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Datadog Announces Support For AWS App Runner

Datadog announced support for application monitoring with AWS App Runner, joining Amazon Web Services (AWS) as a Launch Partner for the new fully managed service.

This new functionality will help engineering and product teams scale, deploy, and monitor their apps without the burden of managing their own infrastructure.

AWS App Runner is a purpose-built container application service that enables customers to build and run containerized web applications and APIs with no prior container or infrastructure experience required. Customers can simply provide their source code, container image, or deployment pipeline and AWS App Runner will build and deploy the application, automatically handling the load balancing, encryption, and scaling needed.

The Datadog integration with AWS App Runner allows customers to comprehensively monitor their applications managed by AWS App Runner to:

- Identify errors: track the success rate of requests to detect issues in code leading to errors.

- Ensure adequate resourcing: get visibility into applications with under or over-provisioned compute and memory.

- Know when to scale up or down: understand application latency to set the right autoscaling rules.

- Ensure security: observe and analyze all AWS App Runner API activity.

“At Datadog, we’re focused on helping customers monitor their applications wherever and however they run,” says Ilan Rabinovitch, Senior VP, Product and Community at Datadog. “Using AWS App Runner, customers can now more easily deploy and scale their web applications from a container image or source code repository. With our new integration, customers can monitor their AWS App Runner metrics, logs, and events to troubleshoot issues faster, and determine the best resource and scaling settings for their app.”

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Datadog Announces Support For AWS App Runner

Datadog announced support for application monitoring with AWS App Runner, joining Amazon Web Services (AWS) as a Launch Partner for the new fully managed service.

This new functionality will help engineering and product teams scale, deploy, and monitor their apps without the burden of managing their own infrastructure.

AWS App Runner is a purpose-built container application service that enables customers to build and run containerized web applications and APIs with no prior container or infrastructure experience required. Customers can simply provide their source code, container image, or deployment pipeline and AWS App Runner will build and deploy the application, automatically handling the load balancing, encryption, and scaling needed.

The Datadog integration with AWS App Runner allows customers to comprehensively monitor their applications managed by AWS App Runner to:

- Identify errors: track the success rate of requests to detect issues in code leading to errors.

- Ensure adequate resourcing: get visibility into applications with under or over-provisioned compute and memory.

- Know when to scale up or down: understand application latency to set the right autoscaling rules.

- Ensure security: observe and analyze all AWS App Runner API activity.

“At Datadog, we’re focused on helping customers monitor their applications wherever and however they run,” says Ilan Rabinovitch, Senior VP, Product and Community at Datadog. “Using AWS App Runner, customers can now more easily deploy and scale their web applications from a container image or source code repository. With our new integration, customers can monitor their AWS App Runner metrics, logs, and events to troubleshoot issues faster, and determine the best resource and scaling settings for their app.”

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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 ...