● Covered by 1 source · 1 reportLow impact1 negative
Observability faces data storage challenges exacerbated by AI-generated telemetry
Key points
- Observability lacks cost-effective full-fidelity telemetry data storage.
- AI systems will generate more logs, traces, and metrics.
- Existing solutions are "hacks" that don't address core storage economics.
- Observability costs can be 10-30% of infrastructure spend for partial data access.
The Observability Data Challenge
The observability industry is currently facing a significant challenge in cost-effectively storing, retaining, searching, and analyzing full-fidelity telemetry data. Despite advancements like OpenTelemetry standardizing instrumentation, many teams operate without complete operational visibility due to these data storage limitations.
AI's Impact on Telemetry Volume
The increasing adoption of AI systems is projected to exacerbate this data problem. AI systems generate a higher volume of logs, traces, and metrics, which will further strain existing observability infrastructures and increase the blind spots for teams trying to monitor their systems.
Ineffective Current Solutions
According to Trevor Parsons, co-founder and co-CEO of Bronto, the industry has largely focused on incremental optimizations rather than fundamentally rebuilding the economics and architecture of telemetry storage. Existing "hacks" and "capabilities" are insufficient to address the rapid growth in data volumes, especially AI telemetry, given already strained observability budgets and inefficient datastores.
High Costs for Partial Visibility
Observability often consumes a significant portion of infrastructure spending, with some estimates placing it at 10-30%. However, this expenditure frequently provides access to only a fraction of the necessary data, meaning teams are paying substantial amounts for incomplete system views. Noel Ruane, co-founder and co-CEO of Bronto, highlights that agents and applications are generating more data daily, while observability vendors have not kept pace with these demands.
✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →
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Reporting from
The observability industry struggles with cost-effectively storing and analyzing full-fidelity telemetry data, leading to incomplete system visibility. The rise of AI systems generating more logs, traces, and metrics is expected to worsen this existing data problem, increasing costs and blind spots for teams.