Expose and graph Kong AI Metrics

This guide walks you through collecting AI metrics and sending them to Prometheus, and setting up the Grafana dashboard.

Overview

Kong’s AI Gateway (AI Proxy) calls LLM-based services according to the settings of the AI Proxy plugin. You can aggregate the LLM provider responses to count the number of tokens used by the AI Proxy. If you have defined input and output costs in the models, you can also calculate cost aggregation. The metrics details also expose if the requests have been cached by Kong Gateway, saving the cost of contacting the LLM providers, which improves performance.

Kong AI Gateway exposes metrics related to Kong and proxied upstream services in Prometheus exposition format, which can be scraped by a Prometheus Server.

Metrics tracked by this plugin are available on both the Admin API and Status API at the http://localhost:<port>/metrics endpoint. Note that the URL to those APIs is specific to your installation. See Accessing the metrics for more information.

The Kong Prometheus plugin records and exposes metrics at the node level. Your Prometheus server will need to discover all Kong nodes via a service discovery mechanism, and consume data from each node’s configured /metrics endpoint.

Grafana dashboard

AI Metrics exported by the plugin can be graphed in Grafana using a drop-in dashboard:

AI Grafana Dashboard

Available metrics

All the following AI LLM metrics are available per provider, model, cache, database name (if cached), embeddings provider (if cached), embeddings model (if cached), and workspace.

When ai_llm_metrics is set to true:

  • AI Requests: AI request sent to LLM providers.
  • AI Cost: AI Cost charged by LLM providers.
  • AI Tokens: AI Tokens counted by LLM providers. These are also available per token type in addition to the options listed previously.
  • AI LLM Latency: Time taken to return a response by LLM providers.
  • AI Cache Fetch Latency: Time taken to return a response from the cache.
  • AI Cache Embeddings Latency: Time taken to generate embedding during the cache.

AI metrics are disabled by default as it may create high cardinality of metrics and may cause performance issues. To enable them, set ai_metrics to true in the Prometheus plugin configuration.

Here is an example of output you could expect from the /metrics endpoint:

  1. curl -i http://localhost:8001/metrics

Response:

  1. HTTP/1.1 200 OK
  2. Server: openresty/1.15.8.3
  3. Date: Tue, 7 Jun 2020 16:35:40 GMT
  4. Content-Type: text/plain; charset=UTF-8
  5. Transfer-Encoding: chunked
  6. Connection: keep-alive
  7. Access-Control-Allow-Origin: *
  10. # HELP ai_llm_requests_total AI requests total per ai_provider in Kong
  11. # TYPE ai_llm_requests_total counter
  12. ai_llm_requests_total{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",workspace="workspace1"} 100
  13. # HELP ai_llm_cost_total AI requests cost per ai_provider/cache in Kong
  14. # TYPE ai_llm_cost_total counter
  15. ai_llm_cost_total{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",workspace="workspace1"} 50
  16. # HELP ai_llm_provider_latency AI latencies per ai_provider in Kong
  17. # TYPE ai_llm_provider_latency bucket
  18. ai_llm_provider_latency_ms_bucket{ai_provider="provider1",ai_model="model1",cache_status="",vector_db="",embeddings_provider="",embeddings_model="",workspace="workspace1",le="+Inf"} 2
  19. # HELP ai_llm_tokens_total AI tokens total per ai_provider/cache in Kong
  20. # TYPE ai_llm_tokens_total counter
  21. ai_llm_tokens_total{ai_provider="provider1",ai_model="model1",cache_status="",vector_db="",embeddings_provider="",embeddings_model="",token_type="prompt_tokens",workspace="workspace1"} 1000
  22. ai_llm_tokens_total{ai_provider="provider1",ai_model="model1",cache_status="",vector_db="",embeddings_provider="",embeddings_model="",token_type="completion_tokens",workspace="workspace1"} 2000
  23. ai_llm_tokens_total{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",token_type="total_tokens",workspace="workspace1"} 3000
  24. # HELP ai_cache_fetch_latency AI cache latencies per ai_provider/database in Kong
  25. # TYPE ai_cache_fetch_latency bucket
  26. ai_cache_fetch_latency{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",workspace="workspace1",le="+Inf"} 2
  27. # HELP ai_cache_embeddings_latency AI cache latencies per ai_provider/database in Kong
  28. # TYPE ai_cache_embeddings_latency bucket
  29. ai_cache_embeddings_latency{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",workspace="workspace1",le="+Inf"} 2
  30. # HELP ai_llm_provider_latency AI cache latencies per ai_provider/database in Kong
  31. # TYPE ai_llm_provider_latency bucket
  32. ai_llm_provider_latency{ai_provider="provider1",ai_model="model1",cache_status="hit",vector_db="redis",embeddings_provider="openai",embeddings_model="text-embedding-3-large",workspace="workspace1",le="+Inf"} 2

Note: If you don’t use any cache plugins, then cache_status, vector_db, embeddings_provider, and embeddings_model values will be empty.

Accessing the metrics

In most configurations, the Kong Admin API will be behind a firewall or would need to be set up to require authentication. Here are a couple of options to allow access to the /metrics endpoint to Prometheus:

  1. If the Status API is enabled, then its /metrics endpoint can be used. This is the preferred method.

  2. The /metrics endpoint is also available on the Admin API, which can be used if the Status API is not enabled. Note that this endpoint is unavailable when RBAC is enabled on the Admin API (Prometheus does not support Key-Auth to pass the token).