Decoupled Observability stacks Free Teams

In 2026, Eric Tschetter, chief architect at Imply, predicts the end of the all-in-one observability black box. AI is driving massive growth in logs, metrics, and traces, pushing tightly coupled, monolithic observability platforms past their architectural limits. Organizations are hitting a breaking point. They can't scale their current observability stacks without tradeoffs: either lose visibility by offloading or sampling data, or absorb runaway infrastructure and licensing costs just to keep data searchable.

Forward-thinking teams are already rethinking their architecture from the ground up, pulling the data layer apart from the tools that sit on top of it. We’ve seen this before in business intelligence (BI). Over the last 40 years, BI evolved from tightly coupled stacks—where collection, storage, compute, and visualization were shipped together—to decoupled, three-layer architectures that gave teams more flexibility and control. That shift is why teams today mix and match tools like Tableau with Snowflake or Databricks with Power BI without moving data or rewriting workflows. Observability is now at the same inflection point. Only this time, AI is the accelerator, and exploding telemetry volumes are the pressure forcing the shift.

Decoupled Observability Stack + Observability Warehouses = Do More with Your Data, For Less

Decoupling the stack separates the collection and routing layer from the data and visualization layers so each can scale independently. This allows teams to expand retention (without proportional indexing costs), improve search speed (without re-architecting dashboards), modernize backend engines (without retraining users), and adopt new tools (without getting trapped into a single vendor’s ecosystem).

Teams already offload portions of observability data into cloud object storage to control costs. Yet, cheap storage doesn’t address the main challenge: effective incident response depends on fast, interactive access to telemetry data across long time ranges. This is where the observability warehouse emerges as a solution. A data layer purpose-built for logs, metrics, and traces that keeps telemetry data quickly searchable, cost-predictable, and decoupled from tools analyzing it. 

By the end of 2026, observability warehouses will become the standard foundation of modern observability stacks. This marks a clear shift away from monolithic black boxes toward decoupled ecosystems where teams can choose the best tools for their needs, without paying a penalty in performance, complexity, or cost.