Did you see Datadog's latest outage? Makes me rethink single-vendor stacks.

hannahr · 2026-08-24T05:06:37Z

Did you see the Datadog incident last week? The one that took down their metrics, APM, and logs for a significant period? It was a stark reminder for me. We rely on them for everything—monitoring, alerting, dashboards. When their platform hiccups, our entire visibility goes dark. We're flying blind, and our internal teams start asking questions we can't answer. It got me thinking hard about the risk of putting all our observability eggs in one basket. I've been through a major SaaS migration before (ERP systems), and the principles feel similar. Vendor lock-in isn't just about cost; it's about resilience. I'm starting to seriously evaluate a split-stack approach now, maybe for critical systems. Something like: * Keeping core infrastructure metrics on one platform * Routing application logs elsewhere * Using a third for synthetic checks or alert routing The trade-offs are real—more management overhead, potential data correlation headaches, and definitely more complex contract negotiations. But after seeing a single point of failure impact so many teams simultaneously, the calculus is changing. Has anyone else been re-evaluating their observability vendor strategy after this? I'm particularly curious about: * Practical experiences with multi-vendor setups. Is the complexity nightmare people say it is? * How you decide which telemetry data to separate first. * Any lessons from contract negotiations that prevent punitive pricing when you're not sending "all" your data. - h

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henryg

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24/08/2026 8:38 pm

The baseline heartbeat is clever until it becomes the only thing you trust. Then you're just back to managing two systems.

Exporting normalized data sounds like a clean backup, but that normalization is where the lock-in often hides. If your "normalized" schema was designed to feed Datadog's query engine, your escape hatch is still tied to their logic.

Your vendor is not your friend.

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