Datadog announced an expanded strategic partnership with Google Cloud, which enables Google Cloud customers to proactively observe and secure their cloud-native and hybrid applications within Datadog's unified platform.
As part of the expanded partnership and integrations, Datadog integrates with Vertex AI, allowing AI ops teams and developers to monitor, analyze and optimize the performance of their machine learning models in production.
"Google Cloud continues to be a key partner for Datadog as we jointly help global businesses observe and secure their cloud applications," said Yrieix Garnier, VP of Product at Datadog. "The new Vertex AI integration expands this partnership and gives AI and ML developers full observability into their production applications built on Vertex AI. With out-of-the-box dashboards and real-time monitors, customers can get started quickly and ensure their models are performing at an optimal level while delivering predictions responsively at scale and without errors."
"Generative AI is fundamentally changing how many businesses operate, fueling a new era of cloud that can benefit virtually every area of an organization," said Kevin Icchpurani, Corporate VP, Global Partner Ecosystem & Channels at Google Cloud. "By applying Vertex AI, Datadog can help AI teams improve how they monitor and analyze the performance of machine learning models, ensuring they are functioning correctly and creating optimal value."
Datadog's integration with Vertex AI provides developers full observability on the prediction performance and resource utilization of their custom AI/ML models. The integration provides an out-of-the-box dashboard with prediction counts, latency, errors and resource (CPU/Memory/Network) utilization grouped by deployed models so teams can compare model performance side-by-side in production environments. It also helps detect data anomalies in order to maintain the reliability and robustness of machine learning applications.
Other new and expanded Google Cloud integrations that were recently announced include:
- Serverless monitoring: Datadog now offers in-depth support for Google Cloud Run—the leading serverless compute technology on Google Cloud. With native distributed tracing across all runtimes and the ability to collect custom metrics and logs, Datadog provides deep insights into customers' Cloud Run workloads as well as fully managed APIs, queues, streams and data stores.
- Google Cloud Ready - Cloud SQL: Datadog has earned the Google Cloud Ready designation for the Google Cloud SQL integration, providing visibility into the performance and health of Cloud SQL to customers. This integration monitors throughput, memory and availability metrics in customers' databases from MySQL, PostgreSQL and SQL Server.
- Google Security Command Center: Customers can now send their Google Cloud Security Command Center findings to Datadog, including vulnerabilities, threats and errors from containers and virtual machines. Using Datadog Cloud SIEM, customers can automatically generate signals and perform investigations.
- Quick setup: Datadog's new setup experience allows Google Cloud customers to get started in just seconds so they can monitor their entire Google Cloud environment, even when there are thousands of projects. New projects can also be auto-discovered to ensure complete and seamless monitoring coverage. With out-of-the-box dashboards and real-time monitors on over 30+ Google Cloud integrations, customers can begin monitoring their services in just a few clicks.
These integrations are available now.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
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.