diff --git a/rust/lance/src/io/exec/fts.rs b/rust/lance/src/io/exec/fts.rs
index 76db9300d0f..009acfffc96 100644
--- a/rust/lance/src/io/exec/fts.rs
+++ b/rust/lance/src/io/exec/fts.rs
@@ -94,6 +94,7 @@ async fn open_fts_segments(
     .await
 }
 
+#[allow(clippy::too_many_arguments)]
 async fn search_segments(
     indices: &[Arc<InvertedIndex>],
     tokens: Arc<Tokens>,
@@ -102,6 +103,7 @@ async fn search_segments(
     pre_filter: Arc<dyn PreFilter>,
     metrics: Arc<FtsIndexMetrics>,
     base_scorer: Arc<MemBM25Scorer>,
+    parallelism: usize,
 ) -> Result<(Vec<u64>, Vec<f32>)> {
     let limit = params.limit.unwrap_or(usize::MAX);
     let mut candidates = std::collections::BinaryHeap::new();
@@ -128,7 +130,7 @@ async fn search_segments(
             }
         })
         .collect::<Vec<_>>();
-    let searches = stream::iter(searches).buffer_unordered(get_num_compute_intensive_cpus());
+    let searches = stream::iter(searches).buffer_unordered(parallelism);
     let mut searches = searches;
 
     while let Some((doc_ids, scores)) = searches.try_next().await? {
@@ -432,6 +434,7 @@ impl ExecutionPlan for MatchQueryExec {
         partition: usize,
         context: Arc<datafusion::execution::TaskContext>,
     ) -> DataFusionResult<SendableRecordBatchStream> {
+        let target_partitions = context.session_config().target_partitions();
         let query = self.query.clone();
         let params = self.params.clone();
         let ds = self.dataset.clone();
@@ -439,6 +442,9 @@ impl ExecutionPlan for MatchQueryExec {
         let preset_base_scorer = self.base_scorer.clone();
         let preset_segments = self.preset_segments.clone();
         let metrics = Arc::new(FtsIndexMetrics::new(&self.metrics, partition));
+        let parallelism = get_num_compute_intensive_cpus()
+            .min(target_partitions)
+            .max(1);
         let column = query.column.ok_or(DataFusionError::Execution(format!(
             "column not set for MatchQuery {}",
             query.terms
@@ -515,6 +521,7 @@ impl ExecutionPlan for MatchQueryExec {
                 pre_filter,
                 metrics.clone(),
                 base_scorer,
+                parallelism,
             )
             .await?;
             scores.iter_mut().for_each(|s| {
@@ -1316,6 +1323,10 @@ impl ExecutionPlan for PhraseQueryExec {
         partition: usize,
         context: Arc<datafusion::execution::TaskContext>,
     ) -> DataFusionResult<SendableRecordBatchStream> {
+        let target_partitions = context.session_config().target_partitions();
+        let parallelism = get_num_compute_intensive_cpus()
+            .min(target_partitions)
+            .max(1);
         let query = self.query.clone();
         let params = self.params.clone();
         let ds = self.dataset.clone();
@@ -1385,6 +1396,7 @@ impl ExecutionPlan for PhraseQueryExec {
                 pre_filter,
                 metrics.clone(),
                 base_scorer,
+                parallelism,
             )
             .await?;
             metrics.baseline_metrics.record_output(doc_ids.len());
diff --git a/rust/lance/src/io/exec/knn.rs b/rust/lance/src/io/exec/knn.rs
index 48bd9fb4196..5340da4fbe5 100644
--- a/rust/lance/src/io/exec/knn.rs
+++ b/rust/lance/src/io/exec/knn.rs
@@ -510,6 +510,7 @@ impl ExecutionPlan for KNNVectorDistanceExec {
         partition: usize,
         context: Arc<datafusion::execution::context::TaskContext>,
     ) -> DataFusionResult<SendableRecordBatchStream> {
+        let target_partitions = context.session_config().target_partitions();
         let input_stream = self.input.execute(partition, context)?;
         if self.is_batch {
             let stream = stream::once(Self::execute_batch(
@@ -572,7 +573,11 @@ impl ExecutionPlan for KNNVectorDistanceExec {
                         .map_err(|e| DataFusionError::ArrowError(Box::new(e), None))
                 }
             })
-            .buffer_unordered(get_num_compute_intensive_cpus());
+            .buffer_unordered(
+                get_num_compute_intensive_cpus()
+                    .min(target_partitions)
+                    .max(1),
+            );
 
         let stream = stream.map(move |batch| {
             let poll = baseline.record_poll(std::task::Poll::Ready(Some(batch)));
@@ -2633,6 +2638,64 @@ mod tests {
         assert_eq!(expected, results[0]);
     }
 
+    /// Verify that KNNVectorDistanceExec with target_partitions=1 produces the same
+    /// row count as the default context. Regression guard for the parallelism cap.
+    #[tokio::test]
+    async fn test_knn_vector_distance_respects_target_partitions() {
+        use arrow_array::UInt64Array;
+        use datafusion::execution::context::{SessionConfig, TaskContext};
+
+        let dim: usize = 16;
+        let n_batches = 10;
+        let batch_size = 50;
+        let schema = Arc::new(ArrowSchema::new(vec![
+            ArrowField::new(ROW_ID, DataType::UInt64, false),
+            ArrowField::new(
+                "vector",
+                DataType::FixedSizeList(
+                    Arc::new(ArrowField::new("item", DataType::Float32, true)),
+                    dim as i32,
+                ),
+                true,
+            ),
+        ]));
+
+        let batches: Vec<RecordBatch> = (0..n_batches)
+            .map(|i| {
+                RecordBatch::try_new(
+                    schema.clone(),
+                    vec![
+                        Arc::new(UInt64Array::from_iter_values(
+                            ((i * batch_size) as u64)..((i + 1) as u64 * batch_size as u64),
+                        )),
+                        Arc::new(
+                            FixedSizeListArray::try_new_from_values(
+                                generate_random_array(dim * batch_size),
+                                dim as i32,
+                            )
+                            .unwrap(),
+                        ),
+                    ],
+                )
+                .unwrap()
+            })
+            .collect();
+
+        let input: Arc<dyn ExecutionPlan> = Arc::new(TestingExec::new(batches));
+        let query_vec = Arc::new(generate_random_array(dim)) as ArrayRef;
+        let exec =
+            KNNVectorDistanceExec::try_new(input, "vector", query_vec, DistanceType::L2).unwrap();
+
+        let low_ctx = Arc::new(
+            TaskContext::default()
+                .with_session_config(SessionConfig::default().with_target_partitions(1)),
+        );
+        let stream = exec.execute(0, low_ctx).unwrap();
+        let batches = stream.try_collect::<Vec<_>>().await.unwrap();
+        let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum();
+        assert_eq!(total_rows, n_batches * batch_size);
+    }
+
     #[test]
     fn test_create_knn_flat() {
         let dim: usize = 128;