Timeseries queries Time-bucketed cache metrics over a window. Every timeseries query must include interval (or the deprecated intervalInSeconds). Each example below posts JSON to:
POST https://{your_control_plane_url}/api/svc/v1/llm-gateway/metrics/query
with Authorization: Bearer <your_api_key> and Content-Type: application/json. To keep the snippets short, only the JSON body is shown; the wrapper is identical to the Overview Quick Start .
The examples below pin the model side with {"fieldName": "virtualModelName", "operator": "IS_NULL", "value": true}. Flip to false for virtual-model-only series.
Hourly savings by namespace
Track cost savings per namespace over time: json={ "startTs" : "2026-04-21T00:00:00.000Z" , "endTs" : "2026-04-22T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 hour" , "aggregations" : [ { "type" : "sum" , "column" : "potentialCostSavings" } ], "groupBy" : [ "cacheNamespace" ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
Hourly p99 lookup latency by cache type
Watch for regressions in cache lookups: json={ "startTs" : "2026-04-21T00:00:00.000Z" , "endTs" : "2026-04-22T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 hour" , "aggregations" : [ { "type" : "p99" , "column" : "cacheLookupLatencyMs" } ], "groupBy" : [ "cacheType" ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
Hourly tokens read from cache
Track cache read volume per cache type: json={ "startTs" : "2026-04-21T00:00:00.000Z" , "endTs" : "2026-04-22T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 hour" , "aggregations" : [ { "type" : "sum" , "column" : "cacheReadInputTokens" } ], "groupBy" : [ "cacheType" ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
Volume by cacheLookupStatus over time: json={ "startTs" : "2026-04-21T00:00:00.000Z" , "endTs" : "2026-04-22T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 hour" , "aggregations" : [], "groupBy" : [ "cacheLookupStatus" ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
Daily savings over a week
Daily cost savings across a 7-day window: json={ "startTs" : "2026-04-14T00:00:00.000Z" , "endTs" : "2026-04-21T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 day" , "aggregations" : [ { "type" : "sum" , "column" : "potentialCostSavings" } ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
5-minute lookup latency in an incident window
Fine-grained breakdown to investigate a regression: json={ "startTs" : "2026-04-21T14:00:00.000Z" , "endTs" : "2026-04-21T16:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "5 minute" , "aggregations" : [ { "type" : "p99" , "column" : "cacheLookupLatencyMs" } ], "groupBy" : [ "cacheType" ], "filters" : [ { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
Hourly savings for semantic cache only
Combine cache-type filter with namespace breakdown: json={ "startTs" : "2026-04-21T00:00:00.000Z" , "endTs" : "2026-04-22T00:00:00.000Z" , "datasource" : "cacheMetrics" , "type" : "timeseries" , "interval" : "1 hour" , "aggregations" : [ { "type" : "sum" , "column" : "potentialCostSavings" } ], "groupBy" : [ "cacheNamespace" ], "filters" : [ { "fieldName" : "cacheType" , "operator" : "IN" , "value" : [ "semantic" ]}, { "fieldName" : "virtualModelName" , "operator" : "IS_NULL" , "value" : true} ] }
