Observability Summit North America 2026

This talk presents a practical methodology for migrating a large proprietary observability platform to an OpenTelemetry-native architecture, using a GenAI-assisted workflow paired with a robust spec-driven strategy. Faced with hundreds of custom Java-based sensors, the engineering team designed a spec-driven conversion process that leverages GenAI to extract specifications, generate unit tests, and assist in implementing Go-based OpenTelemetry receivers. Each stage incorporates human review and test feedback loops to address the reliability limitations of GenAI and ensure functional correctness.

Additionally, a data-driven feasibility evaluation was conducted prior to large-scale conversion, where defined task types were benchmarked with and without GenAI to quantify effort savings and highlight where GenAI provides the greatest value.

Attendees will learn a reproducible workflow for large-scale migrations from proprietary to OpenTelemetry, how to pair GenAI with automated testing to manage risk, and insights on where GenAI accelerates real-world engineering tasks without compromising quality.

Enterprises often struggle with the "black box" nature of proprietary APM tools and the high cost of distributed tracing at scale. In this session, we will demonstrate how Comcast tackled this challenge by migrating 350 critical applications from AppDynamics to a cloud-native OpenTelemetry (OTel) stack, achieving a truly unified end-to-end observability experience.

We will pull back the curtain on the architecture that powers this migration. Specifically, we will show how we leveraged the OpenTelemetry Collector to generate Request, Error, and Duration (R.E.D.) metrics from trace data using the SpanMetrics connector. A key highlight will be our unique deployment of Conduit, which serves as a resilient transport layer to ensure data integrity and effective load balancing in a high-volume environment.

Attendees will leave with a blueprint for breaking free from APM vendor lock-in. To help the community fast-track this transition, we will also be sharing and walking through our reusable, battle-tested Grafana dashboards that can be leveraged by any enterprise.

Operating a SaaS platform presents the same observability problems as any other enterprise, but due to the scale and tenancy, we introduce a huge multiplier on the observability signals, having an effect on cost and effectiveness.

This session dives into the techniques Collibra used to tame these problems and how to maintain clarity when infrastructure spans virtual machines, modern Kubernetes clusters, and a complex mix of single- and multi-tenant architectures. Without the right context, telemetry data becomes a noisy, indistinguishable flood.

We will dive into the architectural decision to leverage the C4 system model, ensuring every piece of telemetry carries the vital context of what it belongs to and where it sits in the hierarchy. Enabling us to gain insights into both signal attribution and allowing virtual chargebacks. The presentation details the implementation of a pipeline using custom-built OpenTelemetry collectors designed to handle the data and enrich it before sending it to the appropriate backends.

This session will give you practical insights on the challenges SaaS platforms have, but the techniques that are used to tame them can be applied everywhere.

What happens when a system degrades under high load while all internal metrics remain “green”? At hyperscale, supporting on-demand services across Southeast Asia’s most populous countries, a team observed up to a 7% drop in message delivery. The root cause was not application code, messaging brokers, or load balancers, but a hidden limitation deep within a cloud provider’s firewall.

This war-story session presents a forensic investigation into a managed cloud load balancer and its interaction with connection-tracking tables. The talk walks through the production cutover that triggered the issue and the targeted load testing that ultimately isolated the failure to cloud infrastructure behavior invisible to standard monitoring.

Beyond root cause analysis, the session focuses on outcomes: how sustained, evidence-based debugging led the cloud provider to acknowledge the issue—initially labeled a “limitation”—and introduce a new observability metric, firewall/connections_tracked. Attendees will leave with a practical framework for debugging black-box cloud failures and identifying the node-level metrics needed to detect silent network drops before they impact users.

Existing cloud and Kubernetes cost management tools struggle to track expenses at a granular level, leaving engineers unable to answer critical questions like: How much is one specific customer costing us in DynamoDB usage? Or, which system component is consuming the most of our Kafka cluster?1


This lightning talk demonstrates how to leverage existing observability frameworks to gain detailed, low-level cost insights. Attendees will learn basic techniques to instrument standard metrics—such as component name, customer ID, and team—with custom labels for fine-grained cost allocation.1


This session includes a practical case study from Red Canary, who has used this exact methodology for over five years to transform their tactical decision-making and better manage cloud spend. By treating cost allocation as an observability problem, engineers can provide the finance team with the deep data required for effective resource management.1


Attendees will leave with an actionable plan for implementing a metrics-based cost tracking system (likely with the tooling you already have), independent of high-level cloud billing tools, to drive significant operational efficiency.

Unified observability has lately been regarded as the holy grail by some. One platform, universal observability, for everything. Usually, this would be the default, but when you are at a 30-year-old non-technical enterprise, dealing with a mixture of legacy and modern systems, it's a whole different story.

A consequence of legacy decisions, in some cases, results in having multiple observability platforms for different teams within the company, adding overhead, cost, noise, and audit complexity. This was the case at Emaar, a property developer based in Dubai, until the PE team took on the exciting project of unifying all observability into one platform. This included applications, infrastructure, network, and security. The complexity arises not just from the different data sources, but rather from the number and nature of the deployment sites. This included sites across 10 countries consisting of data centers, hotels, malls, shops, etc.

This talk will outline the experience of implementing a unified observability platform consisting of thousands of network devices, machines, and application workloads using open-source technologies that resulted in 6 figures of cost savings.