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The latest News and Information on Observabilty for complex systems and related technologies.

How SAP achieved world-class uptime through modern observability

SAP Customer Experience (CX) has undergone a remarkable transformation over recent years, evolving from fragmented monitoring to a scalable, automated observability powerhouse. In a recent fireside chat, Martin Norato Auer, SAP CX’s VP of Observability, shed light on the strategies, practices, and measurable impacts behind SAP’s SLA, uptime, and responsiveness achievements.

Architecting for Value: A Playbook for Sustainable Observability

You’ve built something amazing. Your services are scaling, your users are happy, and your team is shipping code like never before. Then the cloud bill arrives, and one line item makes your eyes water: observability. That Datadog invoice feels less like a utility bill and more like a ransom note. It’s a modern engineering paradox. The tools that give you sight into your complex systems are the same ones that can blind you with runaway costs.

Ship Confluent Cloud Observability in Minutes

You're running Kafka on Confluent Cloud. You care about lag, throughput, retries, and replication. But where do you see those metrics? Confluent gives you metrics, sure, but not all in one place. Some live behind a metrics API, others behind Connect clusters or Schema Registries. You either wire them manually or give up. What if you could stream those metrics to a platform built for high-frequency, high-cardinality time series, and do it in minutes?

How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines

Platform teams are struggling with observability noise, bloated storage costs, and lack of clarity during incidents. Most teams capture everything all the time, leading to expensive, overwhelming, and often unnecessary data volumes. In Telemetry for Modern Apps, Mezmo teamed up with Checkly to demonstrate how synthetic monitoring triggers and responsive telemetry pipelines can help reduce costs while maintaining the context needed during incidents.
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Streamlining multi-cloud complexity with unified observability

A wave of businesses are embracing multi-cloud strategies to gain flexibility and scalability. By combining on-premises infrastructure, private clouds, and public platforms like AWS, Azure, and Google Cloud Platform (GCP), IT teams can experiment, deploy, transform, and improve their IT applications significantly. On the down side, this modern IT approach of employing multiple clouds (in both public and private forms) also brings significant complexity, making it challenging to monitor systems, control costs, and secure environments. There are just too many threads to track and tie together to ensure a taut IT fabric.

Will AI Speed Development in Your Legacy App?

Some people can get an AI assistant to write a day’s worth of useful code in ten minutes. Others among us can only watch it crank out hundreds of lines of crap that never works. What’s the difference? There are some skills specific to AI development. There are also properties of the codebase we’re working in that make it amenable to AI assistance. Most AI demos use projects created from scratch with AI in mind—cute.

I built an MCP Server for Observability. This is my Unhyped Take

Recently, I read a blog titled “It’s The End Of Observability As We Know It (And I Feel Fine)”, which discussed MCP servers in observability and how these systems would potentially be the “end of observability”. As someone who has spun up an MCP server for an observability backend and as someone who has been in the space for a while, I certainly do not think so.