Spark Hire Meet Uptime History
Measured by PulsAPI's own checks, not reported by Spark Hire Meet. Every figure below is computed over the 90 days in the last 365 that PulsAPI actually observed, from 2026-08-08 to 2026-11-05. Days nobody watched are left out rather than counted as perfect.
Spark Hire Meet Uptime at a Glance
- Measured uptime
- 100.00%
- Days with an issue
- 1
- Longest outage
- 1h 38m
- Mean recovery
- 1h 19m
across 90 observed days
day was not fully operational
single longest resolved incident
average time to resolve
Monthly Spark Hire Meet Uptime
Each month is the mean of its observed days. Months with few observed days are marked, because a short month is not evidence of a good one.
| Month | Uptime | Uptime bar | Days observed |
|---|---|---|---|
| November 2026 | 100.00% | 5partial | |
| October 2026 | 100.00% | 31 | |
| September 2026 | 100.00% | 30 | |
| August 2026 | 100.00% | 24 |
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How This Spark Hire Meet Uptime Figure Is Measured
PulsAPI checks Spark Hire Meet continuously (every 60 seconds) and records the result. A day's uptime is the share of that day's checks that came back fully operational, so a 20-minute outage shows up as a dip rather than being rounded away. The window figure is the mean of those daily values.
Two things this deliberately is not. It is not Spark Hire Meet's own published uptime, because vendors generally measure against their own SLA definitions, which usually exclude scheduled maintenance and often exclude partial degradation. And it is not a promise about the future: 90 days of history describes what happened, and the useful way to read it is alongside the incidents behind the dips.
Uptime measured this way is a mean of component uptimes where a vendor publishes components, so coverage matters as much as the percentage. That is why the observed-day count sits beside every figure on this page rather than in a footnote.
These measurements are published under CC BY 4.0. Quote or republish them freely, with attribution to PulsAPI.