v2.2.0 — Metrics, and rates where the model is added

Added

• A Metrics destination in the rail, with three tabs. Usage shows spend per model over a
  chosen window of 7, 30, 90 or 365 days, with cache writes, truncated answers, time to first
  token, wall clock and a per-model cost — five of which the store has recorded since 2.0.0 and
  none of which reached a pixel. Live shows seven point-in-time gauges, starting with how many
  suspended actions are waiting for a person. Calls reads the tool_calls table, which had a
  writer, a pruner and a purge log and no reader at all.
  (#235,
  #232)
• pricing in config gives a model four rates in USD per million tokens — input, output, cache
  read and cache write — keyed "<provider>/<model>". Until one is set the Usage tab says no
  prices are configured rather than showing a spend of $0.00.
  (#235)
• gateway.metricsEnabled serves the same gauges in Prometheus text format at /metrics on the
  gateway's own port. Off by default. gateway.metricsToken sets the bearer token a scrape must
  present; with no token only a loopback bind is served, so enabling metrics on a port a reverse
  proxy fronts does not publish them.
  (#235)
• The Usage tab lists why calls failed, per error kind, status code and provider. The failure
  count was already shown; the reason behind it was recorded and never read.
  (#235)
• The Usage tab breaks the window down by what started the turns — chat, API, automations, memory,
  system. source was aggregated per day and readable only inside one heatmap cell's tooltip, so
  "what does automation cost me this month" meant opening thirty tooltips.
  (#235)
• A model row in the Usage tab expands to the four measurements the columns are checked against:
  how many calls the provider reported against how many were tokenized locally, output tokens per
  second of generation, measured against reported output, and the streamed split that time to
  first token is averaged over. (#235)

Fixed

• A cached token is no longer billed twice. prompt_tokens is the logical input and includes
  the cached halves, so charging it at the input rate and the cached count at the cache rate
  over-stated a warm cache badly — 5.7× on published Kimi K3 rates for a 90%-cached prompt. The
  three input buckets are now disjoint.
  (#235)
• A usage row names the provider that was configured rather than the class that made the call.
  OpenAICompatProvider serves every OpenAI-compatible API, so Moonshot, DeepSeek, Groq,
  OpenRouter and forty others were all recorded as openaicompat — which left "which provider is
  expensive" unanswerable and meant a rate set for moonshot/kimi-k3 could never match its own
  rows. Single-provider backends were wrong too: openai_codex recorded openaicodex.
  (#235)
• Both spellings of the pricing rate keys are accepted (inputPerMtok and inputPerMTok), and an
  entry that states no rate nanoinfra recognises reads as unpriced rather than as $0.00. A
  mistyped key used to produce a confident zero over a month of real spend.
  (#235)

Changed

• Model rates are set where the model is: Settings → Models → Pricing, with a live figure
  showing what the recorded window would have cost at the rates typed, a warning when the model
  reads cached tokens and the cache rate is still zero, and a note when two configurations name
  the same model and therefore share one bill. Settings → Providers → Default pricing sets rates
  for a whole provider, including a free checkbox — the one-edit answer for a local fleet.
  (#235)
• The Settings overview no longer opens with the token summary and the heatmap. They are the
  Usage tab now, and the overview keeps one row that leads there — which also ends the
  five-second re-aggregation of llm_calls that a settings page left open used to run.
  (#235)