You can view the available regional endpoints for the Cloud Logging API on the REST reference pages. For an example, see Method: projects.locations.buckets.list.
You can view the available regional endpoints for the Observability API and for the Telemetry API on their REST reference pages. For more information, see API overview.
You can view the available regional endpoints for the Error Reporting API on the REST reference pages. For an example, see Method: projects.events.list.
]]>Cloud Trace in Observability Analytics is generally available (GA). Observability Analytics lets you query and analyze your trace data by using SQL. You can chart your query results, save your queries, and join your trace and log data.
For more information, see the following documents:
The Observability API is generally available (GA). This API lets you configure the following:
For more information, see the following documents:
Trace scopes are generally available (GA). For more information, see Create and manage trace scopes.
The following remote MCP servers automatically generate a trace span for
tools/call operations. These spans can help you understand the behavior of
your agentic applications. For more information, see
Investigate MCP calls using Trace.
Starting with version 2.66.0, the Ops Agent can export your logs and metrics by using the OpenTelemetry-based Telemetry API rather than by using the Cloud Logging API and Cloud Monitoring API. During the preview Preview period, you can opt-in to using the Telemetry API. For more information, see Use the Telemetry API.
Starting with version 2.66.0, the Ops Agent can export your metrics and logs by using the OpenTelemetry-based Telemetry API rather than by using the Cloud Monitoring API and Cloud Logging API. During the preview Preview period, you can opt-in to using the Telemetry API. For more information, see Use the Telemetry API.
]]>Google Cloud Observability has expanded the supported locations for observability buckets, which store your trace data, to include the following:
For a list of supported locations, see Locations for observability buckets.
]]>The following remote MCP servers automatically generate a trace span for
tools/call operations. These spans can help you understand the behavior of
your agentic applications. For more information, see
Investigate MCP calls using Trace.
Google Cloud Observability has expanded the supported locations for observability buckets, which store your trace data, to include the following:
For a list of supported locations, see Locations for observability buckets.
]]>Cloud Trace is a service covered by the Cloud Observability (Monitoring, Logging, Trace) Service Level Agreement (SLA).
]]>The Cloud Monitoring API MCP server is generally available (GA). To learn about using the Monitoring MCP server to let agents and AI applications interact with your metrics data, see Use the Cloud Monitoring remote MCP server.
]]>The Cloud Logging API MCP server is generally available (GA). To learn about using the Logging MCP server to let agents and AI applications interact with your log entries, see Use the Cloud Logging remote MCP server.
Application Monitoring in Google Cloud provides both agent observability and application observability. Your Application Monitoring dashboards display performance metrics, including the error rates and token usage of your AI resources. Those metrics can help you understand the health and performance of your AI resources.
To learn more, see the following:
]]>Your trace data can be encrypted with a customer-managed encryption key (CMEK). To enable CMEK, set a default storage location and for that location, set a default Cloud Key Management Service key.
You can set these defaults for an organization, a folder, or a project. When set for an organization or folder, the settings apply to that resource and to its descendants. For more information, see Set defaults for observability buckets.
When you configure a default storage location, you control the location of your new observability buckets. These buckets store your trace data.
You can set a default storage location for an organization, a folder, or a project. When set for an organization or folder, the setting applies to that resource and to its descendants. For more information, see Set defaults for observability buckets.
]]>Application Monitoring can display a single, dynamic topology map showing your App Hub applications and your registered and discovered services and workloads. This interactive map identifies services and workloads that have open incidents. It also displays the error rates and P95 latency between your services and workloads.
To learn more, see the following:
]]>Use Cloud Trace to troubleshoot your MCP server usage, tool failures, and latency causes. For more information, see Investigate MCP calls using Trace.
]]>Google Cloud CLI lets you configure trace scopes, manage observability buckets, and set default observability settings. These features are in Public Preview. For more information, see the following documents:
Configure trace scopes by using the Google Cloud console, the Google Cloud CLI, Terraform, or the Observability API. For more information, see Create and manage trace scopes.
Manage trace storage by using the Google Cloud CLI or the Observability API. For more information, see Manage trace storage.
Configure default settings by using the Google Cloud CLI, Terraform, or the Observability API. For more information, see Set defaults for observability buckets.
You can now ingest OTLP-formatted logs into Cloud Logging by using an OpenTelemetry Collector, an OTLP exporter, and the Telemetry API. For more information, see OTLP log ingestion overview. The Telemetry API for log ingestion is in Preview.
]]>Cloud Logging adds support for the ca multi-region. For a complete list
of supported regions, see Supported regions.
Application Monitoring has added a Services and Workloads tab, which lists your registered and discovered services and workloads. From this tab, you can do the following:
Agent or
MCP server.To learn more, see the following:
]]>The filter capabilities for log views have been extended to include support for disjunctive clauses, negation statements, and labels. To learn more, see Filters for log views.
Application Monitoring has added support for the following resources:
Additionally, dashboards for Kubernetes workloads display L4 and L7 traffic metrics, when both are available. For more information, see Application Monitoring supported infrastructure.
]]>For any new project that is created on or after March 30, 2026, if the project enables the Cloud Logging API, then Google Cloud Observability also enables the Telemetry API.
For any new project that is created on or after March 30, 2026, if the project enables the Cloud Monitoring API, Telemetry API.
For any new project that is created on or after March 30, 2026, if the project enables the Cloud Trace API, then Google Cloud Observability also enables the Telemetry API.
You can use the Cloud Trace API MCP server to let agents and AI applications interact with your trace data. This feature is in Preview.
]]>You can use the Error Reporting API MCP server to let agents and AI applications interact with your error data. This feature is in Preview.
]]>The Telemetry API's supports up to 60,000 metric-ingestion requests per minute per region. The regional quota replaces the global quota. To learn more, see Telemetry API quotas and limits for metric ingestion.
The Telemetry API supports trace ingestion of up to 2.4GB per minute for the following regions:
For all other regions, the Telemetry API supports trace ingestion of up to 300 MB per minute.
These regional byte-based quotas replace a global quota which limited the number of requests per minute. To learn more, see Telemetry API limits and quotas.
]]>Google Cloud Observability has expanded the supported locations for observability buckets, which store your trace data, to include the following:
For a list of supported locations, see Locations for observability buckets.
You can create alerting policies that monitor the results of your SQL queries. For more information, see Monitor your SQL query results with an alerting policy. This feature is in public preview.
]]>The automatic backfill operation performed on a log bucket that has been upgraded to use Log Analytics has been temporarily paused. To manually initiate the backfill operation, contact Cloud Customer Care.
]]>You can configure legend templates for PromQL-formatted charts. To learn more, see Configure the name of a legend column.
You can send trace data to your Google Cloud project by using the Cloud Trace API or the Telemetry API. These two APIs are enabled individually.
If you send trace data to the Telemetry API endpoint, then Google Cloud Observability requires that the Cloud Trace API be enabled on your Google Cloud project before it stores the trace data. If the Cloud Trace API is disabled, then Google Cloud Observability discards the trace data.
To learn more, see APIs that ingest trace data.
]]>The SQL queries issued by Observability Analytics can now use a system-defined variable which resolves to the project ID. If a dashboard template uses the project ID variable, then you don't need to update the SQL query after installing the template.
For more information, see the following documents:
]]>For organizations, folders, and projects, you can now configure default settings for observability buckets. Default settings let you specify the following for new observability buckets:
This feature is in public preview. To learn more, see Set defaults for observability buckets.
You can now configure observability buckets to be in the following locations:
Your trace data is stored in an observability bucket. To learn more, see Trace storage overview.
]]>Starting February 18, 2026, trace sinks are deprecated. For more information, see Export trace spans with sinks deprecation.
You can use the Observability Analytics page, which provides a SQL query interface, to query both your trace and log data. To learn more, see the following documents:
To migrate to using Observability Analytics page from a sink-based export of trace data to BigQuery, see Migrate to Observability Analytics.
To query your trace data by using the Observability Analytics page, see Query and analyze traces.
To query your trace data by using BigQuery services, see Query a linked BigQuery dataset.
You no longer need to configure BigQuery reservation assignments to create SQL-based alerting policies or run Log Analytics queries on BigQuery slots. These queries now use on-demand slots by default if no BigQuery reservation assignment exists.
For more information, see the following documents:
]]>You can use the Cloud Logging API MCP server to let agents and AI applications interact with your log entries. This feature is in Preview.
]]>You can use the Cloud Monitoring API MCP server to let agents and AI applications interact with your time series data. This feature is in Preview.
You can now ingest OTLP metrics into Cloud Monitoring by using an OpenTelemetry Collector, an OTLP exporter, and the Telemetry API. For more information, see OTLP metric ingestion overview. The Telemetry API for metric ingestion is in Preview.
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