diff --git a/apps/www/data/products/vector/pageData.tsx b/apps/www/data/products/vector/pageData.tsx
index c9b360178e8..b4cea2e68a1 100644
--- a/apps/www/data/products/vector/pageData.tsx
+++ b/apps/www/data/products/vector/pageData.tsx
@@ -12,15 +12,15 @@ export default {
     title: 'Supabase Vector',
     h1: (
       <span key={'vector-h1'} className="heading-gradient">
-        The best vector database  <br />
-        is the database you already have
+        The open source Vector Toolkit  <br />
+        for Postgres
       </span>
     ),
     subheader: (
       <>
-        An open source toolkit for developing AI applications using Postgres and pgvector. 
-        <br className="hidden md:block" />
         Use the Supabase client libraries to store, index, and query your vector embeddings at scale.
+        <br className="hidden md:block" />
+        Build AI applications with Postgres and pgvector: the best vector database is the database you already have.
       </>
     ),
     image: '/images/product/vector/vector-hero.svg',
diff --git a/apps/www/pages/vector/Vector.tsx b/apps/www/pages/vector/Vector.tsx
index 398ca5cf381..68c2b0e25a9 100644
--- a/apps/www/pages/vector/Vector.tsx
+++ b/apps/www/pages/vector/Vector.tsx
@@ -21,9 +21,9 @@ const EnterpriseCta = dynamic(() => import('~/components/Sections/EnterpriseCta'
 function VectorPage() {
   // base path for images
   const { basePath } = useRouter()
-  const meta_title = 'Supabase Vector | The best vector database is the database you already have.'
+  const meta_title = 'Supabase Vector | The open source Vector Toolkit for Postgres.'
   const meta_description =
-    'An open source toolkit for developing AI applications using Postgres and pgvector. Integrate Supabase Vector database with your favorite ML-models to store, index and access vector embeddings for any AI use case.'
+    'An open source toolkit for developing AI applications using Postgres and pgvector. Use the Supabase Vector Toolkit with your favorite ML-models to store, index, and access vector embeddings for any AI use case.'
 
   return (
     <>