diff --git a/docs/docs/get-started/what-is-openrag.mdx b/docs/docs/get-started/what-is-openrag.mdx
index bae90d26..129d2df9 100644
--- a/docs/docs/get-started/what-is-openrag.mdx
+++ b/docs/docs/get-started/what-is-openrag.mdx
@@ -83,4 +83,43 @@ The **OpenRAG Backend** is the central orchestration service that coordinates al
 
 **Third Party Services** like **Google Drive** connect to the **OpenRAG Backend** through OAuth authentication, allowing synchronication of cloud storage with the OpenSearch knowledge base.
 
-The **OpenRAG Frontend** provides the user interface for interacting with the system.
\ No newline at end of file
+The **OpenRAG Frontend** provides the user interface for interacting with the system.
+
+## Performance expectations
+
+On a local VM with 7 vCPUs and 8 GiB RAM, OpenRAG ingested approximately 5.03 GB across 1,083 files in about 42 minutes.
+This equates to approximately 2.4 documents per second.
+
+You can generally expect equal or better performance on developer laptops and significantly faster on servers.
+Throughput scales with CPU cores, memory, storage speed, and configuration choices such as embedding model, chunk size and overlap, and concurrency.
+
+This test returned 12 errors (approximately 1.1%).
+All errors were file‑specific, and they didn't stop the pipeline.
+
+Ingestion dataset:
+
+* Total files: 1,083 items mounted
+* Total size on disk: 5,026,474,862 bytes (approximately 5.03 GB)
+
+Hardware specifications:
+
+* Machine: Apple M4 Pro
+* Podman VM:
+  * Name: `podman-machine-default`
+  * Type: `applehv`
+  * vCPUs: 7
+  * Memory: 8 GiB
+  * Disk size: 100 GiB
+
+Test results:
+
+```text
+2025-09-24T22:40:45.542190Z /app/src/main.py:231 Ingesting default documents when ready disable_langflow_ingest=False
+2025-09-24T22:40:45.546385Z /app/src/main.py:270 Using Langflow ingestion pipeline for default documents file_count=1082
+...
+2025-09-24T23:19:44.866365Z /app/src/main.py:351 Langflow ingestion completed success_count=1070 error_count=12 total_files=1082
+```
+
+Elapsed time: ~42 minutes 15 seconds (2,535 seconds)
+
+Throughput: ~2.4 documents/second
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