diff --git a/src/neo4j_graphrag/llm/types.py b/src/neo4j_graphrag/llm/types.py
index d199919ec..84eb116fa 100644
--- a/src/neo4j_graphrag/llm/types.py
+++ b/src/neo4j_graphrag/llm/types.py
@@ -27,11 +27,15 @@ class LLMUsage(BaseModel):
             ``None`` when not reported by the provider.
         total_tokens (Optional[int]): Total tokens consumed by the call.
             ``None`` when not reported by the provider.
+        cached_tokens (Optional[int]): Number of prompt tokens served from the
+            provider's context cache (e.g. Vertex AI implicit caching).
+            ``None`` when not reported by the provider.
     """
 
     request_tokens: Optional[int] = None
     response_tokens: Optional[int] = None
     total_tokens: Optional[int] = None
+    cached_tokens: Optional[int] = None
 
 
 class LLMResponse(BaseModel):
diff --git a/src/neo4j_graphrag/llm/vertexai_llm.py b/src/neo4j_graphrag/llm/vertexai_llm.py
index f9ee804be..313899e7e 100644
--- a/src/neo4j_graphrag/llm/vertexai_llm.py
+++ b/src/neo4j_graphrag/llm/vertexai_llm.py
@@ -593,9 +593,19 @@ def _parse_content_response(self, response: GenerationResponse) -> LLMResponse:
         usage = None
         metadata = response.usage_metadata
         if metadata:
+            cached = metadata.cached_content_token_count or None
             usage = LLMUsage(
                 request_tokens=metadata.prompt_token_count,
                 response_tokens=metadata.candidates_token_count,
                 total_tokens=metadata.total_token_count,
+                cached_tokens=cached,
             )
+            if cached:
+                from openinference.semconv.trace import SpanAttributes
+                from opentelemetry import trace as otel_trace
+
+                otel_trace.get_current_span().set_attribute(
+                    SpanAttributes.LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ,
+                    cached,
+                )
         return LLMResponse(content=response.text, usage=usage)