AI safety company founded by ex-OpenAI researchers Dario and Daniela Amodei. Builds the Claude family of large language models with a focus on steerability, interpretability, and reducing harmful outputs.
Method: a rolling quarter (13 weeks ≈ 91 days). Each cell is one 7-day slice of Statuspage-standard uptime (1 − (critical + 0.3 × major) / window; minors and maintenance excluded; red when the slice dips below the stated SLA).
Claude — Incidents by Day last 14d
MinorMajorCritical
Total 5 incidents
0
1
1
2
2
09-3010-0210-0410-0610-0810-1010-1210-14
Worst Incidents
Ranked by severity-weighted duration (impact × wall-clock). Click any incident for the full update timeline.
Type of Failure
Categorised from the public incident headlines (outage / latency / auth / etc.).
Elevated errors
4
67%
Latency
1
17%
Degraded
1
17%
When It Breaks
Incident starts by weekday × hour, UTC. Hover a cell for incident detail.
S
0
M
Monday13:00–14:00 UTC
1incident
1 major
Elevated errors for Claude Mythos 5.1 and Claude Fable 5.1Oct 5
1
T
Tuesday12:00–13:00 UTC
1incident
1 major
Elevated errors for Claude Opus 5.5Oct 6
1
W
Wednesday01:00–02:00 UTC
1incident
1 major
Elevated errors loading usage data on platform.claude.comOct 7
Wednesday13:00–14:00 UTC
1incident
1 major
Elevated errors on platform.claude.comOct 7
2
T
Thursday16:00–17:00 UTC
1incident
1 minor
Delayed credits on the Claude PlatformOct 1
1
F
0
S
0
00
06
12
18
HOUR OF DAY · UTC5 incident starts · hover any cell for detail
Hottest hour
Monday 13:00 1
Worst day
Wednesday 2
Worst time of day
13:00–14:00 2
How fast they fix things
Distribution of resolution times alongside the 12-week trend so you can see whether they're getting faster or slower.
Typical fix
19m
half of incidents resolve faster
On a bad day
148h 35m
9 in 10 resolve faster than this
Distribution
<15m
1
15m–1h
2
1–4h
1
4–24h
1
24h+
1
+0 ongoing (not counted above)
MTTR Trend (12 weeks)
Are they recovering faster or slower over time? Lower is better.