diff --git a/lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsWriter.java b/lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsWriter.java
index 613fa89b020..93bc6b0011a 100644
--- a/lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsWriter.java
+++ b/lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsWriter.java
@@ -142,7 +142,14 @@ public final class Lucene99ScalarQuantizedVectorsWriter implements Accountable {
     ScalarQuantizer scalarQuantizer = fieldData.createQuantizer();
     byte[] vector = new byte[fieldData.dim];
     final ByteBuffer offsetBuffer = ByteBuffer.allocate(Float.BYTES).order(ByteOrder.LITTLE_ENDIAN);
+    float[] copy = fieldData.normalize ? new float[fieldData.dim] : null;
     for (float[] v : fieldData.floatVectors) {
+      if (fieldData.normalize) {
+        System.arraycopy(v, 0, copy, 0, copy.length);
+        VectorUtil.l2normalize(copy);
+        v = copy;
+      }
+
       float offsetCorrection =
           scalarQuantizer.quantize(v, vector, fieldData.vectorSimilarityFunction);
       quantizedVectorData.writeBytes(vector, vector.length);
@@ -194,8 +201,15 @@ public final class Lucene99ScalarQuantizedVectorsWriter implements Accountable {
     ScalarQuantizer scalarQuantizer = fieldData.createQuantizer();
     byte[] vector = new byte[fieldData.dim];
     final ByteBuffer offsetBuffer = ByteBuffer.allocate(Float.BYTES).order(ByteOrder.LITTLE_ENDIAN);
+    float[] copy = fieldData.normalize ? new float[fieldData.dim] : null;
     for (int ordinal : ordMap) {
       float[] v = fieldData.floatVectors.get(ordinal);
+      if (fieldData.normalize) {
+        System.arraycopy(v, 0, copy, 0, copy.length);
+        VectorUtil.l2normalize(copy);
+        v = copy;
+      }
+
       float offsetCorrection =
           scalarQuantizer.quantize(v, vector, fieldData.vectorSimilarityFunction);
       quantizedVectorData.writeBytes(vector, vector.length);
diff --git a/lucene/core/src/test/org/apache/lucene/codecs/lucene99/TestLucene99HnswQuantizedVectorsFormat.java b/lucene/core/src/test/org/apache/lucene/codecs/lucene99/TestLucene99HnswQuantizedVectorsFormat.java
index 4e262ca4c9d..d3d13d5ef61 100644
--- a/lucene/core/src/test/org/apache/lucene/codecs/lucene99/TestLucene99HnswQuantizedVectorsFormat.java
+++ b/lucene/core/src/test/org/apache/lucene/codecs/lucene99/TestLucene99HnswQuantizedVectorsFormat.java
@@ -38,8 +38,10 @@ import org.apache.lucene.index.VectorSimilarityFunction;
 import org.apache.lucene.store.Directory;
 import org.apache.lucene.tests.index.BaseKnnVectorsFormatTestCase;
 import org.apache.lucene.util.ScalarQuantizer;
+import org.apache.lucene.util.VectorUtil;
 
 public class TestLucene99HnswQuantizedVectorsFormat extends BaseKnnVectorsFormatTestCase {
+
   @Override
   protected Codec getCodec() {
     return new Lucene99Codec() {
@@ -57,6 +59,7 @@ public class TestLucene99HnswQuantizedVectorsFormat extends BaseKnnVectorsFormat
     // create lucene directory with codec
     int numVectors = 1 + random().nextInt(50);
     VectorSimilarityFunction similarityFunction = randomSimilarity();
+    boolean normalize = similarityFunction == VectorSimilarityFunction.COSINE;
     int dim = random().nextInt(64) + 1;
     List<float[]> vectors = new ArrayList<>(numVectors);
     for (int i = 0; i < numVectors; i++) {
@@ -65,13 +68,22 @@ public class TestLucene99HnswQuantizedVectorsFormat extends BaseKnnVectorsFormat
     float quantile = Lucene99ScalarQuantizedVectorsFormat.calculateDefaultQuantile(dim);
     ScalarQuantizer scalarQuantizer =
         ScalarQuantizer.fromVectors(
-            new Lucene99ScalarQuantizedVectorsWriter.FloatVectorWrapper(vectors, false), quantile);
+            new Lucene99ScalarQuantizedVectorsWriter.FloatVectorWrapper(vectors, normalize),
+            quantile);
     float[] expectedCorrections = new float[numVectors];
     byte[][] expectedVectors = new byte[numVectors][];
     for (int i = 0; i < numVectors; i++) {
+      float[] vector = vectors.get(i);
+      if (normalize) {
+        float[] copy = new float[vector.length];
+        System.arraycopy(vector, 0, copy, 0, copy.length);
+        VectorUtil.l2normalize(copy);
+        vector = copy;
+      }
+
       expectedVectors[i] = new byte[dim];
       expectedCorrections[i] =
-          scalarQuantizer.quantize(vectors.get(i), expectedVectors[i], similarityFunction);
+          scalarQuantizer.quantize(vector, expectedVectors[i], similarityFunction);
     }
     float[] randomlyReusedVector = new float[dim];
 
diff --git a/lucene/test-framework/src/java/org/apache/lucene/tests/index/BaseKnnVectorsFormatTestCase.java b/lucene/test-framework/src/java/org/apache/lucene/tests/index/BaseKnnVectorsFormatTestCase.java
index 955c6ae7127..d6883793cfb 100644
--- a/lucene/test-framework/src/java/org/apache/lucene/tests/index/BaseKnnVectorsFormatTestCase.java
+++ b/lucene/test-framework/src/java/org/apache/lucene/tests/index/BaseKnnVectorsFormatTestCase.java
@@ -81,7 +81,8 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
   protected void addRandomFields(Document doc) {
     switch (vectorEncoding) {
       case BYTE -> doc.add(new KnnByteVectorField("v2", randomVector8(30), similarityFunction));
-      case FLOAT32 -> doc.add(new KnnFloatVectorField("v2", randomVector(30), similarityFunction));
+      case FLOAT32 -> doc.add(
+          new KnnFloatVectorField("v2", randomNormalizedVector(30), similarityFunction));
     }
   }
 
@@ -611,7 +612,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
         // assert that knn search doesn't fail on a field with all deleted docs
         TopDocs results =
             leafReader.searchNearestVectors(
-                "v", randomVector(3), 1, leafReader.getLiveDocs(), Integer.MAX_VALUE);
+                "v", randomNormalizedVector(3), 1, leafReader.getLiveDocs(), Integer.MAX_VALUE);
         assertEquals(0, results.scoreDocs.length);
       }
     }
@@ -664,7 +665,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
                 fieldTotals[field] += b[0];
               }
               case FLOAT32 -> {
-                float[] v = randomVector(fieldDims[field]);
+                float[] v = randomNormalizedVector(fieldDims[field]);
                 doc.add(new KnnFloatVectorField(fieldName, v, fieldSimilarityFunctions[field]));
                 fieldTotals[field] += v[0];
               }
@@ -885,7 +886,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
       for (int i = 0; i < numDoc; i++) {
         if (random().nextInt(7) != 3) {
           // usually index a vector value for a doc
-          values[i] = randomVector(dimension);
+          values[i] = randomNormalizedVector(dimension);
           ++numValues;
         }
         if (random().nextBoolean() && values[i] != null) {
@@ -1033,7 +1034,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
         float[] value;
         if (random().nextInt(7) != 3) {
           // usually index a vector value for a doc
-          value = randomVector(dimension);
+          value = randomNormalizedVector(dimension);
         } else {
           value = null;
         }
@@ -1061,7 +1062,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
           TopDocs results =
               ctx.reader()
                   .searchNearestVectors(
-                      fieldName, randomVector(dimension), k, liveDocs, visitedLimit);
+                      fieldName, randomNormalizedVector(dimension), k, liveDocs, visitedLimit);
           assertEquals(TotalHits.Relation.GREATER_THAN_OR_EQUAL_TO, results.totalHits.relation);
           assertEquals(visitedLimit, results.totalHits.value);
 
@@ -1071,7 +1072,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
           results =
               ctx.reader()
                   .searchNearestVectors(
-                      fieldName, randomVector(dimension), k, liveDocs, visitedLimit);
+                      fieldName, randomNormalizedVector(dimension), k, liveDocs, visitedLimit);
           assertEquals(TotalHits.Relation.EQUAL_TO, results.totalHits.relation);
           assertTrue(results.totalHits.value <= visitedLimit);
         }
@@ -1097,7 +1098,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
         float[] value;
         if (random().nextInt(7) != 3) {
           // usually index a vector value for a doc
-          value = randomVector(dimension);
+          value = randomNormalizedVector(dimension);
         } else {
           value = null;
         }
@@ -1147,7 +1148,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
           TopDocs results =
               ctx.reader()
                   .searchNearestVectors(
-                      fieldName, randomVector(dimension), k, liveDocs, Integer.MAX_VALUE);
+                      fieldName, randomNormalizedVector(dimension), k, liveDocs, Integer.MAX_VALUE);
           assertEquals(Math.min(k, size), results.scoreDocs.length);
           for (int i = 0; i < k - 1; i++) {
             assertTrue(results.scoreDocs[i].score >= results.scoreDocs[i + 1].score);
@@ -1233,13 +1234,18 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
         squareSum += v[i] * v[i];
       }
     }
+    return v;
+  }
+
+  protected float[] randomNormalizedVector(int dim) {
+    float[] v = randomVector(dim);
     VectorUtil.l2normalize(v);
     return v;
   }
 
   private byte[] randomVector8(int dim) {
     assert dim > 0;
-    float[] v = randomVector(dim);
+    float[] v = randomNormalizedVector(dim);
     byte[] b = new byte[dim];
     for (int i = 0; i < dim; i++) {
       b[i] = (byte) (v[i] * 127);
@@ -1251,10 +1257,14 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
     try (Directory dir = newDirectory()) {
       try (IndexWriter w = new IndexWriter(dir, newIndexWriterConfig())) {
         Document doc = new Document();
-        doc.add(new KnnFloatVectorField("v1", randomVector(3), VectorSimilarityFunction.EUCLIDEAN));
+        doc.add(
+            new KnnFloatVectorField(
+                "v1", randomNormalizedVector(3), VectorSimilarityFunction.EUCLIDEAN));
         w.addDocument(doc);
 
-        doc.add(new KnnFloatVectorField("v2", randomVector(3), VectorSimilarityFunction.EUCLIDEAN));
+        doc.add(
+            new KnnFloatVectorField(
+                "v2", randomNormalizedVector(3), VectorSimilarityFunction.EUCLIDEAN));
         w.addDocument(doc);
       }
 
@@ -1359,7 +1369,7 @@ public abstract class BaseKnnVectorsFormatTestCase extends BaseIndexFileFormatTe
               doc.add(new KnnByteVectorField("knn_vector", b, similarityFunction));
             }
             case FLOAT32 -> {
-              float[] v = randomVector(dim);
+              float[] v = randomNormalizedVector(dim);
               fieldValuesCheckSum += v[0];
               doc.add(new KnnFloatVectorField("knn_vector", v, similarityFunction));
             }