I was experimenting with four different large language model services this morning, and all of them stopped working at almost exactly the same time. A time-of-day traffic spike seems possible, but I would have expected at least one service to remain available. Could increased usage from students or another sudden demand surge explain it? I also checked for obvious DNS problems and did not see signs of a widespread internet outage. What shared dependency or local issue could cause multiple AI services to fail simultaneously?
4 Answers
It is worth checking the exact error messages, timestamps, and whether the failures happen on another network or device. If they work over cellular data but not your normal connection, that points strongly to a local network or ISP issue. If they fail everywhere, compare the providers' status pages and incident reports.
The services may not be as independent as they look. Several products use the same underlying model, cloud provider, authentication system, DNS or CDN, or other shared infrastructure. A failure in one of those dependencies can make multiple front ends appear to go down at once.
You may be the common factor here. If all four services failed only from your connection, the problem is more likely your LAN, router, ISP, DNS resolver, VPN, or browser session than four independent providers failing together.
A coordinated traffic spike is another possibility. If many users arrive at once, providers can hit rate limits or capacity limits, and several services may degrade around the same time even if their physical data centers are separate. Regional routing or an upstream network provider could create the same appearance.
Also, some products are mainly a user interface or orchestration layer around models supplied by another company, so different brands do not necessarily mean different backends.