--- title: "This Month in Datadog - October 2025" description: "In October's This Month in Datadog, explore AI-powered DDSQL queries, Cloud Cost Management updates, OTLP metrics ingestion, and more new features." author: "Datadog" date: 2025-11-05 tags: ["cloud cost management", "opentelemetry", "oci", "ai"] blog_type_id: the-monitor locale: en --- In October’s episode of This Month in Datadog, [Jeremy](https://www.linkedin.com/in/jeremylq/) shows how you can use AI to query with natural language in DDSQL Editor, ensure teams stay continually updated during outages, mitigate the impact of flaky tests, and ingest OpenTelemetry Protocol (OTLP) metrics from serverless and third-party SaaS environments. Later in the episode, [Kayla](https://www.linkedin.com/in/taylorkayla/) spotlights how to easily compare instance costs and performance with Instance Explorer and monitor Oracle Cloud Infrastructure (OCI) spend with Cloud Cost Management’s (CCM) new integration. We also look at [how Datadog’s ecosystem is evolving](https://www.datadoghq.com/blog/1k-integrations-milestone.md) to support whatever you’re building next, and a [blog series about how Datadog combined its SRE and security teams](https://www.datadoghq.com/blog/datadogs-approach-sre-security.md). [Watch video](https://www.youtube.com/embed/ZLTkQ0zMdX4?si=MmBY7ys4KHz5jUQJ) ## New features ### Optimize your spend with new CCM releases Take the guesswork out of choosing instances with [Datadog’s Instance Explorer](https://instances.datadoghq.com/), which lets you compare the cost and performance of instances across AWS, Azure, and Google Cloud. You can also track your [OCI spend with CCM](https://www.datadoghq.com/blog/cloud-cost-management-oci.md) using our new integration, which enables you to monitor, visualize, and optimize your OCI costs in one place. ### Query with natural language in DDSQL Editor Imagine asking a question in natural language and getting a ready-to-run SQL query. With [DDSQL Editor’s new feature](https://docs.datadoghq.com/ddsql_editor/getting_started.md), you can automatically generate and edit queries by using AI, with no SQL expertise required. This makes it easier to explore Datadog telemetry data and accelerate workflows, such as ad hoc operational analysis. ### Keep teams aligned during outages It’s never been easier to keep teams informed during outages. With [External Provider Status](https://www.datadoghq.com/blog/external-provider-status.md), you get near real-time visibility into the health of more than 40 providers, including SaaS APIs, like GitHub and OpenAI, as well as AWS services across regions. With [Datadog Status Pages](https://www.datadoghq.com/blog/status-pages.md), you can keep stakeholders aligned and informed during outages and service disruptions. Create and update notices directly in Datadog showing which services are impacted, the current status of each component, and a full timeline of updates. We’ve also released [Updog.ai](https://updog.ai/), a public-facing web page that shows the live health status of more than 30 SaaS providers. Built on anonymized observability data and AI at internet scale, [Updog.ai](https://www.datadoghq.com/blog/updog-ai.md) is a comprehensive public resource for real-time service transparency. ### Mitigate the impact of problematic tests with Flaky Test Management Re—running flaky tests can take hours, but ignoring them erodes developer confidence in CI pipelines. [Flaky Test Management](https://demo.datadoghq.com/release-notes/track-triage-and-suppress-flaky-tests-with-flaky-test-management) offers a centralized view to track and manage problematic tests. Monitor metrics like pipelines failed, time lost, and failure rate. You can also quarantine tests, or disable them entirely. ### Ingest OpenTelemetry Protocol metrics with the Datadog OTLP Metrics API Our new [OTLP API](https://www.datadoghq.com/blog/otlp-metrics-api.md) lets you ingest metrics from serverless and third-party SaaS environments where collectors can’t be deployed. Now you can gather metrics from managed OTel collectors, and send OTLP metrics to our platform from serverless applications–all without deploying a collector or our Agent. ### Additional updates Other features and updates released this month include: - Test out new prompts and models with [Datadog LLM Observability’s Experiments feature](https://www.datadoghq.com/blog/llm-experiments.md) - Easily investigate failed mobile tests with [Video Replay](https://demo.datadoghq.com/release-notes/expedite-mobile-test-troubleshooting-with-video-replay) - Forward runtime security alerts using our [Falco integration](https://www.datadoghq.com/blog/monitor-falco-with-datadog.md) - Join [Reference Tables](https://demo.datadoghq.com/release-notes/enrich-your-queries-by-joining-reference-tables-with-metrics-logs-rum-and-more) with more sources and preview data directly in the graph editor ## See you next month This Month in Datadog is a monthly roundup of our latest features, product announcements, and more. Subscribe to the [Datadog YouTube channel](https://www.youtube.com/@DatadogHQ) to get notified when future episodes are live. In the meantime, check out our [release notes](https://app.datadoghq.com/release-notes) for a full list of new features and updates. Or see them in action by logging in to the Datadog platform or . See you next month!