diff --git a/ee/session_recordings/ai/error_clustering.py b/ee/session_recordings/ai/error_clustering.py
index edc06ff471355..7a3c12c44dec0 100644
--- a/ee/session_recordings/ai/error_clustering.py
+++ b/ee/session_recordings/ai/error_clustering.py
@@ -6,6 +6,7 @@
 import pandas as pd
 import numpy as np
 from posthog.session_recordings.models.session_recording_event import SessionRecordingViewed
+from datetime import date
 
 CLUSTER_REPLAY_ERRORS_TIMING = Histogram(
     "posthog_session_recordings_cluster_replay_errors",
@@ -30,7 +31,7 @@ def error_clustering(team: Team, user: User):
     if not results:
         return []
 
-    df = pd.DataFrame(results, columns=["session_id", "input", "embeddings"])
+    df = pd.DataFrame(results, columns=["session_id", "error", "embeddings", "timestamp"])
 
     df["cluster"] = cluster_embeddings(df["embeddings"].tolist())
 
@@ -42,7 +43,7 @@ def error_clustering(team: Team, user: User):
 def fetch_error_embeddings(team_id: int):
     query = """
             SELECT
-                session_id, input, embeddings
+                session_id, input, embeddings, generation_timestamp
             FROM
                 session_replay_embeddings
             WHERE
@@ -76,13 +77,21 @@ def construct_response(df: pd.DataFrame, team: Team, user: User):
     clusters = []
     for cluster, rows in df.groupby("cluster"):
         session_ids = rows["session_id"].unique()
-        sample = rows.sample(n=1)[["session_id", "input"]].rename(columns={"input": "error"}).to_dict("records")[0]
+        sample = rows.sample(n=1)[["session_id", "error"]].to_dict("records")[0]
+
+        date_series = (
+            df.groupby([df["timestamp"].dt.date])
+            .size()
+            .reindex(pd.date_range(end=date.today(), periods=7), fill_value=0)
+        )
+        sparkline = dict(zip(date_series.index.astype(str), date_series))
         clusters.append(
             {
                 "cluster": cluster,
                 "sample": sample.get("error"),
                 "session_ids": np.random.choice(session_ids, size=DBSCAN_MIN_SAMPLES - 1),
                 "occurrences": rows.size,
+                "sparkline": sparkline,
                 "unique_sessions": len(session_ids),
                 "viewed": len(np.intersect1d(session_ids, viewed_session_ids, assume_unique=True)),
             }
diff --git a/frontend/src/scenes/session-recordings/errors/SessionRecordingErrors.tsx b/frontend/src/scenes/session-recordings/errors/SessionRecordingErrors.tsx
index 4b2dd2d1abed3..8b73fbcc1f924 100644
--- a/frontend/src/scenes/session-recordings/errors/SessionRecordingErrors.tsx
+++ b/frontend/src/scenes/session-recordings/errors/SessionRecordingErrors.tsx
@@ -2,6 +2,7 @@ import { IconFeatures } from '@posthog/icons'
 import { LemonButton, LemonTable, LemonTabs, Spinner } from '@posthog/lemon-ui'
 import { useActions, useValues } from 'kea'
 import { JSONViewer } from 'lib/components/JSONViewer'
+import { Sparkline } from 'lib/lemon-ui/Sparkline'
 import { useState } from 'react'
 import { urls } from 'scenes/urls'
 
@@ -45,6 +46,17 @@ export function SessionRecordingErrors(): JSX.Element {
                         },
                         width: '50%',
                     },
+                    {
+                        title: '',
+                        render: (_, cluster) => {
+                            return (
+                                <Sparkline
+                                    labels={Object.keys(cluster.sparkline)}
+                                    data={Object.values(cluster.sparkline)}
+                                />
+                            )
+                        },
+                    },
                     {
                         title: 'Occurrences',
                         dataIndex: 'occurrences',
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index a7f65e84c5473..be96494253ae8 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -906,6 +906,7 @@ export type ErrorCluster = {
     sample: string
     occurrences: number
     session_ids: string[]
+    sparkline: Record<string, number>
     unique_sessions: number
     viewed: number
 }