Datadog Expands Strategic Partnership with Google Cloud and Integrates with Vertex AI

New Vertex AI integration gives developers full observability on the prediction performance and resource utilization of their custom AI/ML models

 Datadog the monitoring and security platform for cloud applications 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 is one of the first AI/ML observability solution partners for Vertex AI, allowing AI ops teams and developers to monitor, analyze and optimize the performance of their machine learning models in production.

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“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.

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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.

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