Merge branch 'main' into 2025/10/27/unsafely_incompatible_with_s3_conditional_writes

Author: DaveCTurner

deploy-manage/deploy/elastic-cloud/elastic-cloud-hosted-planning.md

@@ -28,7 +28,7 @@ We recommend that you use at least two availability zones for production and thr
 Increasing the number of zones should not be used to add more resources. The concept of zones is meant for High Availability (2 zones) and Fault Tolerance (3 zones), but neither will work if the cluster relies on the resources from those zones to be operational.
 :::
 
-With multiple {{es}} nodes in multiple availability zones you have the recommended hardware. The next step is to ensure proper index replication. Each index, with the exception of searchable snapshot indices, should have one or more replicas. Use the [index settings API](https://www.elastic.co/docs/api/doc/elasticsearch/v8/operation/operation-indices-get-settings-1) to find any indices without replicas:
+With multiple {{es}} nodes in multiple availability zones you have the recommended hardware. The next step is to ensure proper index replication. Each index, with the exception of searchable snapshot indices, should have one or more replicas. Use the [index settings API]({{es-apis}}operation/operation-indices-get-settings) to find any indices without replicas:
 
 ```sh
 GET _all/_settings/index.number_of_replicas

explore-analyze/elastic-inference/eis.md

@@ -49,7 +49,7 @@ You can now use `semantic_text` with the new ELSER endpoint on EIS. To learn how
 #### Batch size
 
 Batches are limited to a maximum of 16 documents.
-This is particularly relevant when using the [_bulk API](https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-bulk) for data ingestion.
+This is particularly relevant when using the [_bulk API]({{es-apis}}operation/operation-bulk) for data ingestion.
 
 ## Pricing 
 

explore-analyze/elastic-inference/inference-api.md

@@ -11,11 +11,11 @@ products:
 
 # {{infer-cap}} integrations
 
-{{es}} provides a machine learning [{{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/v9/group/endpoint-inference) to create and manage {{infer}} endpoints that integrate with services such as {{es}} (for built-in NLP models like [ELSER](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md) and [E5](/explore-analyze/machine-learning/nlp/ml-nlp-e5.md)), as well as  popular third-party services like Amazon Bedrock, Anthropic, Azure AI Studio, Cohere, Google AI, Mistral, OpenAI, Hugging Face, and more.
+{{es}} provides a machine learning [{{infer}} API]({{es-apis}}group/endpoint-inference) to create and manage {{infer}} endpoints that integrate with services such as {{es}} (for built-in NLP models like [ELSER](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md) and [E5](/explore-analyze/machine-learning/nlp/ml-nlp-e5.md)), as well as  popular third-party services like Amazon Bedrock, Anthropic, Azure AI Studio, Cohere, Google AI, Mistral, OpenAI, Hugging Face, and more.
 
 You can use the default {{infer}} endpoints your deployment contains or create a new {{infer}} endpoint:
 
-- using the [Create an inference endpoint API](https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-inference-put)
+- using the [Create an inference endpoint API]({{es-apis}}operation/operation-inference-put)
 - through the [Inference endpoints UI](#add-inference-endpoints).
 
 ## Default {{infer}} endpoints [default-enpoints]

explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md

@@ -24,7 +24,7 @@ You can optimize your deplyoment for typical use cases, such as search and inges
 Each deployment will be fine-tuned automatically based on its specific purpose you choose.
 
 ::::{note}
-Since eland uses APIs to deploy the models, you cannot see the models in {{kib}} until the saved objects are synchronized. You can follow the prompts in {{kib}}, wait for automatic synchronization, or use the [sync {{ml}} saved objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml).
+Since eland uses APIs to deploy the models, you cannot see the models in {{kib}} until the saved objects are synchronized. You can follow the prompts in {{kib}}, wait for automatic synchronization, or use the [sync {{ml}} saved objects API]({{kib-apis}}group/endpoint-ml).
 ::::
 
 You can define the resource usage level of the NLP model during model deployment. The resource usage levels behave differently depending on [adaptive resources](../../../deploy-manage/autoscaling/trained-model-autoscaling.md#enabling-autoscaling-through-apis-adaptive-allocations) being enabled or disabled. When adaptive resources are disabled but {{ml}} autoscaling is enabled, vCPU usage of Cloud deployments derived from the Cloud console and functions as follows:

explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md

@@ -49,7 +49,7 @@ You need to provide an administrator username and its password and replace the `
 
 Since the `--start` option is used at the end of the Eland import command, {{es}} deploys the model ready to use. If you have multiple models and want to select which model to deploy, you can use the **{{ml-app}} > Model Management** user interface in {{kib}} to manage the starting and stopping of models.
 
-Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml).
+Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API]({{kib-apis}}group/endpoint-ml).
 
 ## Test the NER model [ex-ner-test]
 

explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md

@@ -53,7 +53,7 @@ You need to provide an administrator username and password and replace the `$CLO
 
 Since the `--start` option is used at the end of the Eland import command, {{es}} deploys the model ready to use. If you have multiple models and want to select which model to deploy, you can use the **{{ml-app}} > Model Management** user interface in {{kib}} to manage the starting and stopping of models.
 
-Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml).
+Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API]({{kib-apis}}group/endpoint-ml).
 
 ## Test the text embedding model [ex-text-emb-test]
 

explore-analyze/manage-access-to-ai-assistant.md

@@ -9,16 +9,14 @@ products:
   - id: cloud-serverless
 ---
 
-# Manage access to AI Features
-
-This page describes how to use the GenAI Settings page to control access to AI-powered features in your deployments in the following ways:
+# Manage access to AI features
 
+The GenAI Settings page lets you control access to AI-powered features in the following ways:
 
 - Manage which AI connectors are available in your environment. 
 - Enable or disable AI Assistant and other AI-powered features in your environment.
 - {applies_to}`stack: ga 9.2` {applies_to}`serverless: unavailable` Specify in which Elastic solutions the `AI Assistant for Observability and Search` and the `AI Assistant for Security` appear.
 
-
 ## Requirements
 
 - To access the **GenAI Settings** page, you need the `Actions and connectors: all` or `Actions and connectors: read` [{{kib}} privilege](/deploy-manage/users-roles/cluster-or-deployment-auth/kibana-privileges.md).
@@ -37,10 +35,16 @@ To manage these settings, go to the **GenAI Settings** page by using the navigat
 
 The **GenAI Settings** page has the following settings:
 
-- **Default AI Connector**: Use this setting to specify which connector is selected by default. This affects all AI-powered features, not just AI Assistant. 
-- **Disallow all other connectors**: Enable this setting to prevent connectors other than the default connector specified above from being used in your space. This affects all AI-powered features, not just AI Assistant. 
-- **AI feature visibility**: This button opens the current Space's settings page. Here you can specify which features should appear in your environment, including AI-powered features. 
-- **AI Assistant visibility**: This setting allows you to choose which AI Assistants are available to use and where. You can choose to only show the AI Assistants in their native solutions, in other {{kib}} pages (for example, Discover, Dashboards, and Stack Management), or select **Hide all assistants** to disable AI Assistant throughout {{kib}}.
+- **Default AI Connector**: Use this setting to specify which connector is selected by default when you access AI-powered features. Default setting: **No default connector**.
+  - If **No default connector** is selected, AI-powered features will default to the connector that was most recently used in your environment. 
+  - If **Elastic Managed LLM** or a custom LLM connector is selected, AI-powered features will default to that connector regardless of which connector was used most recently in your environment. 
+- **Disallow all other connectors**: When this setting is disabled, whenever you use an AI-powered feature you can select which connector should power it. Enable it to prevent connectors other than the default connector from being used in your space. Default setting: disabled.
+- **AI feature visibility**: This button opens the current Space's settings page, where you can specify which features are enabled in your environment, including AI-powered features. 
+- **AI Assistant visibility**: This setting allows you to choose which AI Assistants are available to use and where. There are several options:
+  - **Only in their solutions** (default): The Security AI Assistant appears in {{elastic-sec}}, and the {{obs-ai-assistant}} appears in {{es}} and {{observability}}.
+  - **{{obs-ai-assistant}} in other apps**: The {{obs-ai-assistant}} appears throughout {{kib}} regardless of solution. The Security AI Assistant does not appear anywhere.
+  - **Security AI Assistant in other apps**: The Security AI Assistant appears throughout {{kib}} regardless of solution. The {{obs-ai-assistant}} does not appear anywhere.
+  - **Hide all assistants**: Disables AI Assistant throughout {{kib}}.
 
 :::
 
@@ -51,7 +55,7 @@ The **GenAI Settings** page has the following settings:
 The **GenAI Settings** page has the following settings:
 
 - **Default AI Connector**: Click **Manage connectors** to open the **Connectors** page, where you can create or delete AI connectors. To update these settings, you need the `Actions and connectors: all` [{{kib}} privilege](/deploy-manage/users-roles/cluster-or-deployment-auth/kibana-privileges.md).
-- **AI feature visibility**: Click **Go to Permissions tab** to access the active {{kib}} space's settings page, where you can specify which features each custom user role has access to in your environment. This includes AI-powered features such as AI Assistant. 
+- **AI feature visibility**: Click **Go to Permissions tab** to access the active {{kib}} space's settings page, where you can specify which features each [user role](/deploy-manage/users-roles/cluster-or-deployment-auth/kibana-privileges.md) has access to in your environment. This includes AI-powered features. 
 
 :::
 

get-started/_snippets/observability-overview.md

@@ -0,0 +1,45 @@
+Elastic {{observability}} provides unified observability across applications and infrastructure. It combines logs, metrics, application traces, user experience data, and more into a single, integrated platform.
+This consolidation allows for powerful, cross-referenced analysis, enabling teams to move from detecting issues to understanding their root causes quickly and efficiently.
+By leveraging the search and analytics capabilities of {{es}}, it offers a holistic view of system behavior.
+
+Elastic {{observability}} embraces open standards like OpenTelemetry for flexible data collection, and offers scalable, cost-efficient data retention with tiered storage.
+
+For a complete overview, refer to [](/solutions/observability/get-started/what-is-elastic-observability.md).
+
+## Use cases [observability-use-cases]
+
+Apply {{observability}} to various scenarios to improve operational awareness and system reliability. 
+
+:::{dropdown} Use cases
+:open:
+* **Log monitoring and analytics:** Centralize and analyze petabytes of log data from any source. This enables quick searching, ad-hoc queries with ES|QL, and visualization with prebuilt dashboards to diagnose issues.
+* **Application Performance Monitoring (APM):** Gain code-level visibility into application performance. By collecting and analyzing traces with native OTel support, teams can identify bottlenecks, track errors, and optimize the end-user experience.
+* **Infrastructure monitoring:** Monitor metrics from servers, virtual machines, containers, and serverless environments with over 400 out-of-the-box integrations, including OpenTelemetry. This provides deep insights into resource utilization and overall system health.
+* **AI-powered log analysis with Streams**: Ingest raw logs in any format directly to a single endpoint without the need for complex agent management or manual parsing pipelines. Streams leverages AI to automatically parse, structure, and analyze log data on the fly.
+* **Digital experience monitoring:**
+  * **Real User Monitoring (RUM):** Capture and analyze data on how real users interact with web applications to improve perceived performance.
+  * **Synthetic monitoring:** Proactively simulate user journeys and API calls to test application availability and functionality.
+  * **Uptime monitoring:** Continuously check the status of services and applications to ensure they are available.
+* **Universal Profiling:** Gain visibility into system performance and identify expensive lines of code without application instrumentation, helping to increase CPU efficiency and reduce cloud spend.
+* **LLM Observability:** Gain deep insights into the performance, usage, and costs of Large Language Model (LLM) prompts and responses.
+* **Incident response and management:** Investigate operational incidents by correlating data from multiple sources, accelerating root cause analysis and resolution.
+:::
+
+To start your {{observability}} journey, read the [**Get started**](/solutions/observability/get-started.md) guide, which presents all the essential steps, with links to valuable resources. You can also browse the {{observability}} [**Quickstart guides**](/solutions/observability/get-started/quickstarts.md).
+
+## Core concepts [observability-concepts]
+
+At the heart of Elastic {{observability}} are several key components that enable its capabilities. 
+
+:::{dropdown} Concepts
+:open:
+* The three pillars of {{observability}} are:
+
+  * [**Logs:**](/solutions/observability/logs.md) Timestamped records of events that provide detailed, contextual information.
+  * [**Metrics:**](/solutions/observability/infra-and-hosts/analyze-infrastructure-host-metrics.md) Numerical measurements of system performance and health over time.
+  * [**Traces:**](/solutions/observability/apm/traces.md) Representations of end-to-end journeys of requests as they travel through distributed systems.
+* [**OpenTelemetry:**](/solutions/observability/apm/opentelemetry/index.md) {{Observability}} offers first-class, production-grade support for OpenTelemetry. This allows organizations to use vendor-neutral instrumentation and stream native OTel data without proprietary agents, leveraging the Elastic Distribution of OpenTelemetry (EDOT).
+* [**AIOps and AI Assistant:**](/solutions/observability/observability-ai-assistant.md) Leverages predictive analytics and an LLM-powered AI Assistant to reduce the time required to detect, investigate, and resolve incidents. This includes zero-config anomaly detection, pattern analysis, and the ability to surface correlations and root causes.
+* **[Alerting](/solutions/observability/incident-management/alerting.md) and [Cases](/solutions/observability/incident-management/cases.md):** Allows you to create  rules to detect complex conditions and perform actions. Cases allows teams to stay aware of potential issues and track investigation details, assign tasks, and collaborate on resolutions.
+* [**Service Level Objectives (SLOs):**](/solutions/observability/incident-management/service-level-objectives-slos.md) A framework for defining and monitoring the reliability of a service. Elastic {{observability}} allows for creating and tracking SLOs to ensure that performance targets are being met.
+:::