litellm/tests/test_litellm/router_strategy/test_savings_baseline.py
tin-berri 4fcaf7d736
feat(spend): derive a default auto-router savings baseline from the hardest tier (#35907)
* feat(spend): derive a default auto-router savings baseline from the hardest tier

The savings driver shipped off by default: unless an operator names
litellm_settings.autorouter_savings_baseline_model, every auto-routed request
records $0.00 and the dashboard card never populates. Nobody discovers a knob
whose feature they have never seen work, so the default has to come from
somewhere the proxy already knows.

The router's own tier ladder is that place. Without a router a deployment runs
one model that can carry the hardest request it will see, so the derived
baseline is the priciest model in the hardest configured tier, REASONING when
present, otherwise the most severe tier the router actually defines. A cheap
tier is a choice the router made, not a ceiling it was bounded by.

An earlier draft of #35521 derived this per request and was deleted for it:
ranking candidates against the request that ran meant reading the request, and
every input shape it could take produced its own review finding. This
derivation is ranked against one fixed reference request instead, a cache-heavy
shape matching real auto-routed traffic, so it never reads the request at all.
Candidates still resolve through the router's deployments, so Azure base_model
and per-deployment pricing overrides rank correctly.

The deciding router records the result on its routing_decision, because one
model name can carry several tag-scoped routers with different tier ladders and
only the deciding instance knows which of them routed the request. The spend
writer's precedence is: configured baseline, then the recorded one, then off.
When the setting is present the router skips deriving entirely rather than
pricing candidates per decision only to be ignored.

Resolution never raises; an unresolvable baseline zeroes the driver instead of
failing a live request. Rows queued by a pod on the previous release carry no
recorded baseline and fall back to the configured setting, exactly as today.

The schema.d.ts regeneration also picks up the reminder_markers field that
UI-19232 (#35874) added without regenerating, so one hunk there is inherited
staleness rather than part of this change.

* fix(spend): cache the derived baseline, price it by deployment, keep it out of the routing preview

Three review findings on the derived baseline, addressed together because they
all sit on the same value's path from derivation to consumer.

Derivation walked and priced the hardest tier's whole pool inside a property
read on every routing decision, unbounded by pool size. The router now caches
the result per instance with a 30 second TTL, None results included, so the
hot path is a clock compare and a deployment edit still lands within a window
no operator watches closer than.

Ranking used each deployment's effective pricing but recorded only the model
name, so the spend writer priced the winning baseline at its public rate: a
hardest tier whose deployment carries a negotiated rate produced materially
wrong savings. The decision now also records savings_baseline_deployment_id
and the writer resolves it through Router.get_deployment_model_info, exactly
as the selected arm already does. The id is ignored whenever the configured
setting overrides the recorded baseline, since the setting names a model, not
a deployment.

/auto_router/test_routing returns the routing decision verbatim to team admins
while only authorizing the classifier and embedding models, so a derived
baseline would resolve another team's model-group alias into its backend
provider/model mapping and hand it to a caller never authorized for it. The
preview's throwaway router is built with derive_savings_baseline=False; its
decisions are never spend-tracked, so nothing is lost, and a source-pinning
test keeps the flag on the endpoint.

Also strips the explanatory comments this PR had added.

* refactor(spend): pin the derived baseline per router instance instead of a TTL

Creating or editing a router already rebuilds its ComplexityRouter instance,
through unregister and re-add on upsert and through the registry reset on a
full model_list load, so a value derived once per instance refreshes on
exactly the flows that can change it. That makes the TTL a solution to a
problem the rebuild lifecycle already solves, and it goes.

Derivation stays deferred to first use rather than running in __init__: during
a config load this router can be constructed before the deployments its tiers
name, and a baseline pinned at that moment would be empty for the process
lifetime.

The one behavior the TTL had that the pin does not: editing a tier deployment
without touching the router itself refreshed the baseline within a window.
That edit path rebuilds only the edited deployment's own strategies, so the
pin holds the old answer until the router is next saved or the config next
loads. A stale deployment id degrades to public-rate pricing rather than
failing, which is where every other unresolvable baseline already lands.
2026-08-04 22:36:45 -07:00

186 lines
7.9 KiB
Python

import pytest
from litellm.router import Router
from litellm.router_strategy.savings_baseline import (
Baseline,
canonical_model,
_models_in,
_most_expensive,
resolve_baseline,
)
@pytest.fixture
def parent() -> Router:
return Router(
model_list=[
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
{"model_name": "pool", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "pool", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
class TestCanonicalModel:
def test_qualifies_a_bare_name_with_the_provider_that_owns_it(self):
assert canonical_model("claude-opus-5") == "anthropic/claude-opus-5"
def test_keeps_an_already_qualified_name_qualified(self):
assert canonical_model("anthropic/claude-opus-5") == "anthropic/claude-opus-5"
def test_honours_a_separately_declared_provider(self):
assert canonical_model("claude-opus-5", "openai") == "openai/claude-opus-5"
def test_returns_none_for_a_name_no_provider_claims(self):
assert canonical_model("") is None
class TestModelsForGroup:
def test_resolves_a_group_to_the_models_its_deployments_call(self, parent):
assert [c.model for c in _models_in(parent, "cheap")] == ["anthropic/claude-haiku-4-5"]
def test_returns_every_deployment_in_a_pooled_group(self, parent):
assert sorted(c.model for c in _models_in(parent, "pool")) == [
"anthropic/claude-haiku-4-5",
"anthropic/claude-opus-5",
]
def test_treats_an_unknown_group_as_a_model_name(self, parent):
"""A tier can point straight at a provider model rather than a configured group."""
assert [c.model for c in _models_in(parent, "claude-opus-5")] == ["anthropic/claude-opus-5"]
class TestMostExpensive:
"""Ranking runs through the router, because what a deployment costs is the
router's answer to give: it merges configured prices over the built-in map."""
def test_picks_by_output_rate(self, parent):
picked = _most_expensive(parent, [Baseline("anthropic/claude-haiku-4-5"), Baseline("anthropic/claude-opus-5")])
assert picked.model == "anthropic/claude-opus-5"
def test_ignores_models_with_no_per_token_price(self, parent):
"""A free model as baseline would report the whole real spend as a loss."""
picked = _most_expensive(
parent, [Baseline("not-a-real-model-anywhere"), Baseline("anthropic/claude-haiku-4-5")]
)
assert picked.model == "anthropic/claude-haiku-4-5"
def test_returns_none_when_nothing_can_be_priced(self, parent):
assert _most_expensive(parent, [Baseline("not-a-real-model-anywhere")]) is None
def test_returns_none_for_an_empty_candidate_set(self, parent):
assert _most_expensive(parent, []) is None
class TestResolveBaseline:
def test_derives_the_priciest_candidate(self, parent):
assert resolve_baseline(parent, ["cheap", "top"]).model == "anthropic/claude-opus-5"
def test_never_raises_so_a_metric_cannot_fail_a_live_request(self):
"""Read on the routing path while decorating a request that is about to be
served; a dashboard counterfactual must not be able to take routing down."""
class Exploding:
@property
def model_name_to_deployment_indices(self):
raise RuntimeError("router is mid-reload")
assert resolve_baseline(Exploding(), ["anything"]) is None
def test_an_empty_candidate_set_zeroes_the_driver_rather_than_inventing_one(self, parent):
assert resolve_baseline(parent, []) is None
class TestDeploymentsPricedByBaseModel:
"""`litellm_params.model` is not always a model.
On Azure it is the deployment name, which is absent from the cost map, so pricing it
directly drops the candidate. If that candidate was the priciest, the baseline quietly
becomes the second priciest and every saving is understated; if the whole pool is
Azure, nothing prices and the driver reports zero with nothing at default log level
saying why. `model_info.base_model` is what names the real model, which is the chain
router.py already resolves pricing through.
"""
@staticmethod
def _router(*deployments: dict) -> Router:
return Router(model_list=list(deployments))
def test_model_info_base_model_is_preferred_over_the_deployment_name(self):
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert [c.model for c in _models_in(router, "big")] == ["azure/gpt-4.1"]
def test_litellm_params_base_model_is_the_other_accepted_spelling(self):
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment", "base_model": "azure/gpt-4.1"},
},
)
assert [c.model for c in _models_in(router, "big")] == ["azure/gpt-4.1"]
def test_a_deployment_without_a_base_model_still_prices_by_its_model(self):
router = self._router({"model_name": "big", "litellm_params": {"model": "anthropic/claude-opus-5"}})
assert [c.model for c in _models_in(router, "big")] == ["anthropic/claude-opus-5"]
def test_an_azure_deployment_can_win_the_priciest_candidate(self):
"""Without the base_model hop the Azure candidate never prices, so the cheaper
model wins by default and the reported saving shrinks."""
router = self._router(
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert resolve_baseline(router, ["cheap", "big"]).model == "azure/gpt-4.1"
def test_an_all_azure_pool_still_has_a_baseline(self):
"""Otherwise nothing prices, the driver is disabled and the card reads $0.00."""
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert resolve_baseline(router, ["big"]).model == "azure/gpt-4.1"
class TestDeploymentPricingOverrides:
"""A deployment may not be charged the public rate for the model it names."""
def test_a_configured_price_decides_the_baseline_not_the_public_rate(self):
"""A deployment configured far above its public rate is what the traffic would
really have cost. Ranking on the public rate picks the wrong counterfactual and
then prices it at a rate nobody pays."""
router = Router(
model_list=[
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
assert resolve_baseline(router, ["cheap", "top"]).model == "anthropic/claude-opus-5"
overridden = Router(
model_list=[
{
"model_name": "cheap",
"litellm_params": {
"model": "anthropic/claude-haiku-4-5",
"input_cost_per_token": 0.001,
"output_cost_per_token": 0.002,
},
},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
assert resolve_baseline(overridden, ["cheap", "top"]).model == "anthropic/claude-haiku-4-5"