fix(datadog): set top-level model_name on embedding LLMObs spans

Datadog prices llm/embedding spans from meta.model_name and
meta.model_provider. LiteLLM only put those under meta.metadata, so
embedding spans were flagged Partial cost. Lift the existing values onto
Meta for llm and embedding kinds.

Part of #41601

litellm/integrations/datadog/datadog_llm_obs.py

@@ -654,6 +654,12 @@ class DataDogLLMObsLogger(CustomBatchLogger):
             "error": error_info,
             **({"tool_definitions": tool_definitions} if tool_definitions else {}),
         }
+        # Datadog prices llm/embedding spans from top-level meta.model_name /
+        # model_provider. Those values already live in metadata; lift them so
+        # embedding spans are not flagged "Partial cost — Unsupported model provider".
+        if span_kind in ("llm", "embedding"):
+            meta["model_name"] = standard_logging_payload.get("model", "unknown")
+            meta["model_provider"] = standard_logging_payload.get("custom_llm_provider", "unknown")
 
         metrics: Final = self._assemble_metrics(standard_logging_payload, tool_output_tokens)
 

litellm/types/integrations/datadog_llm_obs.py

@@ -69,6 +69,10 @@ class DDLLMObsError(TypedDict, total=False):
 class Meta(TypedDict, total=False):
     # The span kind: "agent", "workflow", "llm", "tool", "task", "embedding", or "retrieval".
     kind: Literal["llm", "tool", "task", "embedding", "retrieval"]
+    # Top-level model fields required for Datadog cost attribution on llm/embedding spans.
+    # Values are also kept under metadata for backwards compatibility.
+    model_name: str
+    model_provider: str
     input: InputMeta  # The span's input information.
     output: OutputMeta  # The span's output information.
     metadata: dict[str, Any]

tests/test_litellm/integrations/datadog/test_datadog_llm_obs.py

@@ -1122,3 +1122,37 @@ def test_reasoning_content_survives_the_mapping(logger: DataDogLLMObsLogger) ->
     )
 
     assert payload["meta"]["output"]["messages"][0]["reasoning_content"] == "thinking"
+
+
+def test_embedding_span_sets_top_level_model_name(logger: DataDogLLMObsLogger) -> None:
+    """Datadog prices embedding spans from meta.model_name, not meta.metadata (issue #41601)."""
+    kwargs = build_payload(messages=None)
+    kwargs["standard_logging_object"]["call_type"] = "aembedding"
+    kwargs["standard_logging_object"]["model"] = "openai/text-embedding-3-small"
+    kwargs["standard_logging_object"]["custom_llm_provider"] = "openai"
+    kwargs["standard_logging_object"]["response"] = {"data": [{"embedding": [0.1, 0.2]}]}
+    kwargs["litellm_params"]["metadata"]["parent_id"] = "parent-span-1"
+
+    start = datetime(2026, 9, 1, 12, 0, 0)
+    span = json.loads(safe_dumps(logger.create_llm_obs_payload(kwargs, start, start + timedelta(seconds=1))))
+
+    assert span["meta"]["kind"] == "embedding"
+    assert span["meta"]["model_name"] == "openai/text-embedding-3-small"
+    assert span["meta"]["model_provider"] == "openai"
+    # Still present under metadata for backwards compatibility.
+    assert span["meta"]["metadata"]["model_name"] == "openai/text-embedding-3-small"
+    assert span["meta"]["metadata"]["model_provider"] == "openai"
+
+
+def test_llm_span_sets_top_level_model_name(logger: DataDogLLMObsLogger) -> None:
+    """llm spans must keep top-level model_name/model_provider for Datadog cost attribution."""
+    kwargs = build_payload()
+    kwargs["standard_logging_object"]["model"] = "openai/gpt-4.1-mini"
+    kwargs["standard_logging_object"]["custom_llm_provider"] = "openai"
+
+    start = datetime(2026, 9, 1, 12, 0, 0)
+    span = json.loads(safe_dumps(logger.create_llm_obs_payload(kwargs, start, start + timedelta(seconds=1))))
+
+    assert span["meta"]["kind"] == "llm"
+    assert span["meta"]["model_name"] == "openai/gpt-4.1-mini"
+    assert span["meta"]["model_provider"] == "openai"