DevOps / SRE / Platform · 23.08.2026, 16:16 UTC
Why real-time AI at scale is so hard
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 23.08.2026 UTC |
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Real-time AI at scale is harder than it looks. Pipelines that hum along in development routinely hit problems in production. It’s always easy to blame the model for all your problems. But issues like rising latency and degrading accuracy can usually be traced back to the data pipeline.
My colleague Tim Koopmans and I recently discussed what typically goes wrong with real-time AI at scale. After Tim shared some hard-fought lessons learned, we talked about how to avoid falling into these traps yourself – including the practices and infrastructure choices that can help you avoid them. You can watch the full video or read the key points below.
Why AI performance fails at scale
Tim learned the following real-time AI performance lessons the hard way: through fits of frustration while building an ML-based financial trading app.
You can’t dig yourself out of tail latency
All too often, latency looks fine in testing, then a P99 spike surfaces under real concurrent load. For example, as Tim’s app approached ~740K operations per second, its P99 latency skyrocketed to 3 seconds.
“I kept blaming the model for being slow, but it turns out the model was fine,” Tim explained. “It was just that the feature lookups were killing me.” Each inference call was doing just a handful of reads, but those reads queued up [behind writes] under load. The average latencies seemed fine, but that P99 tail latency was just unacceptable.
“Tail latency isn’t a bug that you can fix, it’s a property of your architecture.”
Once you hit highly concurrent write throughput, you get lock contention – …