diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index da420ab05787..2a01135216d3 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -299,6 +299,45 @@ def _get_additional_costs(
     return None
 
 
+def _recorded_cache_storage_token_hours(litellm_logging_obj: LitellmLoggingObject | None) -> object:
+    return litellm_logging_obj.context_cache_storage_token_hours if litellm_logging_obj else 0.0
+
+
+def _context_cache_storage_cost(
+    litellm_logging_obj: LitellmLoggingObject | None,
+    *,
+    requested_model: str | None,
+    completion_response: object | None,
+    base_model: str | None,
+    custom_llm_provider: str | None,
+    router_model_id: str | None,
+    region_name: str | None,
+) -> float:
+    token_hours: Final = _recorded_cache_storage_token_hours(litellm_logging_obj)
+    if not isinstance(token_hours, int | float) or token_hours <= 0:
+        return 0.0
+    underlying_model: Final = select_model_name_for_cost_calc(
+        model=requested_model,
+        completion_response=completion_response,
+        base_model=base_model,
+        custom_llm_provider=custom_llm_provider,
+        region_name=region_name,
+    )
+    rate: Final = next(
+        (
+            price
+            for source in (
+                _deployment_model_info(litellm_logging_obj, custom_pricing=True, router_model_id=router_model_id),
+                _cost_map_model_info(underlying_model, custom_llm_provider) if underlying_model else None,
+            )
+            if source is not None
+            and isinstance(price := source.get("cache_storage_cost_per_token_per_hour"), int | float)
+        ),
+        0.0,
+    )
+    return token_hours * rate
+
+
 def _transcription_usage_has_token_details(
     usage_block: Usage | None,
 ) -> bool:
@@ -1482,6 +1521,7 @@ def completion_cost(
             region_name=region_name,
         )
 
+        requested_model: Final = model
         potential_model_names: Final = [
             selected_model,
             _get_response_model(completion_response),
@@ -1871,6 +1911,20 @@ def completion_cost(
                     )
                 else:
                     additional_costs = None
+                _storage_response: ModelResponse | None = (
+                    completion_response if isinstance(completion_response, ModelResponse) else None
+                )
+                _storage_provider: str | None = custom_llm_provider if isinstance(custom_llm_provider, str) else None
+                _storage_region: str | None = region_name if isinstance(region_name, str) else None
+                cache_storage_cost: float = _context_cache_storage_cost(
+                    litellm_logging_obj,
+                    requested_model=requested_model,
+                    completion_response=_storage_response,
+                    base_model=base_model,
+                    custom_llm_provider=_storage_provider,
+                    router_model_id=router_model_id,
+                    region_name=_storage_region,
+                )
 
                 _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
                 cost_for_built_in_tools = StandardBuiltInToolCostTracking.get_cost_for_built_in_tools(
@@ -1883,6 +1937,12 @@ def completion_cost(
                 _final_cost += cost_for_built_in_tools
                 if additional_costs:
                     _final_cost += sum(additional_costs.values())
+                if cache_storage_cost > 0:
+                    _final_cost += cache_storage_cost
+                    if additional_costs is None:
+                        additional_costs = {"cache_storage_cost": cache_storage_cost}
+                    else:
+                        additional_costs["cache_storage_cost"] = cache_storage_cost
 
                 original_cost = _final_cost
                 if litellm.cost_discount_config:
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 715e492b159e..f034107b8864 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -575,6 +575,10 @@ class Logging(LiteLLMLoggingBaseClass):
     classifier_input: Mapping[str, JsonValue] | None = None
     baseline_cache_context: "BaselineCacheContext | None" = None
     baseline_observation: "CapturedBaselineObservation | None" = None
+    context_cache_storage_token_hours: float = 0.0
+
+    def record_context_cache_storage(self, token_hours: float) -> None:
+        self.context_cache_storage_token_hours = token_hours
 
     @property
     def _defer_async_logging(self) -> bool:
diff --git a/litellm/llms/vertex_ai/context_caching/storage_cost.py b/litellm/llms/vertex_ai/context_caching/storage_cost.py
new file mode 100644
index 000000000000..7cb74296f94f
--- /dev/null
+++ b/litellm/llms/vertex_ai/context_caching/storage_cost.py
@@ -0,0 +1,56 @@
+from datetime import datetime
+from typing import Final
+
+from pydantic import BaseModel, ConfigDict, ValidationError
+
+DEFAULT_CONTEXT_CACHE_TTL_SECONDS: Final = 3600.0
+_SECONDS_PER_HOUR: Final = 3600.0
+
+
+class _CachedContentUsage(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    totalTokenCount: int = 0
+
+
+class _CreatedCachedContent(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    usageMetadata: _CachedContentUsage = _CachedContentUsage()
+    createTime: datetime | None = None
+    expireTime: datetime | None = None
+
+
+def _reported_lifetime_seconds(created: _CreatedCachedContent) -> float | None:
+    if created.createTime is None or created.expireTime is None:
+        return None
+    seconds: Final = (created.expireTime - created.createTime).total_seconds()
+    return seconds if seconds > 0 else None
+
+
+def _requested_ttl_seconds(requested_ttl: str | None) -> float | None:
+    if requested_ttl is None or not requested_ttl.endswith("s"):
+        return None
+    try:
+        seconds: Final = float(requested_ttl[:-1])
+    except ValueError:
+        return None
+    return seconds if seconds > 0 else None
+
+
+def context_cache_storage_token_hours(created_cached_content: object, requested_ttl: str | None) -> float:
+    """Token-hours a newly created cachedContents resource is billed for over its whole TTL.
+
+    The lifetime comes from the create response (expireTime - createTime), then the TTL that was
+    requested, then the provider default of one hour.
+    """
+    try:
+        created: Final = _CreatedCachedContent.model_validate(created_cached_content)
+    except ValidationError:
+        return 0.0
+    lifetime_seconds: Final = (
+        _reported_lifetime_seconds(created)
+        or _requested_ttl_seconds(requested_ttl)
+        or DEFAULT_CONTEXT_CACHE_TTL_SECONDS
+    )
+    return created.usageMetadata.totalTokenCount * lifetime_seconds / _SECONDS_PER_HOUR
diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
index 2d35dd9b480f..7e33bde0c09b 100644
--- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
+++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
@@ -21,6 +21,7 @@
 
 from ..common_utils import VertexAIError, get_vertex_base_url
 from ..vertex_llm_base import VertexBase
+from .storage_cost import context_cache_storage_token_hours
 from .transformation import (
     cached_messages_end_on_supported_turn,
     separate_cached_messages,
@@ -425,6 +426,9 @@ def check_and_create_cache(
             raise VertexAIError(status_code=408, message="Timeout error occurred.")
 
         raw_response_cached: Final = response.json()
+        logging_obj.record_context_cache_storage(
+            context_cache_storage_token_hours(raw_response_cached, cached_content_request_body.get("ttl"))
+        )
         cached_content_response_obj: Final = VertexAICachedContentResponseObject(
             name=raw_response_cached.get("name"), model=raw_response_cached.get("model")
         )
@@ -581,6 +585,9 @@ async def async_check_and_create_cache(
             raise VertexAIError(status_code=408, message="Timeout error occurred.")
 
         raw_response_cached: Final = response.json()
+        logging_obj.record_context_cache_storage(
+            context_cache_storage_token_hours(raw_response_cached, cached_content_request_body.get("ttl"))
+        )
         cached_content_response_obj: Final = VertexAICachedContentResponseObject(
             name=raw_response_cached.get("name"), model=raw_response_cached.get("model")
         )
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index f34606ce1272..ecdee276b4d6 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -1,5 +1,6 @@
 {
     "sample_spec": {
+        "cache_storage_cost_per_token_per_hour": 0.0,
         "code_interpreter_cost_per_session": 0.0,
         "computer_use_input_cost_per_1k_tokens": 0.0,
         "computer_use_output_cost_per_1k_tokens": 0.0,
@@ -27091,6 +27092,7 @@
         "cache_read_input_token_cost": 3e-08,
         "cache_read_input_token_cost_flex": 3e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27442,6 +27444,7 @@
     },
     "gemini-3.1-flash-lite-preview": {
         "cache_read_input_token_cost": 2.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27496,6 +27499,7 @@
         "cache_read_input_token_cost_batches": 1.25e-08,
         "cache_read_input_token_cost_flex": 1.25e-08,
         "cache_read_input_token_cost_priority": 4.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "input_cost_per_token_batches": 1.25e-07,
@@ -27557,6 +27561,7 @@
         "cache_read_input_token_cost_batches": 1.5e-08,
         "cache_read_input_token_cost_flex": 1.5e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 3e-07,
         "input_cost_per_token_batches": 1.5e-07,
         "input_cost_per_token_flex": 1.5e-07,
@@ -27651,6 +27656,7 @@
         "cache_read_input_token_cost": 1e-08,
         "cache_read_input_token_cost_flex": 1e-08,
         "cache_read_input_token_cost_priority": 1.8e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 3e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27939,6 +27945,7 @@
         "cache_read_input_token_cost_above_200k_tokens_priority": 4.5e-07,
         "cache_read_input_token_cost_flex": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.25e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "input_cost_per_token": 1.25e-06,
         "input_cost_per_token_above_200k_tokens": 2.5e-06,
         "litellm_provider": "vertex_ai-language-models",
@@ -28041,6 +28048,7 @@
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
         "cache_read_input_token_cost_flex": 2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28152,6 +28160,7 @@
         "output_cost_per_token_above_200k_tokens_priority": 3.24e-05,
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28202,6 +28211,7 @@
         "input_cost_per_audio_token_priority": 1.8e-06,
         "output_cost_per_token_priority": 5.4e-06,
         "cache_read_input_token_cost_priority": 9e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28274,7 +28284,8 @@
         "output_cost_per_token_batches": 4.5e-06,
         "input_cost_per_token_flex": 7.5e-07,
         "output_cost_per_token_flex": 4.5e-06,
-        "cache_read_input_token_cost_flex": 7.5e-08
+        "cache_read_input_token_cost_flex": 7.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06
     },
     "vertex_ai/gemini-3.6-flash": {
         "prompt_cache_min_tokens": 4096,
@@ -28326,6 +28337,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2026-11-19",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -28386,6 +28398,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2027-01-28",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -28446,6 +28459,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28459,6 +28473,7 @@
         "cache_read_input_token_cost_batches": 7.5e-08,
         "cache_read_input_token_cost_flex": 7.5e-08,
         "cache_read_input_token_cost_priority": 2.7e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 1.5e-06,
         "input_cost_per_token_batches": 7.5e-07,
         "input_cost_per_token_flex": 7.5e-07,
@@ -28592,6 +28607,7 @@
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
         "cache_read_input_token_cost_flex": 2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30449,6 +30465,7 @@
         "input_cost_per_audio_token_priority": 1.8e-06,
         "output_cost_per_token_priority": 5.4e-06,
         "cache_read_input_token_cost_priority": 9e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30552,7 +30569,8 @@
         "output_cost_per_token_batches": 4.5e-06,
         "input_cost_per_token_flex": 7.5e-07,
         "output_cost_per_token_flex": 4.5e-06,
-        "cache_read_input_token_cost_flex": 7.5e-08
+        "cache_read_input_token_cost_flex": 7.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06
     },
     "gemini-3.6-flash": {
         "prompt_cache_min_tokens": 4096,
@@ -30604,6 +30622,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2026-11-19",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -30664,6 +30683,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2027-01-28",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -30724,6 +30744,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30737,6 +30758,7 @@
         "cache_read_input_token_cost_batches": 7.5e-08,
         "cache_read_input_token_cost_flex": 7.5e-08,
         "cache_read_input_token_cost_priority": 2.7e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 1.5e-06,
         "input_cost_per_token_batches": 7.5e-07,
         "input_cost_per_token_flex": 7.5e-07,
@@ -50905,6 +50927,7 @@
     },
     "vertex_ai/gemini-3.1-flash-lite-preview": {
         "cache_read_input_token_cost": 2.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -50959,6 +50982,7 @@
         "cache_read_input_token_cost_batches": 1.25e-08,
         "cache_read_input_token_cost_flex": 1.25e-08,
         "cache_read_input_token_cost_priority": 4.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "input_cost_per_token_batches": 1.25e-07,
@@ -51021,6 +51045,7 @@
         "cache_read_input_token_cost_batches": 1.5e-08,
         "cache_read_input_token_cost_flex": 1.5e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 3e-07,
         "input_cost_per_token_batches": 1.5e-07,
         "input_cost_per_token_flex": 1.5e-07,
diff --git a/litellm/types/router.py b/litellm/types/router.py
index a66c4571b393..4da354fd8182 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -274,6 +274,7 @@ class ModelInfo(MirroredPricingParams):
     cost_per_ptu_per_hour: float | None = None
     ptu_effective_from: datetime.datetime | None = None
     ptu_effective_to: datetime.datetime | None = None
+    cache_storage_cost_per_token_per_hour: float | None = None
 
     # when tag-based routing's "!" or "&" constraints eliminate every deployment
     # in this model group, fall back to the default-tagged pool instead of
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index a99c22e9479f..352b8ad4cb38 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -324,6 +324,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
     cache_creation_input_token_cost_above_200k_tokens_batches: ReadOnly[float | None]
     cache_creation_input_token_cost_above_100k_tokens_batches: ReadOnly[float | None]
     cache_creation_input_token_cost_above_272k_tokens_batches: ReadOnly[float | None]
+    cache_storage_cost_per_token_per_hour: ReadOnly[float | None]
     # Smallest prefix this model will actually cache, whatever caching mechanism its provider uses.
     # Absent means the provider-agnostic default applies; see MINIMUM_PROMPT_CACHE_TOKEN_COUNT.
     prompt_cache_min_tokens: int | None
@@ -3945,12 +3946,15 @@ def strip_custom_pricing_fields(cls, model_info: dict[str, Any]) -> dict[str, An
 
 DEPLOYMENT_SCOPED_PRICING_FIELDS: Final[frozenset[str]] = frozenset({"off_peak_pricing"})
 
+DEPLOYMENT_SCOPED_SEPARATE_CHARGE_FIELDS: Final[frozenset[str]] = frozenset({"cache_storage_cost_per_token_per_hour"})
+
 DEPLOYMENT_SCOPED_CAPABILITY_FIELDS: Final[frozenset[str]] = frozenset({"supports_regex_lookaround"})
 
 SHARED_BACKEND_MODEL_INFO_FIELDS: Final[frozenset[str]] = (
     frozenset(ModelInfoBase.__required_keys__ | ModelInfoBase.__optional_keys__)
     - frozenset(CustomPricingLiteLLMParams.model_fields)
     - DEPLOYMENT_SCOPED_PRICING_FIELDS
+    - DEPLOYMENT_SCOPED_SEPARATE_CHARGE_FIELDS
     - DEPLOYMENT_SCOPED_CAPABILITY_FIELDS
 )
 
diff --git a/litellm/utils.py b/litellm/utils.py
index 89544cf31e2a..8a22f29f72aa 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -6238,6 +6238,7 @@ def get_model_info_helper(
                 cache_creation_input_token_cost_above_1hr=_model_info.get(
                     "cache_creation_input_token_cost_above_1hr", None
                 ),
+                cache_storage_cost_per_token_per_hour=_model_info.get("cache_storage_cost_per_token_per_hour"),
                 off_peak_pricing=_model_info.get("off_peak_pricing", None),
                 input_cost_per_character=_model_info.get("input_cost_per_character", None),
                 input_cost_per_token_above_128k_tokens=_model_info.get("input_cost_per_token_above_128k_tokens", None),
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index f34606ce1272..ecdee276b4d6 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -1,5 +1,6 @@
 {
     "sample_spec": {
+        "cache_storage_cost_per_token_per_hour": 0.0,
         "code_interpreter_cost_per_session": 0.0,
         "computer_use_input_cost_per_1k_tokens": 0.0,
         "computer_use_output_cost_per_1k_tokens": 0.0,
@@ -27091,6 +27092,7 @@
         "cache_read_input_token_cost": 3e-08,
         "cache_read_input_token_cost_flex": 3e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27442,6 +27444,7 @@
     },
     "gemini-3.1-flash-lite-preview": {
         "cache_read_input_token_cost": 2.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27496,6 +27499,7 @@
         "cache_read_input_token_cost_batches": 1.25e-08,
         "cache_read_input_token_cost_flex": 1.25e-08,
         "cache_read_input_token_cost_priority": 4.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "input_cost_per_token_batches": 1.25e-07,
@@ -27557,6 +27561,7 @@
         "cache_read_input_token_cost_batches": 1.5e-08,
         "cache_read_input_token_cost_flex": 1.5e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 3e-07,
         "input_cost_per_token_batches": 1.5e-07,
         "input_cost_per_token_flex": 1.5e-07,
@@ -27651,6 +27656,7 @@
         "cache_read_input_token_cost": 1e-08,
         "cache_read_input_token_cost_flex": 1e-08,
         "cache_read_input_token_cost_priority": 1.8e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 3e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -27939,6 +27945,7 @@
         "cache_read_input_token_cost_above_200k_tokens_priority": 4.5e-07,
         "cache_read_input_token_cost_flex": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.25e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "input_cost_per_token": 1.25e-06,
         "input_cost_per_token_above_200k_tokens": 2.5e-06,
         "litellm_provider": "vertex_ai-language-models",
@@ -28041,6 +28048,7 @@
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
         "cache_read_input_token_cost_flex": 2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28152,6 +28160,7 @@
         "output_cost_per_token_above_200k_tokens_priority": 3.24e-05,
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28202,6 +28211,7 @@
         "input_cost_per_audio_token_priority": 1.8e-06,
         "output_cost_per_token_priority": 5.4e-06,
         "cache_read_input_token_cost_priority": 9e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28274,7 +28284,8 @@
         "output_cost_per_token_batches": 4.5e-06,
         "input_cost_per_token_flex": 7.5e-07,
         "output_cost_per_token_flex": 4.5e-06,
-        "cache_read_input_token_cost_flex": 7.5e-08
+        "cache_read_input_token_cost_flex": 7.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06
     },
     "vertex_ai/gemini-3.6-flash": {
         "prompt_cache_min_tokens": 4096,
@@ -28326,6 +28337,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2026-11-19",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -28386,6 +28398,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2027-01-28",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -28446,6 +28459,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -28459,6 +28473,7 @@
         "cache_read_input_token_cost_batches": 7.5e-08,
         "cache_read_input_token_cost_flex": 7.5e-08,
         "cache_read_input_token_cost_priority": 2.7e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 1.5e-06,
         "input_cost_per_token_batches": 7.5e-07,
         "input_cost_per_token_flex": 7.5e-07,
@@ -28592,6 +28607,7 @@
         "cache_read_input_token_cost_priority": 3.6e-07,
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
         "cache_read_input_token_cost_flex": 2e-07,
+        "cache_storage_cost_per_token_per_hour": 4.5e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30449,6 +30465,7 @@
         "input_cost_per_audio_token_priority": 1.8e-06,
         "output_cost_per_token_priority": 5.4e-06,
         "cache_read_input_token_cost_priority": 9e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30552,7 +30569,8 @@
         "output_cost_per_token_batches": 4.5e-06,
         "input_cost_per_token_flex": 7.5e-07,
         "output_cost_per_token_flex": 4.5e-06,
-        "cache_read_input_token_cost_flex": 7.5e-08
+        "cache_read_input_token_cost_flex": 7.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06
     },
     "gemini-3.6-flash": {
         "prompt_cache_min_tokens": 4096,
@@ -30604,6 +30622,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2026-11-19",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -30664,6 +30683,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "deprecation_date": "2027-01-28",
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
@@ -30724,6 +30744,7 @@
         "input_cost_per_token_priority": 1.35e-06,
         "output_cost_per_token_priority": 6.75e-06,
         "cache_read_input_token_cost_priority": 1.35e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "search_context_cost_per_query": {
             "search_context_size_low": 0.014,
             "search_context_size_medium": 0.014,
@@ -30737,6 +30758,7 @@
         "cache_read_input_token_cost_batches": 7.5e-08,
         "cache_read_input_token_cost_flex": 7.5e-08,
         "cache_read_input_token_cost_priority": 2.7e-07,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 1.5e-06,
         "input_cost_per_token_batches": 7.5e-07,
         "input_cost_per_token_flex": 7.5e-07,
@@ -50905,6 +50927,7 @@
     },
     "vertex_ai/gemini-3.1-flash-lite-preview": {
         "cache_read_input_token_cost": 2.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -50959,6 +50982,7 @@
         "cache_read_input_token_cost_batches": 1.25e-08,
         "cache_read_input_token_cost_flex": 1.25e-08,
         "cache_read_input_token_cost_priority": 4.5e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "input_cost_per_token_batches": 1.25e-07,
@@ -51021,6 +51045,7 @@
         "cache_read_input_token_cost_batches": 1.5e-08,
         "cache_read_input_token_cost_flex": 1.5e-08,
         "cache_read_input_token_cost_priority": 5.4e-08,
+        "cache_storage_cost_per_token_per_hour": 1e-06,
         "input_cost_per_token": 3e-07,
         "input_cost_per_token_batches": 1.5e-07,
         "input_cost_per_token_flex": 1.5e-07,
diff --git a/model_prices_and_context_window.schema.json b/model_prices_and_context_window.schema.json
index e51ae0453ea5..8e942d2af98e 100644
--- a/model_prices_and_context_window.schema.json
+++ b/model_prices_and_context_window.schema.json
@@ -281,6 +281,10 @@
           "type": "number",
           "minimum": 0
         },
+        "cache_storage_cost_per_token_per_hour": {
+          "type": "number",
+          "minimum": 0
+        },
         "citation_cost_per_token": {
           "type": "number",
           "minimum": 0
diff --git a/tests/unit/llms/vertex_ai/context_caching/test_storage_cost.py b/tests/unit/llms/vertex_ai/context_caching/test_storage_cost.py
new file mode 100644
index 000000000000..5fd3bbad9cc3
--- /dev/null
+++ b/tests/unit/llms/vertex_ai/context_caching/test_storage_cost.py
@@ -0,0 +1,51 @@
+from typing import Final
+
+import pytest
+
+from litellm.llms.vertex_ai.context_caching.storage_cost import (
+    context_cache_storage_token_hours,
+)
+
+
+@pytest.mark.parametrize(
+    "created, requested_ttl, expected",
+    [
+        (
+            {
+                "usageMetadata": {"totalTokenCount": 5000},
+                "createTime": "2026-01-01T00:00:00.123456789Z",
+                "expireTime": "2026-01-01T02:00:00.123456789Z",
+            },
+            "60s",
+            10000.0,
+        ),
+        ({"usageMetadata": {"totalTokenCount": 5000}}, "1800s", 2500.0),
+        ({"usageMetadata": {"totalTokenCount": 5000}}, "90.5s", 5000 * 90.5 / 3600),
+        (
+            {"usageMetadata": {"totalTokenCount": 4000}, "expireTime": "2026-01-01T02:00:00Z"},
+            "900s",
+            1000.0,
+        ),
+        ({"name": "cachedContents/1"}, "3600s", 0.0),
+        ({"usageMetadata": {"totalTokenCount": "many"}}, "3600s", 0.0),
+        ({"usageMetadata": {"totalTokenCount": 5000}, "expireTime": "not-a-time"}, "3600s", 0.0),
+        ("not-an-object", "3600s", 0.0),
+    ],
+)
+def test_token_hours_follow_reported_lifetime_then_requested_ttl(
+    created: object, requested_ttl: str | None, expected: float
+) -> None:
+    assert context_cache_storage_token_hours(created, requested_ttl) == pytest.approx(expected)
+
+
+def test_token_hours_default_to_one_hour_without_lifetime_or_ttl() -> None:
+    # Source: https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-create
+    # "The default expiration time of a context cache is 60 minutes after it's created" (read 2026-10-07)
+    assert context_cache_storage_token_hours({"usageMetadata": {"totalTokenCount": 5000}}, None) == 5000.0
+
+
+@pytest.mark.parametrize("requested_ttl", ["", "s", "abc", "10m", "-5s"])
+def test_unparseable_requested_ttl_falls_back_to_default_lifetime(requested_ttl: str) -> None:
+    created: Final = {"usageMetadata": {"totalTokenCount": 3600}}
+
+    assert context_cache_storage_token_hours(created, requested_ttl) == context_cache_storage_token_hours(created, None)
diff --git a/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py b/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
index 67d78d6030e8..bf9f73aa08e2 100644
--- a/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
+++ b/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
@@ -1,8 +1,10 @@
-from typing import Final
+from collections.abc import Mapping
+from typing import Final, Literal
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import httpx
 import pytest
+from typing_extensions import ReadOnly, TypedDict
 
 import litellm
 
@@ -12,6 +14,7 @@
 from litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching import (
     ContextCachingEndpoints,
 )
+from litellm.types.llms.openai import AllMessageValues
 
 
 @pytest.fixture
@@ -2289,3 +2292,144 @@ def test_vertex_ai_global_still_uses_global_host(self):
 
         assert url.startswith("https://aiplatform.googleapis.com/")
         assert "/locations/global/cachedContents" in url
+
+
+_STORAGE_TEST_MODEL: Final = "gemini-2.5-pro"
+
+
+_STORAGE_TEST_CACHE_NAME: Final = "cachedContents/storage-cost"
+
+
+def _storage_logging_obj() -> Logging:
+    logging_obj: Final = Logging(
+        model=_STORAGE_TEST_MODEL,
+        messages=[],
+        stream=False,
+        call_type="acompletion",
+        start_time=None,
+        litellm_call_id="context-cache-storage",
+        function_id="context-cache-storage",
+    )
+    logging_obj.update_environment_variables(
+        model=_STORAGE_TEST_MODEL, litellm_params={}, optional_params={}, custom_llm_provider="gemini"
+    )
+    return logging_obj
+
+
+def _storage_messages() -> list[AllMessageValues]:
+    return [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "policy " * 6000, "cache_control": {"type": "ephemeral"}}],
+        },
+        {"role": "user", "content": "Hello"},
+    ]
+
+
+def _json_response(method: str, payload: Mapping[str, object]) -> httpx.Response:
+    return httpx.Response(200, json=payload, request=httpx.Request(method, "https://example.invalid"))
+
+
+_CREATED_CACHE: Final = {
+    "name": _STORAGE_TEST_CACHE_NAME,
+    "model": f"models/{_STORAGE_TEST_MODEL}",
+    "usageMetadata": {"totalTokenCount": 7200},
+    "createTime": "2026-01-01T00:00:00Z",
+    "expireTime": "2026-01-01T00:30:00Z",
+}
+
+
+class _CreateCacheKwargs(TypedDict):
+    messages: ReadOnly[list[AllMessageValues]]
+    optional_params: ReadOnly[dict[str, object]]
+    api_key: ReadOnly[str]
+    api_base: ReadOnly[str | None]
+    model: ReadOnly[str]
+    client: ReadOnly[MagicMock]
+    timeout: ReadOnly[float]
+    logging_obj: ReadOnly[Logging]
+    custom_llm_provider: ReadOnly[Literal["gemini"]]
+    vertex_project: ReadOnly[str | None]
+    vertex_location: ReadOnly[str | None]
+    vertex_auth_header: ReadOnly[str | None]
+
+
+def _create_kwargs(logging_obj: Logging, client: MagicMock) -> _CreateCacheKwargs:
+    return {
+        "messages": _storage_messages(),
+        "optional_params": {},
+        "api_key": "test_key",
+        "api_base": None,
+        "model": _STORAGE_TEST_MODEL,
+        "client": client,
+        "timeout": 30.0,
+        "logging_obj": logging_obj,
+        "custom_llm_provider": "gemini",
+        "vertex_project": None,
+        "vertex_location": None,
+        "vertex_auth_header": None,
+    }
+
+
+def test_creating_a_cache_records_its_storage_token_hours(local_model_cost_map: None) -> None:
+    logging_obj: Final = _storage_logging_obj()
+    client: Final = MagicMock(spec=HTTPHandler)
+    client.get.return_value = _json_response("GET", {})
+    client.post.return_value = _json_response("POST", _CREATED_CACHE)
+
+    _, _, cache_name = ContextCachingEndpoints().check_and_create_cache(**_create_kwargs(logging_obj, client))
+
+    assert cache_name == _STORAGE_TEST_CACHE_NAME
+    assert logging_obj.context_cache_storage_token_hours == pytest.approx(7200 * 0.5)
+
+
+@pytest.mark.asyncio
+async def test_async_creating_a_cache_records_its_storage_token_hours(local_model_cost_map: None) -> None:
+    logging_obj: Final = _storage_logging_obj()
+    client: Final = MagicMock(spec=AsyncHTTPHandler)
+    client.get = AsyncMock(return_value=_json_response("GET", {}))
+    client.post = AsyncMock(return_value=_json_response("POST", _CREATED_CACHE))
+
+    _, _, cache_name = await ContextCachingEndpoints().async_check_and_create_cache(
+        **_create_kwargs(logging_obj, client)
+    )
+
+    assert cache_name == _STORAGE_TEST_CACHE_NAME
+    assert logging_obj.context_cache_storage_token_hours == pytest.approx(7200 * 0.5)
+
+
+def _listing_with(created_body: Mapping[str, object]) -> Mapping[str, object]:
+    return {"cachedContents": [{"name": _STORAGE_TEST_CACHE_NAME, "displayName": created_body["displayName"]}]}
+
+
+def test_reusing_an_existing_cache_records_no_storage(local_model_cost_map: None) -> None:
+    client: Final = MagicMock(spec=HTTPHandler)
+    client.get.return_value = _json_response("GET", {})
+    client.post.return_value = _json_response("POST", _CREATED_CACHE)
+    ContextCachingEndpoints().check_and_create_cache(**_create_kwargs(_storage_logging_obj(), client))
+    client.get.return_value = _json_response("GET", _listing_with(client.post.call_args.kwargs["json"]))
+    reusing_logging_obj: Final = _storage_logging_obj()
+
+    _, _, cache_name = ContextCachingEndpoints().check_and_create_cache(**_create_kwargs(reusing_logging_obj, client))
+
+    assert cache_name == _STORAGE_TEST_CACHE_NAME
+    assert client.post.call_count == 1
+    assert reusing_logging_obj.context_cache_storage_token_hours == 0.0
+
+
+@pytest.mark.asyncio
+async def test_async_reusing_an_existing_cache_records_no_storage(local_model_cost_map: None) -> None:
+    client: Final = MagicMock(spec=AsyncHTTPHandler)
+    client.get = AsyncMock(return_value=_json_response("GET", {}))
+    client.post = AsyncMock(return_value=_json_response("POST", _CREATED_CACHE))
+    await ContextCachingEndpoints().async_check_and_create_cache(**_create_kwargs(_storage_logging_obj(), client))
+    client.get.return_value = _json_response("GET", _listing_with(client.post.call_args.kwargs["json"]))
+    reusing_logging_obj: Final = _storage_logging_obj()
+
+    _, _, cache_name = await ContextCachingEndpoints().async_check_and_create_cache(
+        **_create_kwargs(reusing_logging_obj, client)
+    )
+
+    assert cache_name == _STORAGE_TEST_CACHE_NAME
+    assert client.post.call_count == 1
+    assert reusing_logging_obj.context_cache_storage_token_hours == 0.0
diff --git a/tests/unit/test_cost_calculator.py b/tests/unit/test_cost_calculator.py
index 7d01b1b2906b..2851ec8f766c 100644
--- a/tests/unit/test_cost_calculator.py
+++ b/tests/unit/test_cost_calculator.py
@@ -1,6 +1,8 @@
 import datetime
+import math
 import time
-from collections.abc import Mapping
+from collections.abc import Iterator, Mapping
+from pathlib import Path
 from types import MappingProxyType, SimpleNamespace
 from typing import Final, Literal, cast
 
@@ -5817,6 +5819,211 @@ def test_completion_cost_bills_base_when_gemini_serves_on_demand(
     assert cost == pytest.approx(100 * 0.001 + 50 * 0.002)
 
 
+_STORAGE_RATE_KEY: Final = "cache_storage_cost_per_token_per_hour"
+
+
+_FIXED_START_TIME: Final = datetime.datetime(2026, 1, 1, tzinfo=datetime.timezone.utc)
+
+
+@pytest.fixture
+def _storage_cost_map(_local_model_cost_map: None) -> Iterator[None]:
+    litellm.get_model_info.cache_clear()
+    yield
+    litellm.get_model_info.cache_clear()
+
+
+def _first_chat_model(provider_prefix: str, *, with_storage_rate: bool) -> str:
+    names: Final = [name for name in litellm.model_cost if isinstance(name, str)]
+    return next(
+        name
+        for name in names
+        if _is_priced_chat_entry(_cost_map_entry(name), provider_prefix, with_storage_rate=with_storage_rate)
+    )
+
+
+def _cost_map_entry(name: str) -> object:
+    entry: Final[object] = litellm.model_cost.get(name)
+    return entry
+
+
+def _is_priced_chat_entry(entry: object, provider_prefix: str, *, with_storage_rate: bool) -> bool:
+    return (
+        isinstance(entry, dict)
+        and entry.get("mode") == "chat"
+        and isinstance(provider := entry.get("litellm_provider"), str)
+        and provider.startswith(provider_prefix)
+        and entry.get("input_cost_per_token") is not None
+        and (_STORAGE_RATE_KEY in entry) == with_storage_rate
+    )
+
+
+def _cache_storage_logging_obj(model: str, token_hours: float) -> Logging:
+    logging_obj: Final = Logging(
+        model=model,
+        messages=[{"role": "user", "content": "Hello"}],
+        stream=False,
+        call_type="completion",
+        start_time=_FIXED_START_TIME,
+        litellm_call_id="context-cache-storage",
+        function_id="f",
+    )
+    if token_hours:
+        logging_obj.record_context_cache_storage(token_hours)
+    return logging_obj
+
+
+def _chat_cost(model: str, logging_obj: Logging, **kwargs: object) -> float:
+    usage: Final = Usage(prompt_tokens=1_000, completion_tokens=100, total_tokens=1_100)
+    return completion_cost(
+        completion_response=ModelResponse(model=model, usage=usage),
+        model=model,
+        litellm_logging_obj=logging_obj,
+        **kwargs,
+    )
+
+
+def test_context_cache_storage_is_billed_on_the_creating_request(_storage_cost_map: None) -> None:
+    model: Final = _first_chat_model("vertex_ai", with_storage_rate=True)
+    rate: Final = litellm.get_model_info(model)[_STORAGE_RATE_KEY]
+    creating_request: Final = _cache_storage_logging_obj(model, token_hours=3_600.0)
+
+    with_storage: Final = _chat_cost(model, creating_request)
+    without_storage: Final = _chat_cost(model, _cache_storage_logging_obj(model, token_hours=0.0))
+
+    assert rate is not None and rate > 0
+    assert with_storage - without_storage == pytest.approx(3_600.0 * rate)
+    assert creating_request.cost_breakdown["additional_costs"] == pytest.approx({"cache_storage_cost": 3_600.0 * rate})
+    assert creating_request.cost_breakdown["total_cost"] == pytest.approx(with_storage)
+
+
+def test_context_cache_storage_costs_nothing_for_models_without_a_storage_rate(_storage_cost_map: None) -> None:
+    model: Final = _first_chat_model("gemini", with_storage_rate=False)
+    creating_request: Final = _cache_storage_logging_obj(model, token_hours=3_600.0)
+
+    with_storage: Final = _chat_cost(model, creating_request)
+
+    assert with_storage == _chat_cost(model, _cache_storage_logging_obj(model, token_hours=0.0))
+    assert creating_request.cost_breakdown.get("additional_costs") is None
+
+
+def test_context_cache_storage_uses_the_deployment_storage_rate(_storage_cost_map: None) -> None:
+    model: Final = _first_chat_model("vertex_ai", with_storage_rate=True)
+    deployment_rate: Final = litellm.get_model_info(model)[_STORAGE_RATE_KEY] * 3
+    litellm.register_model(
+        {
+            "storage-cost-deployment": {
+                "litellm_provider": "vertex_ai",
+                "mode": "chat",
+                "input_cost_per_token": 1e-6,
+                "output_cost_per_token": 2e-6,
+                _STORAGE_RATE_KEY: deployment_rate,
+            }
+        }
+    )
+    pricing: Final = {"custom_pricing": True, "router_model_id": "storage-cost-deployment"}
+
+    with_storage: Final = _chat_cost(model, _cache_storage_logging_obj(model, token_hours=500.0), **pricing)
+    without_storage: Final = _chat_cost(model, _cache_storage_logging_obj(model, token_hours=0.0), **pricing)
+
+    assert with_storage - without_storage == pytest.approx(500.0 * deployment_rate)
+
+
+def test_context_cache_storage_joins_the_provider_additional_costs(_storage_cost_map: None) -> None:
+    litellm.register_model(
+        {"router-storage-deployment": {"litellm_provider": "azure_ai", "mode": "chat", _STORAGE_RATE_KEY: 1e-6}}
+    )
+    logging_obj: Final = _cache_storage_logging_obj("azure_ai/model-router", token_hours=500.0)
+    usage: Final = Usage(prompt_tokens=1_000_000, completion_tokens=100, total_tokens=1_000_100)
+
+    cost: Final = completion_cost(
+        completion_response=ModelResponse(model="azure_ai/gpt-4o-2024-08-06", usage=usage),
+        model="azure_ai/model-router",
+        custom_llm_provider="azure_ai",
+        litellm_logging_obj=logging_obj,
+        custom_pricing=True,
+        router_model_id="router-storage-deployment",
+    )
+
+    breakdown: Final = logging_obj.cost_breakdown
+    assert breakdown is not None
+    additional_costs: Final = breakdown.get("additional_costs") or {}
+    assert additional_costs["Azure Model Router Flat Cost"] > 0
+    assert math.isclose(additional_costs["cache_storage_cost"], 500.0 * 1e-6)
+    assert math.isclose(cost, sum(additional_costs.values()))
+
+
+_STORAGE_DEPLOYMENT_MODEL: Final = "gemini-2.5-flash"
+
+
+def _routed_request_cost(
+    token_prices: Mapping[str, float], token_hours: float, *, storage_rate: float | None = None
+) -> float | None:
+    from litellm import Router
+
+    router: Final = Router(
+        model_list=[
+            {
+                "model_name": "storage-cost-deployment",
+                "litellm_params": {"model": f"vertex_ai/{_STORAGE_DEPLOYMENT_MODEL}", **token_prices},
+                "model_info": {} if storage_rate is None else {_STORAGE_RATE_KEY: storage_rate},
+            }
+        ]
+    )
+    deployment_id: Final = router.model_list[0]["model_info"]["id"]
+    assert isinstance(deployment_id, str)
+    return _vertex_request_cost({**token_prices, "metadata": {"model_info": {"id": deployment_id}}}, token_hours)
+
+
+def _vertex_request_cost(litellm_params: Mapping[str, object], token_hours: float) -> float | None:
+    logging_obj: Final = _cache_storage_logging_obj(_STORAGE_DEPLOYMENT_MODEL, token_hours)
+    logging_obj.update_environment_variables(
+        model=_STORAGE_DEPLOYMENT_MODEL,
+        litellm_params=dict(litellm_params),
+        optional_params={},
+        custom_llm_provider="vertex_ai",
+    )
+    usage: Final = Usage(prompt_tokens=1_000, completion_tokens=100, total_tokens=1_100)
+    return logging_obj.response_cost_calculator(ModelResponse(model=_STORAGE_DEPLOYMENT_MODEL, usage=usage))
+
+
+def test_custom_token_prices_without_a_storage_rate_bill_storage_at_the_underlying_model_rate(
+    _storage_cost_map: None,
+) -> None:
+    cost_map_rate: Final = litellm.get_model_info(f"vertex_ai/{_STORAGE_DEPLOYMENT_MODEL}")[_STORAGE_RATE_KEY]
+    token_prices: Final = {"input_cost_per_token": 1e-6, "output_cost_per_token": 2e-6}
+
+    with_storage: Final = _routed_request_cost(token_prices, token_hours=500.0)
+    without_storage: Final = _routed_request_cost(token_prices, token_hours=0.0)
+
+    assert cost_map_rate is not None and cost_map_rate > 0
+    assert without_storage == pytest.approx(1_000 * 1e-6 + 100 * 2e-6)
+    assert with_storage == pytest.approx(without_storage + 500.0 * cost_map_rate)
+
+
+def test_a_storage_only_override_keeps_the_underlying_token_prices(_storage_cost_map: None) -> None:
+    cost_map_rate: Final = litellm.get_model_info(f"vertex_ai/{_STORAGE_DEPLOYMENT_MODEL}")[_STORAGE_RATE_KEY]
+    assert isinstance(cost_map_rate, float)
+    deployment_rate: Final = cost_map_rate * 3
+
+    no_override: Final = _vertex_request_cost({}, token_hours=0.0)
+    without_storage: Final = _routed_request_cost({}, token_hours=0.0, storage_rate=deployment_rate)
+    with_storage: Final = _routed_request_cost({}, token_hours=500.0, storage_rate=deployment_rate)
+
+    assert no_override is not None and no_override > 0
+    assert without_storage == pytest.approx(no_override)
+    assert with_storage == pytest.approx(no_override + 500.0 * deployment_rate)
+
+
+def test_a_deployment_storage_rate_stays_off_the_shared_backend_key(_storage_cost_map: None) -> None:
+    backend_key: Final = f"vertex_ai/{_STORAGE_DEPLOYMENT_MODEL}"
+    cost_map_rate: Final = litellm.get_model_info(backend_key)[_STORAGE_RATE_KEY]
+    assert isinstance(cost_map_rate, float)
+
+    _routed_request_cost({}, token_hours=0.0, storage_rate=cost_map_rate * 3)
+
+    assert litellm.get_model_info(backend_key)[_STORAGE_RATE_KEY] == cost_map_rate
+
+
 def test_custom_pricing_as_completion_cost_param():
     from litellm import Choices, Message, ModelResponse
     from litellm.utils import Usage
diff --git a/tests/unit/test_router_model_cost_isolation.py b/tests/unit/test_router_model_cost_isolation.py
index 4aed84d05c8c..d9404b3922e4 100644
--- a/tests/unit/test_router_model_cost_isolation.py
+++ b/tests/unit/test_router_model_cost_isolation.py
@@ -1471,13 +1471,17 @@ def test_custom_pricing_field_denylist_covers_all_builtin_pricing_fields():
     """
     import typing
 
-    from litellm.types.utils import CustomPricingLiteLLMParams, ModelInfoBase
+    from litellm.types.utils import (
+        DEPLOYMENT_SCOPED_SEPARATE_CHARGE_FIELDS,
+        CustomPricingLiteLLMParams,
+        ModelInfoBase,
+    )
 
     pricing_markers = ("cost", "price", "uplift", "vector_size", "tiered_pricing")
     builtin_pricing_fields = {
         name for name in typing.get_type_hints(ModelInfoBase) if any(marker in name for marker in pricing_markers)
     }
-    denylisted_fields = set(CustomPricingLiteLLMParams.model_fields.keys())
+    denylisted_fields = set(CustomPricingLiteLLMParams.model_fields.keys()) | DEPLOYMENT_SCOPED_SEPARATE_CHARGE_FIELDS
 
     uncovered = sorted(builtin_pricing_fields - denylisted_fields)
     assert not uncovered, (
diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py
index 7551a3109778..375c34f0e844 100644
--- a/tests/unit/test_utils.py
+++ b/tests/unit/test_utils.py
@@ -808,6 +808,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "cache_creation_input_token_cost_flex": {"type": "number"},
                 "cache_creation_input_token_cost_priority": {"type": "number"},
                 "cache_creation_input_token_cost_ultrafast": {"type": "number"},
+                "cache_storage_cost_per_token_per_hour": {"type": "number"},
                 "cache_read_input_token_cost": {"type": "number"},
                 "cache_read_input_token_cost_above_32k_tokens": {"type": "number"},
                 "cache_read_input_token_cost_above_100k_tokens": {"type": "number"},
@@ -7214,6 +7215,26 @@ async def yielding_truncate(messages):
     assert counting_logger.logged_results[0] is inner_result
 
 
+def test_get_model_info_surfaces_context_cache_storage_rate(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    local_cost_map: Final[Mapping[str, object]] = litellm.get_model_cost_map(url="")
+    monkeypatch.setattr(litellm, "model_cost", local_cost_map)
+    litellm.get_model_info.cache_clear()
+    priced: Final = {
+        name: entry["cache_storage_cost_per_token_per_hour"]
+        for name, entry in litellm.model_cost.items()
+        if isinstance(entry, dict) and "cache_storage_cost_per_token_per_hour" in entry
+    }
+
+    surfaced: Final = {
+        name: litellm.get_model_info(name).get("cache_storage_cost_per_token_per_hour") for name in priced
+    }
+    litellm.get_model_info.cache_clear()
+
+    assert priced
+    assert surfaced == priced
+
+
 def test_basic_trimming():
     litellm.turn_on_debug()
     messages = [
diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts
index 1108d4a8743d..d91c0353323c 100644
--- a/ui/litellm-dashboard/src/lib/http/schema.d.ts
+++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts
@@ -50823,6 +50823,8 @@ export interface components {
             cache_creation_input_token_cost?: number | null;
             /** Cache Read Input Token Cost */
             cache_read_input_token_cost?: number | null;
+            /** Cache Storage Cost Per Token Per Hour */
+            cache_storage_cost_per_token_per_hour?: number | null;
             /** Cost Per Ptu Per Hour */
             cost_per_ptu_per_hour?: number | null;
             /** Created At */