Slow SQL queries degrade UX and reliability. This guide shows how to distill OpenTelemetry DB spans into actionable metrics: build span-derived slow-query dashboards, rank queries by traffic impact, and detect regressions with anomaly baselines, so you fix what matters first. Hands-on lab included.
Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.
Alerts are supposed to start an investigation. Too often, they start translation: what is the system doing right now? That translation slows containment, splinters context, and stretches customer impact.
Asynchronous pipelines sit at the core of most modern systems. Message brokers accept traffic, consumers process it in the background, and downstream services depend on the results. When these systems fail, the failure rarely shows up where it starts.
Originally published to the Slight Reliability Podcast.
Causely’s expanded Datadog integration turns Datadog APM signals into system-level causal intelligence, helping teams understand how issues propagate across services and pinpoint true root cause.
How Causely uses FluxCD and GitOps to ship weekly on Kubernetes, keep clusters in sync, and wire up OpenTelemetry and Causely in a hands-on lab you can copy.
Gartner recognized Causely for maintaining a live causality graph and using continuous inference to identify the underlying driver behind changes in golden signals as they emerge, even when failures cascade across multiple services.
In a 50 to 100+ microservice environment with dense service-to-service dependencies, even small regressions can cascade silently. And slowing down isn’t an option. Leadership needs faster delivery and fewer incidents. This is why we built Reliability Delta.
Originally published as a livestream to e-After Work.
Originally posted to Intellyx by Jason English.
Originally posted as a livestream from OllyGarden.
AI
Originally posted to TFIR by Monika Chauhan. Causely’s Severin Neumann explains how causal reasoning, MCP, and AI-driven automation are transforming SRE workflows and Kubernetes reliability.
DevOps & SRE
Originally posted to Techstrong.tv. Learn how Causely integrates reliability engineering into product development, tackling challenges in cloud-native applications.
Blog
With community-standard instrumentation and the OTel Collector, your metrics, logs, and traces are no longer trapped in a walled garden. Originally posted to the ClickHouse blog.
coverage
Originally posted to International Business Times by David Thompson.
Causality
Learn why causal inference is the missing piece in AI-driven observability, and how Causely is the only AI SRE platform that uses causal reasoning to pinpoint where, what, and why application and system related issues occur.
coverage
Originally posted to Cloud Native Now by Mike Vizard.
coverage
Reposted from its original publication on TechTimes by Carl Williams
Causely product
Causely announced the launch of the Causely MCP Server that seamlessly integrates into any MCP-compatible IDE and enables developers to automatically diagnose, understand, and remediate complex issues within Kubernetes and application code using natural language prompts.
Blog
The Causely MCP Server brings our Causal Reasoning Engine directly into the IDE so engineers can understand why incidents happen and apply the right fix at the right layer, whether that’s runtime, configuration, or code.
Press Release
Causely now leverages Google’s Gemini models to enhance how users interact with its Causal Reasoning Engine.
Blog
Gemini’s ability to interpret natural language, generate structured code, and summarize technical context complements Causely’s deterministic causal inference engine, turning complex telemetry into clear and reliable insights.
AI
Modern CTO Podcast's Joel Beasley sits down with Causely CEO Yotam Yemini to dive deep into the world of AI Site Reliability Engineering.