Monitoring, logging, and observability
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On-prem workstations were reporting health by cron scripts that posted into chat. The replacement ships metrics from macOS and Linux machines into VictoriaMetrics. Planning docs looked at Grafana Alloy over Tailscale. That is not what shipped.
The first Langfuse design on EKS was full HA and about $3,400 a month at the load I expected. Scenarios ran from about $1.8K to $7.7K. I re-checked it before buying the hardware. The estimate was about 3.5× too high. The tier that shipped is about $1,000 a month, roughly 71% below the original number.
The first Langfuse write-up on this site was an ECS sketch from earlier in the year. What I actually built, in my current role, is Langfuse v3 on EKS. It is validated on a lean tier. Apps are still cutting over from the old Docker Compose box.
These are the seven things that cause real outages, plus how the keys survive the move.
Our team was building LLM-powered features — agents, chat interfaces, document analysis — and we had zero visibility into what the models were actually doing. No trace of which prompts were sent, what the latency was, how much each call cost, or whether the outputs were any good.
I evaluated hosted observability platforms, but data residency requirements meant we needed to keep everything in our own cloud account. So I self-hosted Langfuse — an open-source LLM observability platform — on AWS. Here is the architecture and what I learned.