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97.3%of 37 verified observed days had no reported provider issue
90-Day Trend
Dashed calendar markers below identify legacy observations whose source-at-observation was not recorded.
Monthly Status Summary
Month
Issue-free days
Days Tracked
Days with issues
September 2026
96.7%
30
1
This percentage summarizes normalized provider-status observations by calendar day. It is not duration-based, component-weighted, or contractual uptime. See the methodology and limitations.
Recorded Daily Status (Last 91 Days)
Jul 2Today
Legacy observation Operational Degraded Partial Outage Major Outage Maintenance No Data
Incident History
September 2026
Delayed file processing for models in app.nanonets.com
A fix has been implemented and we are monitoring the results.
identified
The issue has been identified and a fix is being implemented.
investigating
We are currently investigating this issue.
Legacy Incident Archive
2 archived records restored. These records predate source-at-observation attestation. They are shown as historical ServiceAlert records only and do not establish a provider-reported incident, resolution, duration, or current active state.
July 2026
Delayed file processing for ILM models in app.nanonets.com
minorSource not attested
Last archived state: resolved
View archived record details
monitoring
A fix has been implemented and we are monitoring the results.
identified
The issue has been identified and a fix is being implemented.
investigating
We are currently investigating this issue.
April 2026
Elevated 5xx Errors & Timeouts for Instant Learning (LLM Nano)
majorSource not attested
Last archived state: resolved
View archived record details
monitoring
We’ve temporarily routed LLM Nano traffic to LLM Mini, a more stable, accurate and higher-capacity variant, to mitigate errors.
File processing should now be faster and more reliable while we continue working on resolving the underlying issue.
investigating
We are currently investigating an issue affecting our GPU service provider infrastructure, which is causing elevated 5xx errors and timeouts for Instant Learning models using LLM Nano.
Our team is actively working with the provider to identify and resolve the underlying network issues. We will share further updates as soon as we have more information.
