#42 – Killing Observability Noise: How Grepr Reduces Data by Up to 90% and Rebuilds the Pipeline with Jad Naous

#44 – Tracing at Planet Scale: How Zalando Monitors 60 Million Spans Per Second with Heinrich Hartmann

Heinrich Hartmann joins Dash0’s Mirko Novakovic to share how Zalando runs their observability at an extraordinary scale. Heinrich explains why Zalando standardized on OpenTracing and OpenTelemetry across 3,500 microservices and 250 Kubernetes clusters, what it takes to process up to 60 million spans per second during Cyber Week, and how the team approaches sampling, cost control, and adaptive telemetry. They also discuss the future of observability in the age of AI and Heinrich’s new conference, SignalsConf Berlin, which will focus on reliability and AI.

#43 – Observability at the Proxy Level: How Linkerd Brings Visibility to Services, Identity, and AI Agents with William Morgan

Buoyant CEO and creator of Linkerd William Morgan joins Dash0’s Mirko Novakovic to explore what happens when you instrument traffic at the service mesh layer. Drawing on his experience at Twitter, William explains how Linkerd gives platform teams HTTP and gRPC-level visibility without touching application code, why retry budgets help prevent cascade failures, and how workload identity creates a stronger foundation for security and observability. They also discuss MCP traffic, AI agents in production, and how observability needs to be re-optimized for LLMs.

#42 – Killing Observability Noise: How Grepr Reduces Data by Up to 90% and Rebuilds the Pipeline with Jad Naous

Jad Naous, founder and CEO of Grepr, joins Dash0’s Mirko Novakovic to tackle one of observability’s biggest problems: too much data and not enough signal. Drawing on his experience at AppDynamics and Apache Druid, Jad explains why current architectures are fundamentally broken, and how Grepr compresses, aggregates and extracts signal directly in the stream, reducing data volume by up to 90% before it ever hits tools like Datadog. They dive into real-time pattern detection across logs and traces, why rule-based pipelines fall short, and how high-signal data is the foundation for any future AI SRE system.

#41 – Platform as a Product: Why Internal Platforms Fail (and How to Fix Them) with Abby Bangser

Abby Bangser, founding principal engineer at Syntasso and co-author of “Platform as a Product,” joins Kasper Borg Nissen to unpack why most internal platforms struggle, and what it means to treat them like products. They explore producer vs. consumer dynamics, why golden paths often fail, how to measure platform success, and the shift toward internal “platform marketplaces” that scale beyond a single team. The conversation also covers observability for vs. of the platform, and why AI is putting even more pressure on platforms to remove bottlenecks and make access to resources feel effortless. Links mentioned in this episode: Platform as a Product report: https://www.syntasso.io/platform-as-a-product-oreilly-report [https://www.syntasso.io/platform-as-a-product-oreilly-report]

#40 – Breaking the Observability Model: Pricing, AI SRE, and a Developer-First Mindset with Juraj Masar

Better Stack co-founder and CEO Juraj Masar joins Dash0’s Mirko Novakovic to challenge the fundamentals of modern observability, from cloud lock-in and pricing models to how platforms should be built in the age of AI and how we market them. They discuss why observability costs are fundamentally broken, how Better Stack combines cloud and ‘bare metal,’ and why small teams can outperform large engineering orgs. The conversation also explores eBPF as the new default for instrumentation, the shift toward AI SRE, and how Better Stack has paired a developer-first product with unconventional marketing, from generous free tiers to SEO-driven status pages to a massive YouTube presence.