DevKyleS · pull · Sep 3, 2026 · Sep 3, 2026 · Sep 3, 2026 · Sep 3, 2026
diff --git a/hugo/content/en/account_management/billing/usage_attribution.md b/hugo/content/en/account_management/billing/usage_attribution.md
@@ -22,7 +22,9 @@ Administrators or users with the Usage Read permission can access the Usage Attr
 - Summarizes usage at the end of each month and visualizes usage over time broken out by tags.
 - Generates month-to-date and hourly CSV files.
 
-This feature does not support product usage that cannot be tagged during instrumentation. For example, Incident Management Users, CI Pipeline and Test Users, Parallel Testing Slots, and Audit Trail. 
+This feature does not support product usage that cannot be tagged during instrumentation. For example, Incident Management Users, Parallel Testing Slots, and Audit Trail.
+
+**Note**: To break down CI Pipeline and Test Optimization billing by team or other organizational tags, see [Billing enrichment][5] in the CI Visibility Billing documentation.
 
 ## Getting started
 
@@ -109,3 +111,4 @@ For direct billing customers, month-end cost attribution reports are generated a
 [2]: https://docs.datadoghq.com/api/v1/usage-metering/#get-hourly-usage-attribution
 [3]: https://docs.datadoghq.com/getting_started/tagging/#define-tags
 [4]: https://docs.datadoghq.com/api/latest/usage-metering/#get-monthly-cost-attribution
+[5]: /account_management/billing/ci_visibility/#billing-enrichment
diff --git a/hugo/content/en/account_management/plan_and_usage/bill_overview.md b/hugo/content/en/account_management/plan_and_usage/bill_overview.md
@@ -24,7 +24,7 @@ The [**Bill Overview** page][1] gives administrators a single view of Datadog co
 
 The following filters apply to the {{< ui >}}Bill Overview{{< /ui >}} page:
 
-- {{< ui >}}Product Category{{< /ui >}}: Filter all views by broader product family, such as  Infrastructure, APM, Logs, Security, or AI/ML.
+- {{< ui >}}Product Category{{< /ui >}}: Filter all views by broader product family, such as Infrastructure, APM, Logs, Security, or AI/ML.
 - {{< ui >}}Billing Dimension{{< /ui >}}: Filter to a specific billing or metering dimension (for example, Infra Hosts, Indexed Logs, or Synthetic Browser Tests).
 - {{< ui >}}Sub-Org{{< /ui >}}: Filter to a specific child organization.
 - {{< ui >}}Group by Sub-Org{{< /ui >}}: Toggle to group costs by sub-organization.
@@ -112,7 +112,7 @@ Click {{< ui >}}View Details{{< /ui >}} on a Trends card or click any row in the
 ### Download billable hosts as CSV
 
 {{< site-region region="gov,gov2" >}}
-<div class="alert alert-warning">Downloading billable hosts as a CSV is not yet available for this site.</div>
+<div class="alert alert-warning">Downloading billable hosts as a CSV is not supported for your selected <a href="/getting_started/site">Datadog site</a> ({{< region-param key="dd_site_name" >}}).</div>
 {{< /site-region >}}
 
 Download a CSV of the individual hosts that make up your billable Infra Hosts total for a given month. Use it to reconcile the total shown on the Bill Overview page, find the hosts driving the largest share of your count, attribute usage to teams by tag, or compare months to spot unexpected changes.

diff --git a/hugo/content/en/actions/workflows/build.md b/hugo/content/en/actions/workflows/build.md
@@ -43,20 +43,21 @@ You can create workflows or edit existing workflows from the [Workflow Automatio
 
 ## Create or edit a workflow with AI {#create-a-workflow-with-ai}
 
-If you're not sure where to start, you can automatically generate a workflow, or iterate on an existing workflow with AI.
+Use AI assistance in [Bits Chat][15] to create a workflow from a prompt or update an existing workflow. Bits Chat can ask follow-up questions before creating or updating the workflow.
 
-To generate a workflow:
-1. From the [Workflow Automation][1] page, click {{< ui >}}New Workflow{{< /ui >}}.
-1. Click {{< ui >}}Create a workflow with AI{{< /ui >}}.
-1. Enter a detailed prompt for your workflow. Specify the integrations and actions you'd like to use.
-1. Click the up arrow ({{< ui >}}↑{{< /ui >}}) to create your workflow.
+To create a workflow:
+1. From the [Workflow Automation][1] page, click {{< ui >}}New workflow{{< /ui >}}.
+1. Under {{< ui >}}Build a workflow with AI{{< /ui >}}, enter a detailed prompt or select a suggested prompt.
+1. Click the up arrow ({{< ui >}}↑{{< /ui >}}) to submit the prompt.
+1. Answer any follow-up questions, then confirm the workflow when prompted. Bits creates the workflow and opens it in the workflow editor.
 
-To iterate on an existing workflow:
-1. From an existing workflow, click {{< ui >}}Edit with AI{{< /ui >}}.
-1. Enter a detailed prompt for the behavior you'd like to add to your workflow. Include the integrations and actions you'd like to use.
-1. Click the up arrow ({{< ui >}}↑{{< /ui >}}) to add the functionality to your workflow.
+You can also create a workflow from anywhere in Datadog with Bits Chat. Open Bits Chat by clicking {{< ui >}}Ask Bits{{< /ui >}} in the top-right navigation bar or pressing <kbd>Cmd</kbd> or <kbd>Ctrl</kbd> + <kbd>I</kbd>, then ask Bits to create a workflow.
 
-<div class="alert alert-info">The Workflow Automation AI does not answer questions about the product. If you have questions or feedback, consider joining the <strong>#workflows</strong> channel on the <a href="https://chat.datadoghq.com/">Datadog Community Slack</a></div>
+To update an existing workflow:
+1. Open the workflow and click the AI icon <i class="icon-bits-ai"></i> in the left toolbar. Bits Chat opens with the workflow attached as context.
+1. Enter a detailed prompt describing the changes you want.
+1. Click the up arrow ({{< ui >}}↑{{< /ui >}}) to submit the prompt.
+1. Answer any follow-up questions, then confirm the changes when prompted. Bits updates the workflow in the editor.
 
 ## Create a custom workflow
 
@@ -210,3 +211,4 @@ To perform tasks using the API, see the [Workflow Automation API documentation][
 [12]: /actions/workflows/variables/
 [13]: /api/latest/workflow-automation/
 [14]: /actions/workflows/variables/#context-variables
+[15]: /bits_ai/bits_chat/
diff --git a/hugo/content/en/api/latest/using-the-api/_index.md b/hugo/content/en/api/latest/using-the-api/_index.md
@@ -28,6 +28,7 @@ Available integrations endpoints:
 - [PagerDuty Integration][6]
 - [Slack Integration][5]
 - [Webhooks Integration][7]
+- [Oracle Cloud Integration][43]
 
 **Platform endpoints**
 
@@ -111,3 +112,4 @@ You can also use the Datadog API to manage your account programmatically:
 [40]: /api/latest/microsoft-teams-integration/
 [41]: /api/latest/okta-integration/
 [42]: /api/latest/opsgenie-integration/
+[43]: /api/latest/oci-integration/
diff --git a/hugo/content/en/byoc-logs/introduction/_index.md b/hugo/content/en/byoc-logs/introduction/_index.md
@@ -13,7 +13,7 @@ BYOC (Bring Your Own Cloud) Logs is Datadog's log management solution that runs
 
 Here is a high-level overview of how BYOC Logs works:
 
-{{< img src="/cloudprem/overview_diagram_cloudprem.png" alt="BYOC Logs architecture overview showing how logs flow from sources through BYOC Logs to the Datadog platform" style="width:100%;" >}}
+{{< img src="/cloudprem/overview_diagram_byoc.png" alt="BYOC Logs architecture overview showing how logs flow from sources through BYOC Logs to the Datadog platform" style="width:100%;" >}}
 
 The diagram illustrates the BYOC Logs hybrid architecture, highlighting how data is processed and stored within your infrastructure:
 

diff --git a/hugo/content/en/cloud_cost_management/allocation/container_cost_allocation.mdoc.md b/hugo/content/en/cloud_cost_management/allocation/container_cost_allocation.mdoc.md
@@ -86,6 +86,7 @@ The following table presents the list of collected features and the minimal Agen
 |---|---|---|
 | Container Cost Allocation | 7.27.0 | 1.11.0 |
 | GPU Container Cost Allocation | 7.54.0 | 7.54.0 |
+| Local Storage Cost Allocation | 7.27.0 | 1.11.0 |
 
 1. Configure the Google Cloud Cost Management integration on the [Cloud Cost Setup page][2].
 1. Install the [Datadog Agent][3] in a Kubernetes environment and ensure that you enable the [Orchestrator Explorer][4] in your Agent configuration.
@@ -188,6 +189,12 @@ Next, Datadog looks at all of the pods running on that node for the day. The cos
 
 All other costs are given the same value and tags as the source metric `gcp.cost.amortized`.
 
+### Local storage
+
+For Kubernetes local storage allocation, a Kubernetes node is joined with Google Cloud storage costs associated with its ephemeral-storage capacity.
+
+Next, Datadog examines all pods running on that node for the day. For each pod, Datadog reserves the larger of its ephemeral-storage request and its daily peak usage. The average usage determines the usage cost, the remainder of the reservation is workload idle, and capacity not reserved by any pod is cluster idle. The allocated cost is enriched with the pod's tags.
+
 ### Agentless Kubernetes costs
 
 To view the costs of GKE clusters without enabling Datadog Infrastructure Monitoring, use [GKE cost allocation][18]. Enable GKE cost allocation on unmonitored GKE clusters to access this feature set. This approach comes with the following limitations.
@@ -278,6 +285,19 @@ Costs are allocated into the following spend types:
 | Cluster idle | Cost of resources (such as memory, CPU, and GPU) that are not reserved by workloads in a cluster. This is the difference between the total cost of the resources and what is allocated to workloads. |
 | Not monitored | Cost of resources where the spend type is unknown. To resolve this, install the Datadog Agent on these clusters or nodes. |
 
+### Local storage
+
+Local storage costs are allocated into the following spend types:
+
+| Spend type | Description |
+| --- | --- |
+| Usage | Cost of local storage used by workloads, based on average ephemeral-storage usage that day. |
+| Workload idle | Cost of local storage reserved by workloads but not used. This is the difference between the larger of a pod's ephemeral-storage request or daily peak usage and its average usage. |
+| Cluster idle | Cost of node-local storage capacity not reserved by any workload in the cluster. |
+| Local storage not supported | Cost identified as local storage that cannot be associated with a node's ephemeral-storage capacity. This cost is not allocated to a workload. |
+
+Local storage is tied to the life cycle of a node. It does not include persistent volumes, which retain data independently of pods and nodes.
+
 {% /if %}
 
 ### Aggregated minor pod usage
@@ -352,7 +372,7 @@ Depending on the cloud provider, certain resources may or may not be available f
 | ECS costs | {% x/ %} | N/A | N/A |
 | Data transfer costs | {% x/ %} | Limited* | Limited* |
 | GPU | {% x/ %} | {% x/ %} | {% x/ %}  |
-| {% tooltip contents="Directly-attached storage resources for a node." %} Local storage {% /tooltip %} |  | Limited* | Limited* |
+| {% tooltip contents="Directly-attached storage resources for a node." %} Local storage {% /tooltip %} |  | Limited* | {% x/ %} |
 
 `Limited*` resources have been identified as part of your Kubernetes spend, but are not fully allocated to specific workloads or pods. These resources are host-level costs, not pod or namespace-level costs, and are identified with `allocated_spend_type:<resource>_not_supported`.
 
@@ -382,7 +402,7 @@ When the prerequisites are met, the following cost metrics automatically appear.
 
 | Cost Metric                    | Description    |
 | ---                                | ----------- |
-| `gcp.cost.amortized.shared.resources.allocated` | Google Compute Engine costs allocated by the CPU & memory used by a pod, using 60:40 split for CPU & memory respectively and a 95:3:2 split for GPU, CPU, & memory respectively if a GPU is used by a pod. This allocation method is used when the bill does not already provide a specific split between CPU and memory usage. <br> *Based on `gcp.cost.amortized`* |
+| `gcp.cost.amortized.shared.resources.allocated` | Google Compute Engine costs allocated by the CPU, memory, and local storage used by a pod. When the bill does not provide a specific CPU and memory split, costs use a 60:40 split. GPU hosts use a 95:3:2 split for GPU, CPU, and memory. <br> *Based on `gcp.cost.amortized`* |
 
 {% /if %}
 
@@ -489,9 +509,10 @@ In addition to Kubernetes pod and Kubernetes node tags, the following non-exhaus
 | `kube_stateful_set` | The name of the Kubernetes StatefulSet. |
 | `pod_name` | The name of any individual pod. |
 | `allocated_spend_type:not_monitored` | The tracking and allocation of [Agentless Kubernetes costs](#agentless-kubernetes-costs) associated with resources used by Google Cloud services or workloads, and the Datadog Agent is not monitoring those resources. |
+| `allocated_spend_type:local_storage_not_supported` | Identifies local storage costs that cannot be associated with a node's ephemeral-storage capacity. |
 | `allocated_resource:data_transfer` | The tracking and allocation of costs associated with data transfer activities used by Google Cloud services or workloads. |
 | `allocated_resource:gpu` | The tracking and allocation of costs at a host level associated with GPU resources used by Google Cloud services or workloads. |
-| `allocated_resource:local_storage` | The tracking and allocation of costs at a host level associated with local storage resources used by Google Cloud services or workloads. |
+| `allocated_resource:local_storage` | Identifies local storage costs allocated to Google Cloud Kubernetes workloads. |
 
 {% /if %}
 

diff --git a/hugo/content/en/data_observability/jobs_monitoring/databricks/private_link.md b/hugo/content/en/data_observability/jobs_monitoring/databricks/private_link.md
@@ -101,7 +101,7 @@ Set up your Private Action Runner using **one** of the following options.
     - Use cloud secret storage. Ensure an identity is assigned to the pod ([Azure Workload Identity][5], [AWS IAM Role][6], or [GKE Workload Identity][7]) with permissions to read the secret created in [Step 2](#step-2-databrick-prerequisites), and provide the path to the secret either via the `DATABRICKS_SECRET_PATH` environment variable, or by providing it to the "Secret Path" field in the integration tile.
 1. Restart the deployment for these changes to take effect.
 
-[1]: https://docs.datadoghq.com/actions/private_actions/use_private_actions/?tab=kubernetes#overview
+[1]: https://docs.datadoghq.com/actions/private_actions/set_up_standalone/?tab=kuberneteshelm#install-the-runner
 [2]: https://github.com/DataDog/helm-charts/tree/main/charts/private-action-runner
 [3]: https://app.datadoghq.com/actions/private-action-runners/new
 [4]: https://github.com/DataDog/helm-charts/blob/main/charts/private-action-runner/values.yaml
@@ -127,7 +127,7 @@ Set up your Private Action Runner using **one** of the following options.
 
 1. Restart the Docker container for the changes to take effect.
 
-[1]: https://docs.datadoghq.com/actions/private_actions/use_private_actions/?tab=kubernetes#overview
+[1]: https://docs.datadoghq.com/actions/private_actions/set_up_standalone/?tab=docker#install-the-runner
 [2]: https://app.datadoghq.com/actions/private-action-runners/new
 [3]: https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/how-to-configure-managed-identities?pivots=qs-configure-portal-windows-vm
 [4]: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/attach-iam-role.html
@@ -181,7 +181,7 @@ After completing the setup, jobs and cluster information should appear in [Data
 [1]: https://docs.databricks.com/aws/en/security/network/front-end/front-end-private-connect
 [2]: https://docs.datadoghq.com/actions/private_actions/
 [3]: https://docs.datadoghq.com/agent/guide/private-link/?tab=crossregionprivatelinkendpoints&site=us
-[4]: https://docs.datadoghq.com/actions/private_actions/#how-it-works
+[4]: https://docs.datadoghq.com/actions/private_actions/#private-action-runner
 [5]: https://docs.datadoghq.com/actions/private_actions/run_script/
 [6]: https://docs.datadoghq.com/agent/guide/azure-private-link/?site=us3
 [7]: https://docs.datadoghq.com/agent/guide/gcp-private-service-connect/?site=us5
@@ -193,7 +193,7 @@ After completing the setup, jobs and cluster information should appear in [Data
 [13]: https://app.datadoghq.com/integrations?search=databr&configPage=new&integrationId=databricks
 [14]: /data_observability/jobs_monitoring/databricks/#configure-the-datadog-databricks-integration
 [15]: https://app.datadoghq.com/data-jobs/
-[16]: https://docs.datadoghq.com/actions/private_actions/use_private_actions/?tab=docker#manage-access
+[16]: https://docs.datadoghq.com/actions/private_actions/enroll_runner/#manage-access-to-owned-runners
 [17]: https://app.datadoghq.com/organization-settings/remote-config
 [19]: /data_observability/jobs_monitoring/databricks/#install-the-datadog-agent
 [20]: https://docs.databricks.com/aws/en/admin/system-tables/

diff --git a/hugo/content/en/data_security/guide/tls_ciphers_deprecation.md b/hugo/content/en/data_security/guide/tls_ciphers_deprecation.md
@@ -22,33 +22,47 @@ To test your client's cipher suite compatibility, connect to [tls-config-test.da
 
 As of September 1, 2026, Datadog accepts only the following cipher suites:
 
+### TLS 1.2
+
 | Code         | IANA Name                                         |
 |--------------|---------------------------------------------------|
-| `0xC0,0x2B`  | `TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256`        |
-| `0xC0,0x2F`  | `TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256`          |
-| `0xC0,0x2C`  | `TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384`        |
-| `0xC0,0x30`  | `TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384`          |
-| `0xCC,0xA9`  | `TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256` |
-| `0xCC,0xA8`  | `TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256`   |
-| `0xC0,0x09`  | `TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA`          |
-| `0xC0,0x0A`  | `TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA`          |
-| `0x13,0x01`  | `TLS_AES_128_GCM_SHA256`                        |
-| `0x13,0x02`  | `TLS_AES_256_GCM_SHA384`                        |
-| `0x13,0x03`  | `TLS_CHACHA20_POLY1305_SHA256`                  |
+| `0xC0,0x2B`  | `TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256`         |
+| `0xC0,0x2F`  | `TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256`           |
+| `0xC0,0x2C`  | `TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384`         |
+| `0xC0,0x30`  | `TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384`           |
+| `0xCC,0xA9`  | `TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256`   |
+| `0xCC,0xA8`  | `TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256`     |
+
+### TLS 1.3
+
+| Code         | IANA Name                        |
+|--------------|----------------------------------|
+| `0x13,0x01`  | `TLS_AES_128_GCM_SHA256`         |
+| `0x13,0x02`  | `TLS_AES_256_GCM_SHA384`         |
+| `0x13,0x03`  | `TLS_CHACHA20_POLY1305_SHA256`   |
 
 {{< /site-region >}}
 
 {{< site-region region="gov,gov2" >}}
 
 Datadog accepts the following cipher suites for {{< region-param key="dd_site_name" >}}:
 
+### TLS 1.2
+
 | Code         | IANA Name                                  |
 |--------------|--------------------------------------------|
 | `0xC0,0x2F`  | `TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256`    |
 | `0xC0,0x30`  | `TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384`    |
 | `0xC0,0x2B`  | `TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256`  |
 | `0xC0,0x2C`  | `TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384`  |
 
+### TLS 1.3
+
+| Code         | IANA Name                        |
+|--------------|----------------------------------|
+| `0x13,0x01`  | `TLS_AES_128_GCM_SHA256`         |
+| `0x13,0x02`  | `TLS_AES_256_GCM_SHA384`         |
+
 {{< /site-region >}}
 
 {{< site-region region="us,eu,us3,us5,ap1,ap2,uk1" >}}
@@ -63,6 +77,8 @@ As of **September 1, 2026**, Datadog does not support the following cipher suite
 
 | Code         | IANA Name                                  | OpenSSL Name             |
 |--------------|--------------------------------------------|--------------------------|
+| `0xC0,0x09`  | `TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA`     | `ECDHE-ECDSA-AES128-SHA` |
+| `0xC0,0x0A`  | `TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA`     | `ECDHE-ECDSA-AES256-SHA` |
 | `0xC0,0x14`  | `TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA`       | `ECDHE-RSA-AES256-SHA`   |
 | `0xC0,0x13`  | `TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA`       | `ECDHE-RSA-AES128-SHA`   |
 | `0x00,0x9D`  | `TLS_RSA_WITH_AES_256_GCM_SHA384`          | `AES256-GCM-SHA384`      |