llmgw keys cache --project P --ttl 1h [--any] | --off, and Cache answers… on
the console's project menu. The same request — project, endpoint, canonical
model and the body without stream, stream_options, user, metadata, store and
service_tier, keys sorted — is answered from the gateway's memory for the
TTL. Only reproducible answers (temperature 0 or a seed; embeddings, rerank,
score) unless --any; only a whole 200 from the model asked for, not a
fallback's, a canary's, or one a guardrail changed.
Looked up after the limits and the input guardrails; the output policy
judges a reused answer; no_store and stored conversations bypass it.
Streamed answers are kept put back together and streamed back as chunks.
x-llmgw-cache: hit|miss|bypass, Age on a hit, Cache-Control: no-cache or
x-llmgw-cache: no to bypass. A hit is recorded with no tokens and no cost,
marked cache_hit (migration 0027) and shown by ctl request. An LRU bounded
by router.cache.max_bytes and max_entry_bytes; llmgw_cache_requests_total,
llmgw_cache_bytes, llmgw_cache_entries.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Neither had a page. "We have GPUs, now what" was left to the reader, and what
llmgw stores was scattered across four documents and a changelog — which is
the same as not being written down when somebody is doing a security review.
docs/data.md describes the behaviour and leaves the legal mapping to whoever
holds the obligation. Every claim in it was checked against the source, and it
names where to look for each: usage rows carry no text field, keys are stored
hashed, conversations are off until a named project turns them on and cannot be
turned on without a retention window.
It also states the egress exactly, which took reading every outbound call to
say honestly: nothing on the serving path leaves the cluster, and the two
things that can reach outside — fetching weights, and ctl plan reading a model's
architecture — are both started by an operator. Weight fetching points wherever
you tell it, including an internal mirror.
docs/choosing-models.md is the opinion llmgw itself does not have. Three jobs
rather than one model, with the third — embeddings — being the one that gets
forgotten and the one that decides whether the deployment can answer questions
about your company or only questions in general.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
