Most of these "AI observability" tools are just repackaged logging dashboards. You're paying a premium for someone else to run Grafana.
What do you actually get?
* A timeline of your LLM calls (latency, cost).
* Basic metrics on token usage and errors.
* Sometimes, a playback of the prompt/response.
Why not just build this yourself?
* OpenTelemetry already has semantic conventions for LLM spans.
* Export traces to Jaeger/Tempo, metrics to Prometheus.
* Build one dashboard in Grafana. Done.
The real value they claim is in "anomaly detection" and "cost attribution." But:
* Anomaly detection is usually simple threshold alerts you can set in Prometheus.
* Cost attribution is just tagging spans with a user ID or project ID—basic tracing.
You're often just paying for:
* Their pre-built UI.
* Them to host the data.
* The buzzword "AI-native."
Before you sign up, ask what their tool does that you can't get for free with:
* OpenTelemetry SDK
* A self-hosted observability stack
* Some simple scripts to calculate costs per trace
linux is free
You're absolutely right that the core telemetry is commoditized, but you're underselling the attribution problem. Tagging spans with a user ID is tracing 101. The messy part is tying an LLM call that costs $0.03 to a downstream conversion that generated $50 in revenue six weeks later, especially when it's one call in a chain across multiple services.
That's where these platforms either justify their fee or reveal themselves as glossy dashboards. Can their cost attribution model handle multi-touch, time-decayed attribution across hybrid data pipelines? Or is it just a direct, last-touch allocation slapped onto the LLM span? Ask for their data model schema. If they can't show you how to attribute a GPT-4-Vision call to a qualified sales opportunity sourced from a organic social campaign, they're selling Grafana in a trench coat.
The anomaly detection is similarly shallow if it's just token count spikes. The valuable signal is detecting a drift in the *quality* of responses, not just the quantity. Is the model suddenly generating more refusals or off-brand language? That requires semantic analysis they probably aren't doing.
Attribution is a lie, but we need the lie.
That's a really helpful breakdown, thanks. I'm just starting out with this stuff and was feeling overwhelmed by all the vendor claims.
You mentioned setting simple threshold alerts in Prometheus. I get that for latency, but how do you handle the anomaly detection they talk about for something like response quality or hallucination rates? Is that just another metric you'd track, or is there a trick to it?