AI research and deployment company behind ChatGPT and the GPT family of models. Originally a non-profit; restructured into a capped-profit company in 2019. Mission framed as building AGI that benefits all of humanity.
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).
OpenAI — Incidents by Day last 30d
MinorMajorCritical
Total 1 incidents
0
0
1
1
1
05-0705-1105-1505-1905-2305-2705-3106-04
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
1
100%
When It Breaks
Incident starts by weekday × hour, UTC. Hover a cell for incident detail.
S
0
M
0
T
0
W
Wednesday04:00–05:00 UTC
1incident
1 major
Elevated error rates on Codex, ChatGPT and Responses APIJun 3
1
T
0
F
0
S
0
00
06
12
18
HOUR OF DAY · UTC1 incident starts · hover any cell for detail
Hottest hour
Wednesday 04:00 1
Worst day
Wednesday 1
Worst time of day
04:00–05:00 1
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
6h 11m
half of incidents resolve faster
On a bad day
6h 11m
9 in 10 resolve faster than this
Distribution
<15m
0
15m–1h
0
1–4h
0
4–24h
1
24h+
0
+0 ongoing (not counted above)
MTTR Trend (12 weeks)
Are they recovering faster or slower over time? Lower is better.