Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Eric Tschetter, Chief Architect at Imply
For years, observability platforms promised an all-in-one experience: collect, store, search, and visualize telemetry in one tightly integrated system. But AI-driven systems are exploding log, metric, and trace volumes faster than monolithic architectures can support. Teams end up trapped in an “observability black box,” forced into trade-offs by either reducing visibility by offloading or sampling data, or accepting runaway infrastructure and licensing costs to keep data searchable.
Imply’s The Breaking Point for Observability Leaders report found that nearly 80% of respondents admit they filter, archive, or offload logs just to stay within budget. Teams are spending more and seeing less. In 2026, this tension won’t just persist. It will hit a breaking point.
AI-driven growth breaks monoliths
As telemetry volumes accelerate, monolithic observability stacks force teams into all-or-nothing scaling: expand ingestion and you must expand indexing, storage, compute, and sometimes even the UI tier. Routine needs like longer retention, faster queries, or scaling to new applications trigger costly upgrades and operational friction. Cold tiers help with budget, but they slow investigations when speed matters most.
This is why the future of observability is moving toward a fundamentally different model: a decoupled architecture built around an independent, scalable data layer.
The natural evolution to a decoupled stack
Most observability platforms are tightly interlocked, so when telemetry grows, teams are forced to scale the entire stack at once. In a decoupled model, the collection and routing layer is separated from the data layer and the visualization layer. Each component can scale independently, letting teams:
- Expand retention without proportional indexing cost
- Improve search speed without re-architecting dashboards
- Modernize backend engines without retraining users
- Adopt new tools without vendor lock-in
This separation brings predictability back to cost and performance. It also ensures the right data stays readily accessible when incidents occur.
We’ve seen it happen before with business intelligence tools.
Observability is following BI’s footsteps
Business intelligence began as tightly bundled stacks in the 1980s, with collection, storage, compute, and visualization all shipped together. As data volumes grew and specialized tools matured, the market shifted to a decoupled, three-layer architecture:
- Ingestion and Transformation
- Storage and Compute
- Visualization and Exploration
This shift unlocked the modern BI ecosystem we know today, where teams can pair Tableau with Snowflake, or Databricks with Power BI, without moving data or rewriting workflows. This decoupled model provides teams with the flexibility needed to scale systems easily. Observability is hitting the same inflection point now. Only this time, the accelerator is AI, and it’s exploding telemetry volumes.
Enter: The observability warehouse
An observability warehouse is the missing backbone for modern observability: a purpose-built data layer optimized for logs, metrics, and traces that delivers hot-tier performance at low, predictable cost. Think of it as the foundation that allows the rest of the observability stack to scale independently-similar to how cloud data warehouses transformed BI.
Many organizations already offload some of their observability data into cloud object storage to control spend. But cheap storage alone doesn’t solve the core problem: incident response demands interactive, real-time exploration. An observability warehouse closes that gap by making all telemetry readily searchable at scale while lowering unit costs and avoiding vendor lock-in.
By the end of 2026, we expect more teams to adopt observability warehouses as a common pattern for breaking out of the “observability” black box loop. Without a dedicated data layer to address telemetry’s continued growth, rising costs lead to shorter retention and colder data. The payoff? Slower investigations and higher MTTR when incidents hit. An observability warehouse breaks the cycle by keeping telemetry searchable without increased costs, so teams can evolve tools on top without disruption.
##
ABOUT THE AUTHOR
Eric Tschetter is one of the original authors of the open source Apache Druid� project and Chief Architect at Imply. He has been consistently working with Druid throughout his roles as a Fellow at Splunk and before that as a Distinguished Engineer at Yahoo Inc. Eric was a member of the founding team of Tidepool, a Diabetes Data Non-Profit, and was the VP of Engineering at Metamarkets, where Druid originated. Eric holds a Master’s in Computer Science from the University of Tokyo and B.A. in Computer Science and Japanese from the University of Texas at Austin.
