langfuse · marliessophie · Jul 22, 2026 · Jul 16, 2026 · Jul 16, 2026 · Jul 19, 2026
diff --git a/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx b/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx
@@ -207,7 +207,7 @@ Each experiment run generates traces that are automatically scored by your selec
 </Tab>
 </Tabs>
 
-## Set up step-by-step
+## Set up step-by-step [#set-up-step-by-step]
 
 <Steps>
 
@@ -263,9 +263,6 @@ With your evaluator and model selected, configure which data to run the evaluati
 <Tabs items={["Live Production Data", "Offline Experiment Data"]}>
 <Tab>
 
-<Tabs items={["Observations (Recommended)", "Traces (Legacy)"]}>
-<Tab>
-
 **Configuration Steps**
 
 1. Select "Live Observations" as your evaluation target
@@ -279,37 +276,10 @@ With your evaluator and model selected, configure which data to run the evaluati
   - [JS/TS v3 → v4 migration guide](/docs/observability/sdk/upgrade-path/js-v3-to-v4)
 - **When filtering by trace attributes**: To filter observations by trace-level attributes (`userId`, `sessionId`, `version`, `tags`, `metadata`, `traceName`), use [`propagate_attributes()`](/docs/observability/sdk/instrumentation#add-attributes-to-observations) in your instrumentation code. Without this, trace attributes will not be available on observations. If you do set up trace-level attribute filtering and are not propagating attributes to observations, your observations will not be matched by the evaluator.
 
-</Tab>
-
-<Tab>
-
-<Callout type="info">
-**Performance consideration**: We recommend using Observation-level evaluators for production monitoring. They complete in seconds (vs minutes for trace-level), eliminating evaluation delays and backlogs. They also offer better precision and cost efficiency. See [upgrade guide](/faq/all/llm-as-a-judge-migration).
-</Callout>
-
-**Configuration Steps**
-
-1. Select "Live Traces" as your evaluation target
-2. Filter traces by name, tags, userId, and other trace-level attributes
-3. Choose whether to run on new traces only or include existing traces (backfilling)
-4. Configure sampling percentage (e.g., 5%) to manage evaluation costs and throughput
-5. Preview matched traces from the last 24 hours to validate your filter configuration
-
-<Frame fullWidth>
-  ![Production tracing data](/images/docs/evaluator-trace-filter.png)
-</Frame>
-
-**Requirements**
-
-- **OTel-based SDKs**: If you're using Python v3+ or JS/TS v4+, trace input/output is derived from the root observation by default. To explicitly set trace input/output for these evaluators, use `set_trace_io()` (Python) or `setTraceIO()` (JS/TS). See the [Python v3 → v4](/docs/observability/sdk/upgrade-path/python-v3-to-v4) and [JS/TS v4 → v5](/docs/observability/sdk/upgrade-path/js-v4-to-v5) migration guides.
-
-<Callout type="info">
-We recommend migrating to [observation-level evaluators](/faq/all/llm-as-a-judge-migration) instead of using `set_trace_io()` / `setTraceIO()`. Once migrated, you can remove these calls from your codebase entirely.
+<Callout type="warning">
+**Deprecation of trace-level evaluators:** The legacy trace-level evaluator feature is built on the old trace-centric data model and will be deprecated by the end of October 2026 — see the [Fast Preview rollout timeline](/docs/v4#rollout-timeline). Multi-span evaluations will build on the new observation-centric data model. To move existing trace-level evaluators, follow the [upgrade guide](/faq/all/llm-as-a-judge-migration).
 </Callout>
 
-</Tab>
-</Tabs>
-
 </Tab>
 <Tab>
 

diff --git a/content/docs/v4.mdx b/content/docs/v4.mdx
@@ -54,7 +54,7 @@ As agentic applications grow more complex, a single trace can contain hundreds o
   />
 </Cards>
 
-## Preview access and rollout timeline
+## Preview access and rollout timeline [#rollout-timeline]
 
 | Organization creation date (Langfuse Cloud)    | Fast Preview default | "Preview" toggle visibility      |
 | ---------------------------------------------- | -------------------- | -------------------------------- |
@@ -73,6 +73,8 @@ The toggle remains reversible for organizations created before the cutoff date.
 
 **Export source cutoff (2026-05-20):** Cloud projects created on or after this date are locked to **Enriched observations (recommended)** as the export source for blob storage, PostHog, and Mixpanel integrations. Legacy export sources are hidden from the UI for these projects. Projects created before this date retain full choice across all export sources.
 
+**Trace-level evaluator deprecation (end of October 2026):** Legacy trace-level LLM-as-a-Judge evaluators are built on the old trace-centric data model and will be deprecated by the end of October 2026. Upcoming multi-span evaluations will build on the observation-centric data model. Follow the [evaluator upgrade guide](/faq/all/llm-as-a-judge-migration) to move existing evaluators to the observation level.
+
 <Callout type="info">
 Data from SDKs `langfuse-js` < `5.0.0`, `langfuse-python` < `4.0.0`, or direct OpenTelemetry exporters that do not send `x-langfuse-ingestion-version: 4` can be delayed by up to 10 minutes in the new UI. Upgrade to the latest SDKs or set that header on your OTEL span exporter to see new data in real time.
 </Callout>