sets significantly ...
As the digital landscape constantly evolves, it's critical for businesses to stay ahead, especially when it comes to operating systems updates. A recent ControlUp study revealed that 82% of enterprise Windows endpoint devices have yet to migrate to Windows 11. With Microsoft's cutoff date on October 14, 2025, for Windows 10 support fast approaching, the urgency cannot be overstated ...
In Part 1 of this two-part series, I defined multi-CDN and explored how and why this approach is used by streaming services, e-commerce platforms, gaming companies and global enterprises for fast and reliable content delivery ... Now, in Part 2 of the series, I'll explore one of the biggest challenges of multi-CDN: observability.
CDNs consist of geographically distributed data centers with servers that cache and serve content close to end users to reduce latency and improve load times. Each data center is strategically placed so that digital signals can rapidly travel from one "point of presence" to the next, getting the digital signal to the viewer as fast as possible ... Multi-CDN refers to the strategy of utilizing multiple CDNs to deliver digital content across the internet ...