DevOps / SRE / Platform · 26.08.2026, 20:17 UTC
Observability has a data problem. AI is about to make it worse.
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 26.08.2026 UTC |
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Observability is entering a new phase now that OpenTelemetry has standardized instrumentation for data collection. Unfortunately, the observability industry still lacks a cost-effective way to store, retain, search, and analyze full-fidelity telemetry data. This results in blind spots in observability and many teams operating without full operational visibility.
As AI systems generate more logs, traces, and metrics — thereby making the blind spots issue worse — Bronto, a Dublin, Ireland, firm offering an intelligent data observability platform, is betting that the next observability platform battle will be won at the data layer, not the dashboard layer.
Bolt-ons and incremental efficiency aren’t enough
Trevor Parsons, co-founder and co-CEO of Bronto, tells The New Stack that the industry has been optimizing at the edges rather than rebuilding the economics and architecture of telemetry storage. The industry has introduced a wide array of “hacks” and “capabilities” to avoid tackling this issue head-on and ultimately to protect their margins.
“If you are a couple of times cheaper or 50% cheaper, that ain’t going to cut it,” Parsons says, because data volumes, especially AI telemetry, are growing so quickly, on top of already stretched observability budgets and inefficient datastores.
Promises, Promises, Promises…
Parsons elaborates, “Observability has always and continues to have a data problem.”
The eternal promise of observability has been delivering teams a clearer view of what’s happening inside their systems.
“Observability has always and continues to have a …