
Datadog announced the general availability of its Continuous Integration (CI) Visibility product.
With insights into continuous integration pipelines, CI Visibility enables developers and engineering organizations to quickly determine and fix the root cause of issues detected in build and testing pipelines.
Datadog CI Visibility provides advanced monitoring and observability capabilities that not only help development and engineering teams understand when an issue occurs in their CI pipeline, but also provide the insights to help identify why the issue is occurring and how to resolve it. It does so by providing deep, end-to-end visibility across each stage of your development pipeline and each step of your test execution history. This new capability allows engineering teams to address CI issues quickly and maintain more efficient, reliable pipelines.
"The combined power of Datadog and CircleCI makes life easier for DevOps teams. CircleCI's insights can easily integrate into Datadog users' workflows to help them make even more informed decisions when an event occurs in their pipeline," said Tom Trahan, VP Business Development, CircleCI. "To augment this data with observability insights, users can leverage Datadog CI Visibility to visualize key metrics such as build duration and failure rates, as well as detect flaky tests to ensure smooth, reliable workflows."
"CI/CD is a core part of the DevOps toolkit, yet, until now, developers have never had the level of visibility into earlier-stage workflows that they have enjoyed in production," said Ilan Rabinovitch, SVP, Product and Community, Datadog. "With the release of Datadog CI Visibility, we are delivering industry-first observability that extends from production environments to pre-production development and testing pipelines. This enables organizations to improve developer productivity by delivering more robust code more quickly—and ultimately deliver more resilient digital experiences at the speed that today's customers demand."
Datadog's CI Visibility product delivers:
- Pipeline visibility: Visualize pipeline data across CI providers in a single pane of glass, and identify opportunities to improve the performance and reliability of your workflows.
- Testing visibility: Identify and debug flaky tests before they degrade the reliability of your test suites.
- Advanced performance analytics: Track the historical performance of your tests to identify regressions over time and view the code commit that introduced the flaky test. Visualize each service a test interacts with to understand why tests are failing, automatically surface common errors to reveal systemic issues, and correlate test results with related logs and network performance data.
- Support for heterogeneous environments: Out-of-the-box support for multiple CI providers, including Buildkite, CircleCI, GitHub Actions, GitLab and Jenkins, as well as instrumentation for tests in multiple languages, including Java, JavaScript, .NET, Python, Ruby and Swift.
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 ...