diff --git a/server/app/scanpy_engine/scanpy_engine.py b/server/app/scanpy_engine/scanpy_engine.py
index 58c0843d..092a9b72 100644
--- a/server/app/scanpy_engine/scanpy_engine.py
+++ b/server/app/scanpy_engine/scanpy_engine.py
@@ -2,7 +2,7 @@ import warnings
 
 import numpy as np
 from pandas.core.dtypes.dtypes import CategoricalDtype
-import scanpy as sc
+import anndata
 from scipy import sparse
 
 from server.app.driver.driver import CXGDriver
@@ -140,11 +140,10 @@ class ScanpyEngine(CXGDriver):
                 self.schema["annotations"][ax].append(ann_schema)
 
     def _load_data(self, data):
-        # Based on benchmarking, cache=True has no impact on perf.
-        # Note: as of current scanpy/anndata release, setting backed='r' will
-        # result in an error.  https://github.com/theislab/anndata/issues/79
+        # as of AnnData 0.6.19, backed mode performs initial load fast, but at the
+        # cost of significantly slower access to X data.
         try:
-            self.data = sc.read(data, cache=True)
+            self.data = anndata.read_h5ad(data)
         except ValueError:
             raise ScanpyFileError(
                 "File must be in the .h5ad format. Please read "
@@ -174,6 +173,11 @@ class ScanpyEngine(CXGDriver):
 
     @requires_data
     def _validate_data_types(self):
+        if sparse.isspmatrix(self.data.X) and not sparse.isspmatrix_csc(self.data.X):
+            warnings.warn(
+                f"Scanpy data matrix is sparse, but not a CSC (columnar) matrix.  "
+                f"Performance may be improved by using CSC."
+            )
         if self.data.X.dtype != "float32":
             warnings.warn(
                 f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
diff --git a/server/cli/launch.py b/server/cli/launch.py
index 402fb431..1c4f1005 100644
--- a/server/cli/launch.py
+++ b/server/cli/launch.py
@@ -1,11 +1,12 @@
 import logging
 from os import devnull
-from os.path import splitext, basename
+from os.path import splitext, basename, getsize
 import sys
 import warnings
 import webbrowser
 
 import click
+import psutil
 
 from server.app.app import Server
 from server.app.util.errors import ScanpyFileError
@@ -13,6 +14,10 @@ from server.app.util.utils import custom_format_warning
 from server.utils.constants import MODES
 
 
+# anything bigger than this will generate a special message
+BIG_FILE_SIZE_THRESHOLD = 100 * 2**20  # 100MB
+
+
 @click.command()
 @click.argument("data", metavar="<data file>", type=click.Path(exists=True, file_okay=True, dir_okay=False))
 @click.option(
@@ -148,7 +153,16 @@ security risk by including the --scripts flag. Make sure you trust the scripts t
         log = logging.getLogger("werkzeug")
         log.setLevel(logging.ERROR)
 
-    click.echo(f"[cellxgene] Loading data from {basename(data)}, this may take awhile...")
+    file_size = getsize(data)
+
+    # if a big file, let the user know it may take a while to load.
+    if file_size > BIG_FILE_SIZE_THRESHOLD:
+        click.echo(f"[cellxgene] Loading data from {basename(data)}, this may take awhile...")
+    else:
+        click.echo(f"[cellxgene] Loading data from {basename(data)}.")
+    # if file is larger than main memory, let the user know performance may suffer
+    if file_size > .95 * psutil.virtual_memory().total:
+        click.echo(f"[cellxgene] Warning: data file is larger than RAM - application may be very slow.")
 
     # Fix for anaconda python. matplotlib typically expects python to be installed as a framework TKAgg is usually
     # available and fixes this issue. See https://matplotlib.org/faq/virtualenv_faq.html
diff --git a/server/requirements.txt b/server/requirements.txt
index 6219c809..fa30fa0d 100644
--- a/server/requirements.txt
+++ b/server/requirements.txt
@@ -1,3 +1,4 @@
+anndata>=0.6.15
 click>=6.7
 Flask>=1.0.2
 Flask-Caching>=1.4.0
@@ -8,6 +9,7 @@ flatbuffers>=1.10.0
 matplotlib>=2.2
 numpy>=1.15.2
 pandas>=0.23.1
+psutil>=5.6.2
 scanpy>=1.3.7
 scipy>=1.1.0
 scikit-learn>=0.19.1,!=0.20.0