Usage analytics 

Opening the module 

Open Admin Tools > LLM > Analytics. The submodule sits next to the other LLM sections in the left-hand navigation and is admin-only, like the rest of the module.

Choosing a date range 

A range switcher at the top of the page selects the reporting window. The range is a plain ?range= link, so changing it is an ordinary page reload — there is no AJAX. Four presets are available:

KPI tiles 

A row of tiles summarises the selected range:

• Total cost — the summed estimated cost across the window.
• Total requests — the number of AI requests recorded.
• Total tokens — prompt plus completion tokens consumed.
• Providers — how many distinct providers were used.
• Models — how many distinct models were used.

These are totals for the chosen range, not all-time figures.

Cost and requests trend 

A line chart plots daily estimated cost and daily request count across the range. Days with no usage are filled in as zero so the line is continuous rather than skipping gaps.

Breakdown charts 

Three bar charts split the window's usage along different axes:

• By provider — cost and requests per service_provider (OpenAI, Anthropic, Ollama, …).
• By model — cost and requests per model. This dimension is new: it relies on the model_uid / model_id columns added to the usage table, so it only reflects usage recorded after that change.
• By service — cost and requests per service type (chat, vision, translation, speech, image).

Per-user table 

A table lists usage grouped by backend user, ordered by cost. Each row shows the user's request count, token total, and estimated cost for the selected range, plus a monthly-budget bar that visualises how much of their per-user budget (see Per-user AI budgets) they have consumed.

Requests made without an authenticated backend user (CLI, scheduler, be_user = 0) are grouped under a system row.

A note on cost 

All cost figures are estimated. They are computed from the model pricing you configured (cents per 1M tokens, applied to the recorded prompt/completion token split), not billed back from the provider. Treat them as a planning and trend signal, not as an invoice. Costs are captured at call time, so they reflect the pricing in effect when each request ran. See ADR-029: Usage Analytics Dashboard for the design rationale.

Specialized services (DALL·E, text-to-speech, Whisper, DeepL) still record their requests and units, but their cost is currently shown as 0 — token-based pricing does not apply to them yet. Streaming responses are not recorded at all, because chunked output has no single terminal token count to price.

Usage columns in the list views 

The Providers, Models, Configurations, and Tasks list views each carry three extra columns — Cost (30d), Requests (30d) and Tokens (30d) — summarising the last 30 days of usage for that row, so you can spot the heavy hitters without leaving the list.

Two attribution notes:

• The Providers column aggregates by adapter type (the value stored on each usage row), not by individual provider record — two providers that share an adapter therefore show the same figures.
• The Tasks column relies on per-task tracking: each task execution records its task_uid so usage rolls up to the task that triggered it. Calls made outside a task (direct API/service use) are not attributed to any task row.

Demo data for local development 

To populate the module with something to look at during local development, run the dev-only DDEV command:

ddev seed-usage

It generates roughly 90 days of realistic historic usage across providers, models, services, and users so the trend line, breakdown charts, and per-user table all have content. This command is for local DDEV environments only — do not run it against production data.