Implements
https://linear.app/codercom/issue/AIGOV-286/add-interception-cost-calculation-to-aibridge-token-usages
Adds spend attribution to AI Gateway. After the upstream response, each
token-usage record now captures the user's effective group, the
per-token prices in effect at that moment, and a computed cost — so
spend is recorded as an immutable, point-in-time snapshot.
Concretely, `aibridge_token_usages` gains `effective_group_id`,
`input_price_micros`, `output_price_micros`, `cache_read_price_micros`,
`cache_write_price_micros`, and `cost_micros`. When a usage record is
written, the effective group is resolved (per-user override, else the
deployment budget policy), the `(provider, model)` price is looked up
and snapshotted onto the row, and cost is computed from the
provider-reported token counts. A model that isn't in the price table
records its tokens with a `NULL` cost; any *other* resolution failure
fails the write, so a `NULL` cost unambiguously means "model not priced"
rather than "lookup errored."
All values are stored in micro-units (1 unit = 1,000,000 micro-units;
Phase 1 assumes USD, so 1 micro-unit = $0.000001). Prices are quoted per
million tokens.
This also grants the AI Bridge RBAC subject `read` on `ai_model_prices`
(the per-interception price lookup needs it; it previously only had
`update` for the startup seeder).
## Cost precision
Cost is computed per token category as `tokens × price / 1_000_000` with
integer division, then the four categories are summed. The division is
done **per category** (not once over the summed numerator) on purpose:
it keeps the per-category line items summing exactly to the stored total
— no "the parts don't add up to the whole" in reporting).
Integer division truncates sub-micro-unit fractions. For example, a
cheap model at $0.10 per million tokens is a price of `100_000`; 9
tokens cost `9 × 100_000 / 1_000_000 = 900_000 / 1_000_000 = 0` (the
true 0.9 micro-units floors to 0). At real list prices this rarely bites
— $3/M input is a price of `3_000_000`, so even a single token is 3
micro-units. The per-record under-count is bounded below 1 micro-unit
per category, so under $0.000004 total across the four categories, which
is acceptable for list-price-based cost approximation.
## Overflow safety
`cost_micros` is a `BIGINT` (int64), and the largest intermediate value
is a single category's `tokens × price` before division. int64's ceiling
is ≈ `9.223e18`.
- At a steep $75/M model (price `75_000_000`), overflow would require
~123 billion tokens in one response: `123e9 × 75e6 = 9.225e18`, just
over the limit. `122e9` stays under at `9.15e18`.
- A realistically maxed-out Opus 4.8 response (≈1M input + 128K output
at list prices) costs about $15, with a numerator around `1.5e13` —
roughly six orders of magnitude below the ceiling.
So overflow is unreachable from real token counts.
### Multi-currency support
In the future, we may encounter issues with multi-currency support,
especially when dealing with currencies that have very large exchange
rates relative to USD, for example:
IRR: ~1,300,000 IRR ≈ 1 USD
VND: ~26,000 VND ≈ 1 USD
For currencies with such large denominations, numeric overflow is
technically possible, considering that we have only about six orders of
magnitude of headroom before reaching the limit (see above).
## `effective_group_id` has no foreign key
`effective_group_id` records the group a spend was attributed to, as an
immutable historical fact. It is intentionally **not** a foreign key, so
the record survives deletion of the group.
Alternatives were considered and rejected:
- **`ON DELETE SET NULL`** would mutate an "immutable" record — deleting
a group silently erases that interception's attribution and under-counts
the group's historical spend.
- **`RESTRICT` / `NO ACTION`** would block group deletion entirely
(groups are hard-deleted).
- **`CASCADE`** would delete spend history when a group is deleted — the
worst outcome for an audit record.
There is also no insert-time check that the group still exists: the id
comes from a budget that was just resolved, meaning it was valid at some
point.
## Open question: group name snapshotting
Should we also snapshot the group *name* onto each record? Two options:
- **Denormalize it now** — readable in historical reports even after a
group is deleted, but the snapshot can drift from the current name on
rename, raising a "show point-in-time vs. current name" question.
- **Postpone until needed** — it's a purely additive column later, and
the name is display-only (not correctness-bearing like the price). The
cost: names of groups deleted before the column is added can't be
backfilled.
Leaning toward postponing until a concrete reporting need settles the
drift question.
Add `agent_firewall_session_id` (UUID NULL) and
`agent_firewall_sequence_number` (INT NULL) to `aibridge_interceptions`
with a partial index on `agent_firewall_session_id`. No FK to
`boundary_sessions` (soft reference, resolved at query time).
`RecordInterception` reads the new fields from the proto request (merged
in #25884) via `parseOptionalUUID` / `parseOptionalInt32` helpers.
> This PR was authored by Coder Agents.
## Problem
Centralized requests recorded *the first available key from the pool at
`CreateInterceptor` time* as `credential_hint`, so the interception
could be persisted in the database with a hint that didn't match the key
that actually served the request. The fix consists in storing, at
end-of-interception, the hint of the key that succeeded, or the last
attempted key if all keys are unavailable.
## Changes
- Add `Key.Hint()` and update `credential_hint` on every failover
attempt so it reflects the actually-used key.
- Stop pre-populating `credential_hint` at `CreateInterceptor`.
Centralized starts empty and is updated by the key failover loop.
- Persist the final hint via `RecordInterceptionEnded`; SQL updates
`credential_hint` only when `credential_kind = 'centralized'` so BYOK
keeps its start-time value.
- Log the actually-used hint on interception end/failure; start log uses
a `<keypool-pending>` placeholder for centralized.
> [!NOTE]
> Initially generated by Claude Opus 4.7, modified and reviewed by
@ssncferreira
Allows an `api_key_id` to be passed from a trusted in-memory transport
(currently: `chatd`) to `aibridged` for use in authenticating LLM
requests.
This value can _only_ be passed via context, and all users of the
in-memory transport _must_ provide it.
It can be used in conjunction with BYOK headers.
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
In order to allow Coder Agents to use AI Gateway in OSS, we need to rehome the `aibridged`\-related code into the AGPL path.
The HTTP API is only registered under enterprise so will still require the AI Governance Add-on to be present in order to use it, whereas Coder Agents uses an in-memory pipe to the same handlers.
Yevhenii Shcherbina