Google Cloud Monitoring

End-to-end observability for Google Cloud environments

Organizations across a wide range of industries rely on Google Cloud to run their infrastructure and global-scale applications—from start-ups looking to reduce upfront infrastructure cost to enterprises aiming to build and release products to market faster. Google Cloud provides the ability to deploy distributed, scalable infrastructure, but it also introduces new observability needs, as organizations need visibility into every layer of their dynamic infrastructure to maintain and optimize performance.

Datadog collects and unifies all of the data streaming from these complex environments, aggregating logs, metrics, traces, security signals, cost data, and more in a single platform so teams get full context into their Google Cloud environment. Datadog’s 35+ easy-to-install Google Cloud integrations and preconfigured dashboards for popular Google Cloud services like Google Kubernetes Engine, Google Compute Engine, Cloud Functions, and BigQuery allow teams to start monitoring their environment in minutes. Teams can also deploy the Datadog Agent directly on their hosts and compute instances to collect metrics with greater granularity—down to one-second resolution—including those not available via Google's monitoring APIs.

A comprehensive view across hybrid and multi-cloud environments

Organizations with hybrid and multi-cloud environments often use multiple monitoring tools for end-to-end visibility. This can create data silos and limit teams in their ability to effectively monitor and address issues. Datadog unifies observability data from any host and service, providing deep, cross-platform visibility into critical applications. For organizations using Google Cloud alongside other cloud providers or on-prem infrastructure, this eliminates the need for multiple point solutions.

Datadog’s Log Management spans hybrid and multi-cloud environments to provide a centralized, cost-effective log management tool for an organization’s entire stack. And with Datadog's Service Map (included as part of Datadog’s Application Performance Monitoring and Universal Service Monitoring), teams can visualize the dependencies between databases, APIs, containers, and more, enabling them to easily follow the data flowing between Google Cloud, other cloud providers, and on-prem hosts.

Track every phase of a Google Cloud migration

When migrating to Google Cloud or other cloud platforms, organizations often refactor their applications to leverage the flexibility and scalability of the cloud and decrease friction during the migration process. Datadog enables teams to seamlessly monitor the performance of their updated and legacy services side-by-side in every stage of a migration so they can ensure expected benchmarks are met. And with 750+ integrations, Datadog can monitor even more of the application services teams rely on during a migration, including those not deployed on Google Cloud.

The Host Map (included in Datadog’s Infrastructure Monitoring) enables teams to monitor real-time data such as network throughput and CPU utilization for all hosts across availability zones to visualize performance before, during, and after a migration.

Secure your Google Cloud environment

As applications and infrastructure expand, securing the full scope of a Google Cloud environment becomes increasingly complex. Datadog Security seamlessly integrates into an organization's Google Cloud environment for full-stack threat detection, posture management, workload security, application security, and more. By combining observability data with a full spectrum of security insights in a single platform, Datadog enables easy collaboration between development, security, and operations teams and yields faster security outcomes with enhanced context. Once issues have been identified, teams can also take action from within Datadog, kicking off Jira tickets or triggering automated remediation workflows. And as one of the only observability providers with support for Google’s Private Service Connect, Datadog enables organizations to monitor their Google Cloud environment without transmitting data over the public internet, increasing data security.

Automatically scale with dynamic Google Cloud infrastructure

Ephemeral infrastructure platforms such as Google Kubernetes Engine, Cloud Functions, and Cloud Run can rapidly auto-scale to support application traffic, creating new resources to meet demand or downsizing them to save on costs. Datadog integrates with these services to collect data in real time and, unlike legacy monitoring tools, automatically scales with Google Cloud infrastructure by monitoring resources as soon as they are created. And with native integrations for services such as Chef and Ansible, teams can leverage their existing workflows to deploy and configure Datadog on new hosts, clusters, and other application resources instantly.

Manage your Google Cloud costs

One of the most common challenges in migrating successfully to the cloud is understanding the cost of running applications. Organizations often struggle to properly optimize their applications for the cloud and lack insight into cost drivers, leading to unexpected cost overruns. Datadog Cloud Cost Management seamlessly correlates the cost of cloud infrastructure with performance data, enabling engineers and FinOps practitioners to identify areas for optimization. What would otherwise be siloed data can instead be used in preconfigured dashboards to understand where spend is concentrated during a migration. This visibility—combined with service- and team-specific reports—fosters a culture of cost-consciousness and gives employees a view into their respective contributions to overall cloud spend. Organizations looking for further cost savings can connect Datadog to their Google Cloud environments using Private Service Connect to significantly reduce data transfer costs.

Monitor and optimize Google Cloud AI workloads

As organizations move AI capabilities out of development and into production, observability solutions become essential to ensure the efficiency and performance of models and their underlying infrastructure. Datadog’s integration with Vertex AI provides inference metrics like model prediction performance and resource utilization for custom and prebuilt foundational models run in Vertex AI. This enables teams to identify and diagnose the root cause of performance issues and compare the performance of models in production. And with an integration with NVIDIA’s DCGM Exporter, organizations can evaluate the health of their broader GPU infrastructure and correlate GPU performance with other technologies in their AI stack.

Start monitoring your Google Cloud, hybrid, or multi-cloud environment in minutes

Observability is critical to achieving operational excellence in the cloud. Datadog’s comprehensive platform enables organizations to detect and resolve performance issues, secure their infrastructure and applications, effectively migrate to the cloud, modernize their stack with ephemeral infrastructure and artificial intelligence, reduce cloud costs, and more. Teams can start monitoring their cloud environment in minutes with Datadog’s 750+ easy-to-install integrations and seamlessly connect Datadog into existing workflows in tools like Slack, PagerDuty, and Jira.