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Datadog Expands Partnership with Google Cloud Including Vertex AI Integration

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.

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Datadog Expands Partnership with Google Cloud Including Vertex AI Integration

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

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

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Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...