Skip to main content

Datadog Supports AWS App Mesh

Datadog announced the general availability of its support for Amazon Web Services (AWS) App Mesh, a hosted service that dynamically configures service mesh proxies.

With Datadog’s AWS App Mesh integration engineering teams can monitor their services, proxies, and tracing requests, ensuring strong performance and identifying potential issues for troubleshooting.

“With AWS App Mesh, customers can observe communications in a consistent manner and easily control how traffic flows between every part of an application without having to change their code,” said Deepak Singh, Director of Compute Services, Amazon Web Services, Inc. “We are pleased to have worked with Datadog as an AWS Partner Network (APN) launch Partner for AWS App Mesh. Datadog's integration makes it easy for AWS App Mesh users to monitor the performance of their microservices, view platform and application logs, and trace the path of requests as they move through the service mesh. Datadog's platform provides a powerful way for developers to better understand their applications."

AWS App Mesh makes it easy to run microservices by providing consistent visibility and network traffic controls for each microservice in an application. AWS App Mesh removes the need to update application code to change how monitoring data is collected or traffic is routed between microservices. AWS App Mesh configures each microservice to export monitoring data and implements consistent communications control logic across your application. This makes it easy to quickly pinpoint the exact location of errors and automatically re-route network traffic when there are failures or when code changes need to be deployed.

Integrating Datadog with AWS App Mesh allows teams to collect hundreds of metrics tracking internal activity, as well as the performance of services and the applications that those services depend on. Teams can then create visualizations and alerts in Datadog to monitor the performance and health of all their services in one place.

In addition to tracking key metrics, teams can also trace requests to all the services in their service mesh with Datadog APM & Distributed Tracing. Datadog APM can visualize distributed request traces in detailed flame graphs, illuminating each call’s timing and dependencies, and allowing teams to explore an automatically generated Service Map to see how requests flow between all their services. Along with an existing integration with Datadog Log Management, teams using AWS App Mesh can gain full visibility into the performance of their critical services.

“Since announcing our integration with AWS App Mesh last fall, our customers have enthusiastically adopted its capabilities,” said Michael Gerstenhaber, Director of Product Management, Datadog. “Service Meshes have become an integral component of production container workloads, and we’re excited to work with AWS to provide the observability our customers need to monitor their services and proxies.”

The Latest

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Datadog Supports AWS App Mesh

Datadog announced the general availability of its support for Amazon Web Services (AWS) App Mesh, a hosted service that dynamically configures service mesh proxies.

With Datadog’s AWS App Mesh integration engineering teams can monitor their services, proxies, and tracing requests, ensuring strong performance and identifying potential issues for troubleshooting.

“With AWS App Mesh, customers can observe communications in a consistent manner and easily control how traffic flows between every part of an application without having to change their code,” said Deepak Singh, Director of Compute Services, Amazon Web Services, Inc. “We are pleased to have worked with Datadog as an AWS Partner Network (APN) launch Partner for AWS App Mesh. Datadog's integration makes it easy for AWS App Mesh users to monitor the performance of their microservices, view platform and application logs, and trace the path of requests as they move through the service mesh. Datadog's platform provides a powerful way for developers to better understand their applications."

AWS App Mesh makes it easy to run microservices by providing consistent visibility and network traffic controls for each microservice in an application. AWS App Mesh removes the need to update application code to change how monitoring data is collected or traffic is routed between microservices. AWS App Mesh configures each microservice to export monitoring data and implements consistent communications control logic across your application. This makes it easy to quickly pinpoint the exact location of errors and automatically re-route network traffic when there are failures or when code changes need to be deployed.

Integrating Datadog with AWS App Mesh allows teams to collect hundreds of metrics tracking internal activity, as well as the performance of services and the applications that those services depend on. Teams can then create visualizations and alerts in Datadog to monitor the performance and health of all their services in one place.

In addition to tracking key metrics, teams can also trace requests to all the services in their service mesh with Datadog APM & Distributed Tracing. Datadog APM can visualize distributed request traces in detailed flame graphs, illuminating each call’s timing and dependencies, and allowing teams to explore an automatically generated Service Map to see how requests flow between all their services. Along with an existing integration with Datadog Log Management, teams using AWS App Mesh can gain full visibility into the performance of their critical services.

“Since announcing our integration with AWS App Mesh last fall, our customers have enthusiastically adopted its capabilities,” said Michael Gerstenhaber, Director of Product Management, Datadog. “Service Meshes have become an integral component of production container workloads, and we’re excited to work with AWS to provide the observability our customers need to monitor their services and proxies.”

The Latest

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...