--- title: "Highlights from Google Cloud Next 2025" description: "Learn about the top themes, presentations, and product releases from Google Cloud Next 2025." author: "Trammell Saltzgaber" date: 2025-05-29 tags: ["google", "google cloud", "google cloud next"] blog_type_id: the-monitor locale: en --- Google Cloud Next is the biggest event of the year for the Google Cloud community, showcasing the latest and greatest offerings from Google Cloud and hundreds of its partners. As a long-time Google Cloud partner and recipient of [three Google Cloud Partner of the Year awards](https://www.datadoghq.com/about/latest-news/press-releases/google-cloud-next/) in 2025, Datadog was there in full force, delivering several [speaking sessions](https://cloud.withgoogle.com/next/25/session-library?filters=solution-productivity-collaboration#all) and running a booth on the expo floor where we met with thousands of attendees. In case you missed it, don’t worry. We’ve got you covered with a recap of the key themes from this year’s event. ![Showcase at Google Cloud Next 2025](https://web-assets.dd-static.net/42588/1776299705-google-next-2025-recap-google-next-2025-v2.jpg) ## Innovations across the AI stack Google Cloud continues to make major investments in its AI capabilities. From AI accelerators like [Ironwood](https://blog.google/products/google-cloud/ironwood-tpu-age-of-inference/), its latest generation of TPUs, to new models like [Gemini 2.5](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/), its newest thinking model that leads in industry benchmarks like GPQA, AIME 2025, and Humanity’s Last Exam, Google Cloud's announcements included improvements at each layer of the AI stack. Datadog has similarly invested in its AI monitoring capabilities to equip customers with the tools needed to ensure these Google Cloud technologies are performant, secure, and cost-effective. [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/) enables teams to monitor, troubleshoot, observe, and secure their LLMs, and with [auto-instrumentation for Gemini-powered and Vertex AI LLMs](https://www.datadoghq.com/blog/monitor-google-gemini-datadog-llm-observability.md), getting started has never been easier. And for monitoring the performance and utilization of AI accelerators, Datadog offers [GPU monitoring](https://www.datadoghq.com/product-preview/gpu-monitoring/) (now in Preview) and an integration with [Google Cloud TPU](https://www.datadoghq.com/blog/monitor-google-cloud-tpus-with-datadog.md), which help teams detect resource bottlenecks and optimize their infrastructure for cost and performance. Google also announced a number of new capabilities around the development of AI agents, including its new [Agent2Agent (A2A) protocol](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/). Datadog is a launch partner for this release, and we look forward to collaborating with the Google team to streamline the development and monitoring of interoperable, secure agentic applications. ## Bolstered storage for training and inference As organizations run more training and inference workloads with increasingly large datasets, storage needs to keep pace to minimize performance bottlenecks. At Next, Google announced several new storage offerings across Google Compute Engine and Google Cloud Storage to accelerate AI workloads. Between [Anywhere Cache](https://cloud.google.com/storage/docs/anywhere-cache), [Rapid Storage](https://cloud.google.com/blog/products/storage-data-transfer/high-performance-storage-innovations-for-ai-hpc), and [Hyperdisk Exapools](https://cloud.google.com/blog/products/compute/delivering-new-compute-innovations-and-offerings), these technologies provide industry-leading capacity, accelerate throughput, and reduce latency. Organizations that rely on Google Cloud Storage can find data volumes growing and storage patterns becoming more complex, and it can be hard for them to understand and proactively optimize their storage utilization. That’s why Datadog built [Storage Monitoring](https://www.datadoghq.com/blog/datadog-storage-monitoring.md). With Storage Monitoring for Google Cloud Storage, users get visibility into their Google Cloud Storage at the object and prefix levels, enabling them to identify bottlenecks, track performance, and quickly detect unusual growth in their storage consumption. Interested customers can request to join the [Preview here](https://www.datadoghq.com/product-preview/storage-monitoring/). ## Data analytics augmented with artificial intelligence In addition to giving customers the tools to build their own AI-powered applications, Google has embedded AI capabilities into its core services, including [BigQuery](https://cloud.google.com/blog/products/data-analytics/data-analytics-innovations-at-next25). New AI agents in BigQuery help teams build data pipelines, perform data preparation, detect anomalies in datasets, and automate metadata generation. With the BigQuery AI query engine, data scientists can ask queries in natural language to process structured and unstructured data together alongside real-world context. BigQuery is often a critical component of its users’ Google Cloud stacks, which makes performance and cost optimization especially important. In existing monitoring solutions, it can be a challenge to pinpoint the largest drivers of BigQuery usage and areas for optimization. This is why Datadog launched [expanded BigQuery monitoring capabilities](https://www.datadoghq.com/about/latest-news/press-releases/google-cloud-next/) at Next. With these new features, users can view BigQuery spend by user and project to identify cost-drivers, pinpoint the longest-running queries in those segments to optimize, detect data quality issues, and identify failed jobs so they can fix or eliminate unneeded queries. Interested customers can sign up for the Preview [here](https://www.datadoghq.com/product-preview/bigquery-monitoring/). ## A new approach to security Security was another key theme in Google’s keynote this year. Google continued to expand its use of AI across its product portfolio with the release of new security AI agents for malware detection and alert triage, but the headline was the introduction of [Google Unified Security](https://cloud.google.com/security/google-unified-security?hl=en). This solution connects a number of existing Google Cloud security tools, including Google SecOps and Google Cloud Security Command Center, into a single converged solution powered by Gemini. For organizations using or evaluating Google Unified Security, Datadog helps extend Google’s threat visibility with observability context and flexible detection capabilities. Customers can ingest Google Cloud logs into [Datadog Cloud SIEM](https://www.datadoghq.com/product/cloud-siem/) to correlate Google’s findings with telemetry from across your environment, including third-party security signals. Datadog Cloud SIEM supports centralized detection and faster investigations by combining insights from [Google Security Command Center](https://www.datadoghq.com/blog/datadog-google-security-command-center.md), [Cloud Armor](https://www.datadoghq.com/blog/network-attacks-google-cloud-armor.md), and the rest of your stack, giving teams the context they need to accelerate root cause analysis and streamline response. And with Datadog Observability Pipelines, teams can filter, enrich, and route their logs directly to SIEM solutions, including Datadog Cloud SIEM and [Google SecOps via a new integration](https://www.datadoghq.com/blog/observability-pipelines-route-logs-google-secops.md), supporting migrations or hybrid workflows. For organizations operating in hybrid or multi-cloud environments, Datadog offers a comprehensive security solution spanning [cloud security](https://www.datadoghq.com/product/cloud-security-management/), [code security](https://www.datadoghq.com/product/code-security/), and [threat management](https://www.datadoghq.com/product/cloud-siem/), with a rapidly expanding feature set that includes [app and API protection with Google Service Extensions](https://docs.datadoghq.com/security/application_security/threats/setup/threat_detection/gcp-service-extensions.md) for inline security at the load balancer level. From Cloud SIEM to CSPM and more, Datadog provides security capabilities that span clouds, so teams have a single place to secure and protect their full cloud and application stack. ## Monitor and secure your Google Cloud environment This was just a subset of Google’s announcements and the ways that Datadog can monitor and secure your Google Cloud environment. From [GKE autoscaling](https://www.datadoghq.com/blog/datadog-kubernetes-autoscaling.md) and a new [Google Cloud Run sidecar](https://www.datadoghq.com/blog/instrument-cloud-run-with-datadog-sidecar.md) to [cloud cost recommendations](https://docs.datadoghq.com/cloud_cost_management/recommendations.md) and [On-Call](https://www.datadoghq.com/blog/datadog-on-call.md), Datadog is continually building new products and features to give Google Cloud customers the best possible observability and security experience. ![Datadog at Google Cloud Next 2025](https://web-assets.dd-static.net/42588/1776299709-google-next-2025-recap-google-next-2025-datadog.jpg) To learn more about how Datadog can help you achieve your Google Cloud goals, refer to our [solutions page](https://www.datadoghq.com/solutions/gcp/). Read our [documentation](https://docs.datadoghq.com/integrations/google_cloud_platform.md?tab=dataflowmethodrecommended) to get started or, if you're not already a customer, sign up for a 14-day . We’re looking forward to seeing you all at next year’s event!