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diff --git a/scripts/benchmark/legacy-vs-blobs/legacy-vs-blobs.py b/scripts/benchmark/legacy-vs-blobs/legacy-vs-blobs.py
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+#!/usr/bin/env python
+
+# Plot bars comparing different implementations of mail pipeline.
+#
+# This script can be improved to account for arbitrary number of data sets, but
+# it is not doing it right now.
+
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+# make a prettier graph
+from mpltools import style
+style.use('ggplot')
+
+OUTPUT_FILENAME = 'legacy-vs-blobs.png'
+
+# each value below will generate one bar for each for each (amount, size) pair.
+# The script expects to find files in ./data/SET/ for each set of
+# implementations.
+#
+# The baseline values will be the legacy results in ./data/no-cache/.
+
+graphs = [
+    '1000_10k',
+    '100_100k',
+    '10_1000k',
+    '1_10000k',
+]
+
+
+# the JSON structure returned by the following function is ugly, but the
+# original JSONs are even uglier, so this is here just to make the life of the
+# script easier.
+#
+# We want to have something like:
+#
+#   data[variation][graph][implementation] = <stats>
+#
+# Where:
+#
+#   - variation is one data set under ./data (i.e. no-cache, cache, persistent,
+#     etc).
+#   - graph is one of the values in graphs variable above.
+#   - implementation is either legacy or blobs (we just need legacy for the
+#     no-cache variation, as that is the one we are using as baseline.
+
+def get_data():
+    folders = ['cache', 'no-cache', 'persistent']
+    data = {}
+    for folder in folders:
+        data[folder] = {}
+        for graph in graphs:
+            with open('data/%s/%s.json' % (folder, graph)) as f:
+                d = json.loads(f.read())
+                benchmarks = d['benchmarks']
+            data[folder][graph] = {}
+            for t in ['blobs', 'legacy']:
+                result = filter(lambda b: t in b['name'], benchmarks)
+                if result:
+                    result = result.pop()
+                    data[folder][graph][t] = result['stats']
+    return data
+
+
+def plot_data(data):
+
+    N = 4
+
+    # this is our baseline (i.e. legacy / legacy)
+    absolutes = (1, 1, 1, 1)
+
+    ind = np.arange(N)  # the x locations for the groups
+    width = 0.20        # the width of the bars
+
+    fig, ax = plt.subplots()
+    rects1 = ax.bar(ind, absolutes, width)
+
+    # for each graph, calculate the ratios
+    ratios = {'no-cache': [], 'cache': [], 'persistent': []}
+    for graph in graphs:
+        legacy = data['no-cache'][graph]['legacy']['mean']
+
+        # calculate ratios for no-cache / legacy
+        ratio = data['no-cache'][graph]['blobs']['mean'] / legacy
+        ratios['no-cache'].append(ratio)
+
+        # calculate ratios for cache / legacy
+        ratio = data['cache'][graph]['blobs']['mean'] / legacy
+        ratios['cache'].append(ratio)
+
+        # calculate ratios for persistent / legacy
+        ratio = data['persistent'][graph]['blobs']['mean'] / legacy
+        ratios['persistent'].append(ratio)
+
+    # create the boxes with the ratios
+    nocache = tuple(ratios['no-cache'])
+    rects2 = ax.bar(ind + width, nocache, width)
+
+    cache = tuple(ratios['cache'])
+    rects3 = ax.bar(ind + (2 * width), cache, width)
+
+    persistent = tuple(ratios['persistent'])
+    rects4 = ax.bar(ind + (3 * width), persistent, width)
+
+    # add some text for labels, title and axes ticks
+    ax.set_ylabel('Ratio of time (legacy is baseline)')
+    ax.set_xlabel('Amount and size of email messages')
+    ax.set_title('Inbox loading time: legacy vs blobs mail pipeline')
+    ax.set_xticks(ind + (1.5 * width))
+    ax.set_xticklabels(
+        tuple(map(lambda name: name.replace('_', ' x '), graphs)))
+
+    ax.legend(
+        (rects1[0], rects2[0], rects3[0], rects4[0]),
+        ('legacy', 'blobs', 'blobs + session cache',
+         'blobs + session cache + persistent http'))
+    # ax.grid()
+
+    plt.savefig(OUTPUT_FILENAME)
+    # plt.show()
+
+
+if __name__ == '__main__':
+    data = get_data()
+    plot_data(data)