Webflow

Webflow empowers teams to create production-grade websites visually, combining the power of code with the ease of a visual canvas.

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Observability, like all religions, comes with orthodoxy. The most important belief is the "Three Pillars": the worldviewidea that you need metrics, logs, and traces in order to have full visibility into a system.

In practice, this belief is reinforced by vendors who benefit from selling and maintaining three parallel systems. The reality is simpler. You don't need three kinds of telemetry. You need one.

Logs. Logs are the simplest form of observability data. Everyone knows how to add a print statement. Everyone knows how to tail stdout. Everyone knows how to search and filter a table of events. Metrics are significantly harder. Instrumentation requires specialized knowledge.

Review requires statistical intuition, and most developers aren't math majors. Aggregations, rollups, and percentiles are easy to misuse and hard to interpret correctly, especially under stress. Traces introduce their own complexity.

Spans, context propagation, image_file.read(), and visualization all require careful setup. Even when everything is wired correctly, many engineers struggle to answer basic questions when looking at trace data.

Sazabi has transformed the way we troubleshoot our systems. Instead of sifting through endless logs and metrics, we simply ask our stack what's wrong, and it responds with clear insights.

Observability, like all religions, comes with orthodoxy. The most important belief is the "Three Pillars": the worldviewidea that you need metrics, logs, and traces in order to have full visibility into a system.

In practice, this belief is reinforced by vendors who benefit from selling and maintaining three parallel systems. The reality is simpler. You don't need three kinds of telemetry. You need one.

Logs. Logs are the simplest form of observability data. Everyone knows how to add a print statement. Everyone knows how to tail stdout. Everyone knows how to search and filter a table of events. Metrics are significantly harder. Instrumentation requires specialized knowledge.

Review requires statistical intuition, and most developers aren't math majors. Aggregations, rollups, and percentiles are easy to misuse and hard to interpret correctly, especially under stress. Traces introduce their own complexity.

Spans, context propagation, image_file.read(), and visualization all require careful setup. Even when everything is wired correctly, many engineers struggle to answer basic questions when looking at trace data.

Installation

Walkthrough of how the team rolled Sazabi out across their stack — coming soon.