Elastic announced a new integration with Azure AI Foundry, delivering observability for agentic AI applications and large language models (LLMs).
The integration provides site reliability engineers (SREs) and developers with real-time insights into LLMs, generative AI and agentic AI workloads, enabling them to build, monitor, and optimize intelligent agents on Azure AI Foundry with greater reliability and efficiency while also including guardrails.
As organizations adopt agentic AI for mission-critical applications, they face challenges such as uncontrolled token usage, latency bottlenecks, and compliance blind spots. Elastic’s integration with Azure AI Foundry addresses these issues with pre-built dashboards that deliver real-time insights into model usage, performance, costs and content filtering—all in a unified view.
“Agentic AI is only as strong as the models and infrastructure that power it,” said Santosh Krishnan, general manager, Observability & Security at Elastic. “With Elastic and Azure AI Foundry, developers and SREs gain clear visibility into how their agents are performing, understand the drivers of cost, and fix performance bottlenecks in real time. That means they can scale AI applications faster without compromising reliability, compliance, or budget.”
“This integration with Elastic delivers real-time visibility into token usage, latency, and costs, with built-in safeguards for any model hosted in Azure AI Foundry,” said Amanda Silver, corporate vice president at Microsoft Azure CoreAI. “Developers can now build and scale agents on Azure AI Foundry with the operational clarity and control they need to succeed in production.”
The Elastic Azure AI Foundry integration is available in tech preview on Elastic Observability.
The Latest
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...
In a 2026 survey conducted by Liquibase, the research found that 96.5% of organizations reported at least one AI or LLM interaction with their production databases, often through analytics and reporting, training pipelines, internal copilots, and AI generated SQL. Only a small fraction reported no interaction at all. That means the database is no longer a downstream system that AI "might" reach later. AI is already there ...
From Fragmented Systems to Intelligent Platforms: An ITIL+ Playbook for Enterprise IT Transformation
In many organizations, IT still operates as a reactive service provider. Systems are managed through fragmented tools, teams focus heavily on operational metrics, and business leaders often see IT as a necessary cost center rather than a strategic partner. Even well-run ITIL environments can struggle to bridge the gap between operational excellence and business impact. This is where the concept of ITIL+ comes in ...
UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...
For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...
AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...
Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...
Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...