diff --git a/.apigentools-info b/.apigentools-info
index cd72d923930a7..3dd3636a258ea 100644
--- a/.apigentools-info
+++ b/.apigentools-info
@@ -4,13 +4,13 @@
     "spec_versions": {
         "v1": {
             "apigentools_version": "1.6.6",
-            "regenerated": "2024-10-02 16:24:00.237162",
-            "spec_repo_commit": "3f12bebc"
+            "regenerated": "2024-10-02 20:44:16.145760",
+            "spec_repo_commit": "76b7b19d"
         },
         "v2": {
             "apigentools_version": "1.6.6",
-            "regenerated": "2024-10-02 16:24:08.473625",
-            "spec_repo_commit": "3f12bebc"
+            "regenerated": "2024-10-02 20:44:24.139514",
+            "spec_repo_commit": "76b7b19d"
         }
     }
 }
\ No newline at end of file
diff --git a/data/api/v1/full_spec.yaml b/data/api/v1/full_spec.yaml
index 26db134dc64cd..c71799654ae89 100644
--- a/data/api/v1/full_spec.yaml
+++ b/data/api/v1/full_spec.yaml
@@ -30079,6 +30079,8 @@ paths:
 
         - network-performance: `network-performance alert`
 
+        - cloud cost: `cost alert`
+
 
         **Notes**:
 
diff --git a/data/api/v1/full_spec_deref.json b/data/api/v1/full_spec_deref.json
index a8a9bc47dafa9..c6c8ff0ecaa2a 100644
--- a/data/api/v1/full_spec_deref.json
+++ b/data/api/v1/full_spec_deref.json
@@ -971261,7 +971261,7 @@
         }
       },
       "post": {
-        "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
+        "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n- cloud cost: `cost alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
         "operationId": "CreateMonitor",
         "requestBody": {
           "content": {
diff --git a/data/api/v1/translate_actions.json b/data/api/v1/translate_actions.json
index 4823bbc6b341f..f7e986376a320 100644
--- a/data/api/v1/translate_actions.json
+++ b/data/api/v1/translate_actions.json
@@ -600,7 +600,7 @@
     "summary": "Get all monitor details"
   },
   "CreateMonitor": {
-    "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
+    "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n- cloud cost: `cost alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
     "summary": "Create a monitor",
     "request_description": "Create a monitor request body.",
     "request_schema_description": "Object describing a monitor."
diff --git a/static/resources/json/full_spec_v1.json b/static/resources/json/full_spec_v1.json
index a8a9bc47dafa9..c6c8ff0ecaa2a 100644
--- a/static/resources/json/full_spec_v1.json
+++ b/static/resources/json/full_spec_v1.json
@@ -971261,7 +971261,7 @@
         }
       },
       "post": {
-        "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
+        "description": "Create a monitor using the specified options.\n\n#### Monitor Types\n\nThe type of monitor chosen from:\n\n- anomaly: `query alert`\n- APM: `query alert` or `trace-analytics alert`\n- composite: `composite`\n- custom: `service check`\n- forecast: `query alert`\n- host: `service check`\n- integration: `query alert` or `service check`\n- live process: `process alert`\n- logs: `log alert`\n- metric: `query alert`\n- network: `service check`\n- outlier: `query alert`\n- process: `service check`\n- rum: `rum alert`\n- SLO: `slo alert`\n- watchdog: `event-v2 alert`\n- event-v2: `event-v2 alert`\n- audit: `audit alert`\n- error-tracking: `error-tracking alert`\n- database-monitoring: `database-monitoring alert`\n- network-performance: `network-performance alert`\n- cloud cost: `cost alert`\n\n**Notes**:\n- Synthetic monitors are created through the Synthetics API. See the [Synthetics API](https://docs.datadoghq.com/api/latest/synthetics/) documentation for more information.\n- Log monitors require an unscoped App Key.\n\n#### Query Types\n\n##### Metric Alert Query\n\nExample: `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #`\n\n- `time_aggr`: avg, sum, max, min, change, or pct_change\n- `time_window`: `last_#m` (with `#` between 1 and 10080 depending on the monitor type) or `last_#h`(with `#` between 1 and 168 depending on the monitor type) or `last_1d`, or `last_1w`\n- `space_aggr`: avg, sum, min, or max\n- `tags`: one or more tags (comma-separated), or *\n- `key`: a 'key' in key:value tag syntax; defines a separate alert for each tag in the group (multi-alert)\n- `operator`: <, <=, >, >=, ==, or !=\n- `#`: an integer or decimal number used to set the threshold\n\nIf you are using the `_change_` or `_pct_change_` time aggregator, instead use `change_aggr(time_aggr(time_window),\ntimeshift):space_aggr:metric{tags} [by {key}] operator #` with:\n\n- `change_aggr` change, pct_change\n- `time_aggr` avg, sum, max, min [Learn more](https://docs.datadoghq.com/monitors/create/types/#define-the-conditions)\n- `time_window` last\\_#m (between 1 and 2880 depending on the monitor type), last\\_#h (between 1 and 48 depending on the monitor type), or last_#d (1 or 2)\n- `timeshift` #m_ago (5, 10, 15, or 30), #h_ago (1, 2, or 4), or 1d_ago\n\nUse this to create an outlier monitor using the following query:\n`avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0`\n\n##### Service Check Query\n\nExample: `\"check\".over(tags).last(count).by(group).count_by_status()`\n\n- `check` name of the check, for example `datadog.agent.up`\n- `tags` one or more quoted tags (comma-separated), or \"*\". for example: `.over(\"env:prod\", \"role:db\")`; `over` cannot be blank.\n- `count` must be at greater than or equal to your max threshold (defined in the `options`). It is limited to 100.\nFor example, if you've specified to notify on 1 critical, 3 ok, and 2 warn statuses, `count` should be at least 3.\n- `group` must be specified for check monitors. Per-check grouping is already explicitly known for some service checks.\nFor example, Postgres integration monitors are tagged by `db`, `host`, and `port`, and Network monitors by `host`, `instance`, and `url`. See [Service Checks](https://docs.datadoghq.com/api/latest/service-checks/) documentation for more information.\n\n##### Event Alert Query\n\n**Note:** The Event Alert Query has been replaced by the Event V2 Alert Query. For more information, see the [Event Migration guide](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features/).\n\n##### Event V2 Alert Query\n\nExample: `events(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Process Alert Query\n\nExample: `processes(search).over(tags).rollup('count').last(timeframe) operator #`\n\n- `search` free text search string for querying processes.\nMatching processes match results on the [Live Processes](https://docs.datadoghq.com/infrastructure/process/?tab=linuxwindows) page.\n- `tags` one or more tags (comma-separated)\n- `timeframe` the timeframe to roll up the counts. Examples: 10m, 4h. Supported timeframes: s, m, h and d\n- `operator` <, <=, >, >=, ==, or !=\n- `#` an integer or decimal number used to set the threshold\n\n##### Logs Alert Query\n\nExample: `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `index_name` For multi-index organizations, the log index in which the request is performed.\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Composite Query\n\nExample: `12345 && 67890`, where `12345` and `67890` are the IDs of non-composite monitors\n\n* `name` [*required*, *default* = **dynamic, based on query**]: The name of the alert.\n* `message` [*required*, *default* = **dynamic, based on query**]: A message to include with notifications for this monitor.\nEmail notifications can be sent to specific users by using the same '@username' notation as events.\n* `tags` [*optional*, *default* = **empty list**]: A list of tags to associate with your monitor.\nWhen getting all monitor details via the API, use the `monitor_tags` argument to filter results by these tags.\nIt is only available via the API and isn't visible or editable in the Datadog UI.\n\n##### SLO Alert Query\n\nExample: `error_budget(\"slo_id\").over(\"time_window\") operator #`\n\n- `slo_id`: The alphanumeric SLO ID of the SLO you are configuring the alert for.\n- `time_window`: The time window of the SLO target you wish to alert on. Valid options: `7d`, `30d`, `90d`.\n- `operator`: `>=` or `>`\n\n##### Audit Alert Query\n\nExample: `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg` and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Pipelines Alert Query\n\nExample: `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### CI Tests Alert Query\n\nExample: `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n##### Error Tracking Alert Query\n\nExample(RUM): `error-tracking-rum(query).rollup(rollup_method[, measure]).last(time_window) operator #`\nExample(APM Traces): `error-tracking-traces(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Database Monitoring Alert Query**\n\nExample: `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.\n\n**Network Performance Alert Query**\n\nExample: `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #`\n\n- `query` The search query - following the [Log search syntax](https://docs.datadoghq.com/logs/search_syntax/).\n- `rollup_method` The stats roll-up method - supports `count`, `avg`, and `cardinality`.\n- `measure` For `avg` and cardinality `rollup_method` - specify the measure or the facet name you want to use.\n- `time_window` #m (between 1 and 2880), #h (between 1 and 48).\n- `operator` `<`, `<=`, `>`, `>=`, `==`, or `!=`.\n- `#` an integer or decimal number used to set the threshold.",
         "operationId": "CreateMonitor",
         "requestBody": {
           "content": {