diff --git a/pytensor/link/c/cmodule.py b/pytensor/link/c/cmodule.py
index f1f098edbf..13ed2b3d9f 100644
--- a/pytensor/link/c/cmodule.py
+++ b/pytensor/link/c/cmodule.py
@@ -2947,6 +2947,13 @@ def check_libs(
     except Exception as e:
         _logger.debug(e)
     _logger.debug("Failed to identify blas ldflags. Will leave them empty.")
+    warnings.warn(
+        "PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.\n"
+        "This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.\n"
+        "Alternatively, you can use a experimental backend such as Numba or JAX that perform their own BLAS optimizations, "
+        "by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.",
+        UserWarning,
+    )
     return ""
 
 
diff --git a/pytensor/tensor/blas_headers.py b/pytensor/tensor/blas_headers.py
index 2806bfc41d..645f04bfb3 100644
--- a/pytensor/tensor/blas_headers.py
+++ b/pytensor/tensor/blas_headers.py
@@ -742,6 +742,11 @@ def blas_header_text():
 
     blas_code = ""
     if not config.blas__ldflags:
+        # This code can only be reached by compiling a function with a manually specified GEMM Op.
+        # Normal PyTensor usage will end up with Dot22 or Dot22Scalar instead,
+        # which opt out of C-code completely if the blas flags are missing
+        _logger.warning("Using NumPy C-API based implementation for BLAS functions.")
+
         # Include the Numpy version implementation of [sd]gemm_.
         current_filedir = Path(__file__).parent
         blas_common_filepath = current_filedir / "c_code/alt_blas_common.h"
@@ -1003,10 +1008,6 @@ def blas_header_text():
     return header + blas_code
 
 
-if not config.blas__ldflags:
-    _logger.warning("Using NumPy C-API based implementation for BLAS functions.")
-
-
 def mkl_threads_text():
     """C header for MKL threads interface"""
     header = """