Originally published on NextFuture On May 2, 2026, two analyses of the LLM observability category dropped within four hours of each other — and both made the same point: eight tools claim identical keywords (tracing, observability, logging, cost tracking) but instrument your stack at completely different layers. If you picked yours from a feature comparison table, there's a reasonable chance it's the wrong architectural fit for your workload. What changed Four distinct tool architectures are now in production : SDK-based tracers (Langfuse, Phoenix), reverse-proxy loggers (Helicone), evals platforms with tracing bolt-ons, and enterprise ML monitors that added LLM support last year (Datadog LLM Observability, Arize). They all pass the same marketing checklist but instrument at different points in your request path. OpenTelemetry's gen_ai.* semantic conventions reached stable status , but they only standardize token counts and latency — not output quality, prompt version, or agent-step attribution.…