diff --git a/src/backend/langflow/components/models/AmazonBedrock.py b/src/backend/langflow/components/models/AmazonBedrock.py
new file mode 100644
index 000000000..7b38cfa52
--- /dev/null
+++ b/src/backend/langflow/components/models/AmazonBedrock.py
@@ -0,0 +1,62 @@
+from typing import Optional
+
+from langchain.llms.base import BaseLLM
+from langchain.llms.bedrock import Bedrock
+from langflow import CustomComponent
+
+
+class AmazonBedrockComponent(CustomComponent):
+    display_name: str = "Amazon Bedrock model"
+    description: str = "LLM model from Amazon Bedrock."
+
+    def build_config(self):
+        return {
+            "model_id": {
+                "display_name": "Model Id",
+                "options": [
+                    "ai21.j2-grande-instruct",
+                    "ai21.j2-jumbo-instruct",
+                    "ai21.j2-mid",
+                    "ai21.j2-mid-v1",
+                    "ai21.j2-ultra",
+                    "ai21.j2-ultra-v1",
+                    "anthropic.claude-instant-v1",
+                    "anthropic.claude-v1",
+                    "anthropic.claude-v2",
+                    "cohere.command-text-v14",
+                ],
+            },
+            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
+            "streaming": {"display_name": "Streaming", "field_type": "bool"},
+            "endpoint_url": {"display_name": "Endpoint URL"},
+            "region_name": {"display_name": "Region Name"},
+            "model_kwargs": {"display_name": "Model Kwargs"},
+            "cache": {"display_name": "Cache"},
+            "code": {"advanced": True},
+            "inputs": {"display_name": "Input"},
+        }
+
+    def build(
+        self,
+        inputs: str,
+        model_id: str = "anthropic.claude-instant-v1",
+        credentials_profile_name: Optional[str] = None,
+        region_name: Optional[str] = None,
+        model_kwargs: Optional[dict] = None,
+        endpoint_url: Optional[str] = None,
+        streaming: bool = False,
+        cache: Optional[bool] = None,
+    ) -> BaseLLM:
+        try:
+            output = Bedrock(
+                credentials_profile_name=credentials_profile_name,
+                model_id=model_id,
+                region_name=region_name,
+                model_kwargs=model_kwargs,
+                endpoint_url=endpoint_url,
+                streaming=streaming,
+                cache=cache,
+            )  # type: ignore
+        except Exception as e:
+            raise ValueError("Could not connect to AmazonBedrock API.") from e
+        return output.invoke(input=inputs)
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