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Datadog Releases Private Locations for Synthetic Monitoring

Datadog announced the general availability of Private Locations for Synthetic Monitoring.

This new capability enables DevOps teams to proactively monitor internal applications that are not accessible from the public internet, so they can understand the performance of these applications from any location that is mission critical to their business operations.

As modern businesses build and iterate their applications, they rely on Datadog Synthetic Monitoring to ensure their customers have consistent and positive digital experiences. While Datadog’s existing managed locations are useful for simulating application behavior and discovering user experience issues, these are focused on public-facing websites and endpoints. When businesses need to test internal applications or simulate the behavior of applications that serve customers from discrete geographic regions, they require customized synthetic locations.

With the launch and general availability of Private Locations for Synthetic Monitoring, Datadog customers now can achieve testing coverage for both internal-only and public-facing applications. Customers can build code-free tests and reduce their manual overhead with self-maintaining tests from both managed and private locations, thus ensuring that their applications are performant and reliable around the world.

“Since the launch of Synthetic Monitoring in 2019 on top of our monitoring platform, our customers have been able to constantly check the performance of their websites and endpoints across the globe, alongside logs, metrics and traces,” said Renaud Boutet, VP of Product at Datadog. “With private locations generally available, we are now adding coverage to include applications that are not available publicly.”

Private Locations for Datadog Synthetic Monitoring will also allow customers to:

- Deploy the private location as a container inside their internal environments through integrations with Docker, Kubernetes, ECS and other container integrations.

- Easily scale private locations by adding Docker containers, for more nimble proactive testing.

- Automatically correlate between Synthetic Monitoring and APM, logs, infrastructure metrics, and Datadog’s 400+ integrations.

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Datadog Releases Private Locations for Synthetic Monitoring

Datadog announced the general availability of Private Locations for Synthetic Monitoring.

This new capability enables DevOps teams to proactively monitor internal applications that are not accessible from the public internet, so they can understand the performance of these applications from any location that is mission critical to their business operations.

As modern businesses build and iterate their applications, they rely on Datadog Synthetic Monitoring to ensure their customers have consistent and positive digital experiences. While Datadog’s existing managed locations are useful for simulating application behavior and discovering user experience issues, these are focused on public-facing websites and endpoints. When businesses need to test internal applications or simulate the behavior of applications that serve customers from discrete geographic regions, they require customized synthetic locations.

With the launch and general availability of Private Locations for Synthetic Monitoring, Datadog customers now can achieve testing coverage for both internal-only and public-facing applications. Customers can build code-free tests and reduce their manual overhead with self-maintaining tests from both managed and private locations, thus ensuring that their applications are performant and reliable around the world.

“Since the launch of Synthetic Monitoring in 2019 on top of our monitoring platform, our customers have been able to constantly check the performance of their websites and endpoints across the globe, alongside logs, metrics and traces,” said Renaud Boutet, VP of Product at Datadog. “With private locations generally available, we are now adding coverage to include applications that are not available publicly.”

Private Locations for Datadog Synthetic Monitoring will also allow customers to:

- Deploy the private location as a container inside their internal environments through integrations with Docker, Kubernetes, ECS and other container integrations.

- Easily scale private locations by adding Docker containers, for more nimble proactive testing.

- Automatically correlate between Synthetic Monitoring and APM, logs, infrastructure metrics, and Datadog’s 400+ integrations.

The Latest

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 ...