Datadog announced the general availability of Datadog Monitoring for Oracle Cloud Infrastructure (OCI), which enables Oracle customers to monitor enterprise cloud-native and traditional workloads on OCI with telemetry in context across their infrastructure, applications and services.
With this launch, Datadog helps customers migrate with confidence from on-premises to cloud environments, execute multi-cloud strategies and monitor AI/ML inference workloads.
Datadog Monitoring for Oracle Cloud Infrastructure helps customers:
- Gain visibility into OCI and hybrid environments: Teams can collect and analyze metrics from their OCI stack by using Datadog’s integrations for 20+ major OCI services and 750+ other technologies. In addition, customers can visualize the performance of OCI cloud services, on-premises servers, VMs, databases, containers and apps in near-real time with customizable, drag-and-drop, and out-of-the-box dashboards and monitors.
- Monitor AI/ML inference workloads: Teams can monitor and receive alerts on the usage and performance of GPUs, investigate root causes, monitor operational performance and evaluate the quality, privacy and safety of LLM applications.
- Get code-level visibility into applications: Real-time service maps, AI-powered synthetic monitors and alerts on latency, exceptions, code-level errors, log issues and more give teams deeper insight into the health and performance of their applications, including those using Java.
“With today’s announcement, Datadog enables Oracle customers to unify monitoring of OCI, on-premises environments and other clouds in a single pane of glass for all teams,” said Yrieix Garnier, VP of Product at Datadog. “This helps teams migrate to the cloud and execute multi-cloud strategies with confidence, knowing that they can monitor services side-by-side, visualize performance data during all stages of a migration and immediately identify service dependencies.”
Datadog Monitoring for Oracle Cloud Infrastructure is generally available now.
The Latest
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
The mobile app industry continues to grow in size, complexity, and competition. Also not slowing down? Consumer expectations are rising exponentially along with the use of mobile apps. To meet these expectations, mobile teams need to take a comprehensive, holistic approach to their app experience ...
Users have become digital hoarders, saving everything they handle, including outdated reports, duplicate files and irrelevant documents that make it difficult to find critical information, slowing down systems and productivity. In digital terms, they have simply shoved the mess off their desks and into the virtual storage bins ...
Today we could be witnessing the dawn of a new age in software development, transformed by Artificial Intelligence (AI). But is AI a gateway or a precipice? Is AI in software development transformative, just the latest helpful tool, or a bunch of hype? To help with this assessment, DEVOPSdigest invited experts across the industry to comment on how AI can support the SDLC. In this epic multi-part series to be posted over the next several weeks, DEVOPSdigest will explore the advantages and disadvantages; the current state of maturity and adoption; and how AI will impact the processes, the developers, and the future of software development ...
Half of all employees are using Shadow AI (i.e. non-company issued AI tools), according to a new report by Software AG ...
On their digital transformation journey, companies are migrating more workloads to the cloud, which can incur higher costs during the process due to the higher volume of cloud resources needed ... Here are four critical components of a cloud governance framework that can help keep cloud costs under control ...
Operational resilience is an organization's ability to predict, respond to, and prevent unplanned work to drive reliable customer experiences and protect revenue. This doesn't just apply to downtime; it also covers service degradation due to latency or other factors. But make no mistake — when things go sideways, the bottom line and the customer are impacted ...
Organizations continue to struggle to generate business value with AI. Despite increased investments in AI, only 34% of AI professionals feel fully equipped with the tools necessary to meet their organization's AI goals, according to The Unmet AI Needs Surveywas conducted by DataRobot ...
High-business-impact outages are costly, and a fast MTTx (mean-time-to-detect (MTTD) and mean-time-to-resolve (MTTR)) is crucial, with 62% of businesses reporting a loss of at least $1 million per hour of downtime ...
Organizations recognize the benefits of generative AI (GenAI) yet need help to implement the infrastructure necessary to deploy it, according to The Future of AI in IT Operations: Benefits and Challenges, a new report commissioned by ScienceLogic ...
