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Datadog Introduces New APM Solution

Datadog announced the release of a new Application Performance Monitoring (APM) extension to its platform, built to help DevOps teams quickly troubleshoot issues in complex, distributed applications that use hybrid clouds, microservices or containers.

“Most APM tools on the market today are designed to troubleshoot coding issues in isolation,” said Amit Agarwal, Chief Product Officer at Datadog. “However, in modern cloud-scale applications, quickly resolving problems requires examining changes in both the infrastructure and code simultaneously.”

Datadog’s APM solution supports a variety of use cases, including performance optimization and outage diagnosis to reduce the impact of costly downtime for large-scale users.

Highlights:

- Quick installation via a hosted SaaS solution that works out of the box

- Automatic tracing of individual requests from end to end across hosts and services

- Flame graphs for quickly and accurately identifying the most frequently used code paths

- Easily customizable dashboards for data aggregation and correlation

- Built-in collaboration between teams spanning dev and ops organizations

- Smart alerting via email, SMS and cloud-based collaboration tools

- Machine-learning based anomaly detection

- Transparent tag-based aggregation of performance data from microservices, containers and ephemeral hosts

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Datadog Introduces New APM Solution

Datadog announced the release of a new Application Performance Monitoring (APM) extension to its platform, built to help DevOps teams quickly troubleshoot issues in complex, distributed applications that use hybrid clouds, microservices or containers.

“Most APM tools on the market today are designed to troubleshoot coding issues in isolation,” said Amit Agarwal, Chief Product Officer at Datadog. “However, in modern cloud-scale applications, quickly resolving problems requires examining changes in both the infrastructure and code simultaneously.”

Datadog’s APM solution supports a variety of use cases, including performance optimization and outage diagnosis to reduce the impact of costly downtime for large-scale users.

Highlights:

- Quick installation via a hosted SaaS solution that works out of the box

- Automatic tracing of individual requests from end to end across hosts and services

- Flame graphs for quickly and accurately identifying the most frequently used code paths

- Easily customizable dashboards for data aggregation and correlation

- Built-in collaboration between teams spanning dev and ops organizations

- Smart alerting via email, SMS and cloud-based collaboration tools

- Machine-learning based anomaly detection

- Transparent tag-based aggregation of performance data from microservices, containers and ephemeral hosts

The Latest

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...