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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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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

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...