Stéphane Tailland
05cf2bcce1
Four settings, all of them off, which is why the AI gateway produced
HTTP-level telemetry and nothing about the model.
logging.log_payloads the prompt and the completion, in the log
records and — since OTel instrumentation is on
here — in the spans too
logging.log_statistics model usage and token counts
prometheus.ai_metrics kong_ai_llm_* : requests, tokens by type, cost,
provider latency, per model and per consumer
input_cost / output_cost cost per 1M tokens, which is what makes
kong_ai_llm_cost_total mean anything
Also removes this morning's pre-function/post-function body capture from
this gateway. It was the wrong tool for the LLM route: Kong's own
implementation reassembles a streamed SSE answer, while accumulating raw
chunks in kong.ctx.shared does not. The five business domains keep theirs
— their MCP traffic is not streamed the same way.
The costs are a snapshot, checked against OpenRouter's price list for
google/gemini-3.7-flash on 2026-08-27: $0.375 in, $1.875 out per 1M
tokens. They are substitution variables rather than literals precisely
because a stale figure produces confidently wrong money rather than an
error.
Note what this does NOT fix: usage is missing on streamed responses
because the OpenAI-compatible API only reports it when the caller asks
for stream_options.include_usage, and that is LibreChat's request to
make, not Kong's. Expect token counts to stay partial until then.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
platform-llm
Platform-owned llm bounded context — Gateway infrastructure and the
shared LLM proxy in one repo. Never split per functional domain, and never
split between infra/routing here either: both are owned by the same
(platform) team, unlike catalog's dataplane-catalog/catalog-api split
(feature team vs. platform team, a real access boundary that doesn't exist
for this domain).
What it does
Fronts OpenRouter (https://openrouter.ai) via one proxy route
(/llm/v1/chat/completions) — the client's own model field selects among
any OpenRouter-hosted model, not just a fixed set Kong picks.
Active policies:
ai-rate-limiting-advanced, token-based, both global (across all callers) and per-client (per authenticated consumer).ai-prompt-guard(regex) andai-semantic-prompt-guard(embedding-based, backed by Redis Stack) for prompt-injection/jailbreak safeguarding.- Two-tier auth:
key-auth(static, shared key) for chatbot UIs that can't do OIDC on their outbound LLM connection (LibreChat and similar tools — structural limitation of that whole category, not fixable client-side),openid-connectfor agents built in-house. Either succeeds; a request passing neither is rejected (request-terminationon the fallback anonymous consumer).
Not yet active: PII redaction (ai-sanitizer) — needs a private Kong
Cloudsmith registry image and an AI Gateway Enterprise entitlement, blocked
pending that access. Everything else in this repo is unaffected.
Structure
dev/ai-gateway-kustomization.yaml+dev/ai-gateway-externalsecret.yaml— Gateway/DataPlane/Control Plane infra (same sharedk8s-platformtemplate every domain uses).dev/proxy-kustomization.yaml+dev/proxy-externalsecret.yaml— the LLM proxy itself (./kong).dev/gitrepository.yaml— this repo, self-referential.