docs: make clear that eval via sdks is available in self-hosted

pages/docs/scores/model-based-evals.mdx

@@ -11,7 +11,6 @@ There are two ways to run model-based evaluations in Langfuse:
 1. [Via the Langfuse UI (beta)](#ui)
 2. [Via the Python SDK or API](#sdk)
 
-
 ## Via Langfuse UI (beta) [#ui]
 
 <AvailabilityBanner
@@ -68,7 +67,6 @@ Once the configruation is saved, Langfuse will start running the evals on the tr
   ![Langfuse](/images/docs/evals-log.png)
 </Frame>
 
-
 ### See scores
 
 Upon receiving new traces, navigate to the trace detail view to see the associated scores.
@@ -79,9 +77,17 @@ Upon receiving new traces, navigate to the trace detail view to see the associat
 
 </Steps>
 
-
 ## Via Python SDK [#sdk]
 
+<AvailabilityBanner
+  availability={{
+    hobby: "full",
+    pro: "full",
+    team: "full",
+    selfHosted: "full",
+  }}
+/>
+
 You can run your own model-based evals on data in Langfuse via the Python SDK. This gives you full flexibility to run various eval libraries on your production data and discover which work well for your use case.
 
 Popular libraries:
@@ -90,4 +96,4 @@ Popular libraries:
 - Langchain Evaluators ([Cookbook](/guides/cookbook/evaluation_with_langchain))
 - RAGAS for RAG applications ([Cookbook](/guides/cookbook/evaluation_of_rag_with_ragas))
 - UpTrain evals ([Cookbook](/guides/cookbook/evaluation_with_uptrain))
-- Whylabs Langkit
+- Whylabs Langkit