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Datadog Announces New Cloud Cost Management Capabilities

Datadog announced new capabilities for its Cloud Cost Management product, including container cost allocation, cost monitors and support for Microsoft Azure.

Cloud Cost Management shows an organization's granular cost data, scoped to specific services, so that engineers can optimize cloud spend and performance.

"As organizations increase their usage of containers and multiple clouds, the ability to centralize cost data and allocate spend across different dimensions becomes even more important," said Kayla Taylor, Senior Product Manager, Cloud Cost Management at Datadog. "Datadog Cloud Cost Management gives engineers visibility into spend and helps create a cost-conscious culture so they can take action on cost insights. With granular alerting and visibility into containers and Azure environments, Datadog Cloud Cost Management provides the relevant observability and cost data that engineers need, in the platform they already use everyday, to empower them to reduce waste and avoid unexpected cost overages."

Datadog's new capabilities for Cloud Cost Management help organizations:

- Understand Container Costs: With a quick and easy setup process, container cost allocation gives FinOps and engineering teams full visibility into spend, so organizations understand why and when container costs change and can detect idle costs.

- Respond to Cost Changes: Customizable and granular cost monitors help service owners rapidly respond to unexpected cost changes alongside application performance data. Alerts are tailored to specific services so engineers can quickly pivot from detecting a cost overrun to identifying ways to take action in a single pane of glass.

- Allocate Spend Across Azure and AWS: With support for Azure in addition to AWS, organizations can now seamlessly understand the teams, services and environments responsible for their highest cloud costs. Teams using Microsoft Azure can optimize for performance and cost, with full visibility into infrastructure and application telemetry.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Datadog Announces New Cloud Cost Management Capabilities

Datadog announced new capabilities for its Cloud Cost Management product, including container cost allocation, cost monitors and support for Microsoft Azure.

Cloud Cost Management shows an organization's granular cost data, scoped to specific services, so that engineers can optimize cloud spend and performance.

"As organizations increase their usage of containers and multiple clouds, the ability to centralize cost data and allocate spend across different dimensions becomes even more important," said Kayla Taylor, Senior Product Manager, Cloud Cost Management at Datadog. "Datadog Cloud Cost Management gives engineers visibility into spend and helps create a cost-conscious culture so they can take action on cost insights. With granular alerting and visibility into containers and Azure environments, Datadog Cloud Cost Management provides the relevant observability and cost data that engineers need, in the platform they already use everyday, to empower them to reduce waste and avoid unexpected cost overages."

Datadog's new capabilities for Cloud Cost Management help organizations:

- Understand Container Costs: With a quick and easy setup process, container cost allocation gives FinOps and engineering teams full visibility into spend, so organizations understand why and when container costs change and can detect idle costs.

- Respond to Cost Changes: Customizable and granular cost monitors help service owners rapidly respond to unexpected cost changes alongside application performance data. Alerts are tailored to specific services so engineers can quickly pivot from detecting a cost overrun to identifying ways to take action in a single pane of glass.

- Allocate Spend Across Azure and AWS: With support for Azure in addition to AWS, organizations can now seamlessly understand the teams, services and environments responsible for their highest cloud costs. Teams using Microsoft Azure can optimize for performance and cost, with full visibility into infrastructure and application telemetry.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.