Observability has become a growing challenge across today’s complex infrastructure landscapes. The shift to hybrid and multi-cloud infrastructures and increasing telemetry volumes have increased costs and complexity. Traditional observability solutions often operate as �dumb pipes,’ collecting vast amounts of data without extracting meaningful value. ControlTheory is changing this by introducing controllability-a new approach that actively manages observability through intelligent feedback loops, cost control, and operational efficiency.
VMblog sat down with Bob Quillin, CEO and Co-Founder of ControlTheory, to discuss how their platform is reshaping observability strategies and helping organizations cut costs, avoid vendor lock-in, and prepare for the future of AI-driven operations.
VMblog: Observability has become increasingly costly and complex. What’s driving this challenge, and why does ControlTheory believe controllability is the answer?
Bob Quillin: Over the last decade, observability has grown into a high-cost, high-complexity problem. Organizations generate vast amounts of telemetry data, but much of it is stored without ever being meaningfully used. In many environments, especially large-scale or hybrid systems, this is particularly problematic due to the scale of workloads and overreliance on proprietary observability tools.
At ControlTheory, we believe observability shouldn’t be a one-way data pipeline that overwhelms teams with noise. Instead, we introduce controllability. This is an intelligent approach that actively manages telemetry, ensuring organizations collect only the data they need, precisely when they need it. By adding real-time feedback loops, our platform dynamically adjusts observability parameters, eliminating unnecessary data collection and optimizing operational efficiency.
VMblog: ControlTheory describes �controllability’ as an evolution of observability. What does this mean in practical terms?
Quillin: Controllability means actively managing observability rather than passively collecting data. For teams across any infrastructure environment, this translates into three key benefits. Firstly, there’s cost control. Our platform detects telemetry spikes, reduces metric cardinality, and intelligently filters logs and traces, preventing runaway observability costs.
Secondly, the issue of operational control. We sharpen root cause analysis by increasing the signal-to-noise ratio, using smart sampling and telemetry meta-metrics. And finally, the benefit of adaptive control. With dynamic feedback loops, teams can auto-scale telemetry pipelines based on workload demand, ensuring observability remains cost-effective and relevant in real time.
VMblog: One of the biggest concerns is the skyrocketing cost of observability. How does ControlTheory help organizations optimize their spend without sacrificing visibility?
Quillin: Traditional observability solutions often charge based on telemetry volume from ingestion to indexing to retention, which means costs scale rapidly as data grows. ControlTheory takes a different approach. We decouple observability from volume-based pricing and instead focus on control-layer components like control planes and collectors.
By intelligently filtering, compressing, and routing telemetry, we reduce data ingestion costs without losing visibility. We also help avoid expensive vendor lock-in by leveraging open standards like OpenTelemetry. This ensures that organizations maintain flexibility while keeping costs predictable and manageable.
VMblog: Vendor lock-in is a major issue in observability. How does ControlTheory ensure flexibility while integrating with existing tools?
Quillin: One of our core principles is openness. Many organizations are locked into proprietary observability stacks that limit their ability to optimize costs and performance. ControlTheory is built on OpenTelemetry, enabling seamless integration with existing observability tools without requiring a rip-and-replace migration.
Our approach allows organizations to unify observability across different environments and platforms while maintaining full control over their data. By leveraging an open control plane, enterprises can manage their telemetry lifecycle flexibly and within their own data plane, ensuring they are not tied to any single vendor’s pricing model.
VMblog: You’ve talked about feedback loops as a key component of controllability. How do they work, and how do they improve observability?
Quillin: Traditional observability is largely static-data flows in one direction without any real-time optimization. Feedback loops change this by enabling observability to self-adjust based on operational needs.
For example, during peak usage, our adaptive telemetry pipelines can increase data collection for critical systems while reducing unnecessary logs elsewhere. This not only improves cost efficiency but also ensures observability remains agile and actionable, adjusting in real time to changes in demand or system behavior.
VMblog: AI is becoming a core part of modern IT operations. How does ControlTheory prepare organizations for AI-driven observability?
Quillin: AI is fundamentally reshaping how organizations handle observability, introducing new demands such as more data, better data quality, greater complexity, and a need for real-time adaptability.
ControlTheory is designed to work seamlessly with AI workflows by optimizing data collection for machine learning models. Our feedback loops support reinforcement learning, human-in-the-loop observability tuning, and AI-driven anomaly detection. This ensures that observability evolves in lockstep with AI advancements rather than becoming a bottleneck.
VMblog: Many enterprises have multiple observability solutions. How does ControlTheory help manage telemetry across these complex environments?
Quillin: Deciding what telemetry data goes where is both a strategic and tactical question that can be governed by a control plane that implements intelligent rules based on business reasons, cost, use case, and need. Many teams are either migrating from one observability system to another or consolidating their systems for efficiency. Policies can be set in place to selectively route, reroute, filter, and A/B test telemetry migration patterns to best meet these policies.
ControlTheory’s control plane unified controls of observability across these distributed environments. By applying a standardized observability governance layer, organizations can seamlessly manage telemetry across different observability solutions. The result is a more coherent and efficient observability strategy-regardless of where workloads reside.
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Bob Quillin is the CEO and Co-Founder of ControlTheory, an organization dedicated to transforming observability through the power of controllability.
