The observability pyramid is upside down - too much data at the top, not enough insight delivered. But now, the pyramid is crumbling under its own weight.For years, we’ve treated observability like an inverted pyramid – wide at the top with massive data lakes floating in the sky, narrow at the bottom with little edge intelligence. The model was simple: collect everything first, store it all, and try to make sense of it later.
But over time, that inversion has become untenable —-MTTRs are rising, SREs are burning out, and teams are drowning in telemetry that overflows both our human and AI context windows. The pyramid is crumbling and collapsing under its own weight – too much data at the top, not enough insight delivered.
The Top-Heavy Trap
Traditional observability has been built around centralization: ship everything into one place, then analyze it. That approach created a massive, expensive data lake full of raw telemetry – where humans try to interpret it all through dashboards and alerts.
The result?
- We’ve inverted our effort – spending more time managing data than learning from it
- Worse, we have pushed top-heavy observability models optimized for collection, not comprehension – beautiful systems that can see everything but understand almost nothing.
Distillation: The Force That Flips the Pyramid Right-side Up
To fix the structure, we don’t need more or faster or cheaper storage – they are all just addressing the symptoms – we need a process that inverts the model and offers a cure
That process is distillation: refining telemetry at the edge, compressing noise into signal, and passing forward only what drives context and understanding.
Distillation turns raw data into structured insight before it piles up, before you pay for all that ingest, indexing, and retention, and before your job is reduced to wading through it all.
- It doesn’t reduce observability – it restores it.
- With distillation, the pyramid rights itself naturally: wide at the base to collect what matters, narrow at the top with insight and impact.
Continuous AI as the Foundation
Distillation is powerful, but what makes it sustainable is Continuous AI — intelligence that’s always watching, adapting, and learning. Through three continuous layers – Edge Distillation, Operational Inference, and Continuous Agentic Intelligence – data collection evolves into living comprehension.
AI doesn’t replace human reasoning; it reinforces it, making understanding load-bearing again. Continuous AI transforms observability from a reporting function into an always-on process of understanding.
Why This Shift Matters
Modern observability can’t scale linearly with data – and AI context windows prevent it from trying. Kubernetes, microservices, and inference workloads are pushing telemetry volumes beyond human capacity. The old model – collecting everything and analyzing later – is mathematically unsustainable and philosophically out of sync with the potential of AI.
Distillation and continuous learning aren’t optimizations; they’re necessities for balance. We don’t need more data lakes and dashboards – we need stronger foundations for understanding.
Looking Ahead to KubeCon
At KubeCon + CloudNativeCon North America 2025, look for the themes of distillation and Continuous AI to run through every where – from AI-driven edge telemetry to leaner data architectures and self-evolving systems. ControlTheory will be there – booth #1570 – with our observability distillation solution Dstl8 powered by Möbius Continuous AI.
We’re not tearing observability down – we’re rebuilding it to stand upright again. If the last decade was about observability, the next will be about comprehension – powered by distillation and Continuous AI.
Back
press@controltheory.com
