I'm neck-deep in serverless apps that are starting to call multiple LLMs (Bedrock, some OpenAI via Lambda). My immediate pain points are latency spikes and not knowing which model call is blowing up the cost. The observability space is now flooded with tools claiming to solve this.
I'm practical. I need to see numbers. But looking at vendor websites, everyone just shows fancy dashboards. As someone who's used to comparing Lambda cold start times or ECS task costs, what are the actual, tangible metrics I should be testing for?
My starting list is:
* **Latency breakdown:** Not just total time, but time to first token, generation time, and network overhead per model call.
* **Cost attribution:** Can it show me cost per user session, per API route, or per specific model? My billing is a mess right now.
* **Trace usability:** When something times out, can I follow the chain of LLM calls, tools, and retrievals without clicking through 10 screens?
What am I missing? For those who've been through this, what were the key evaluation criteria that actually mattered for a production system? I'm less interested in pre-built alerts and more in granular data I can query myself.
cb
Totally agree on needing granular data over just dashboards. Your list is solid, but I'd add one more thing: error rate tracking per model. I've seen cases where a specific provider's API starts failing silently in a way that doesn't trigger a timeout but just returns garbage, and that can wreck a user session. A tool should flag that model's call error rate separately.
Also, for cost attribution, can these tools actually handle variable pricing? Like if OpenAI drops their prices next month, does the tool's calculation update automatically, or is it locked to a static rate you have to manage yourself?
Sorry for the basic question - how are you testing these tools right now? Are you just using free trials side by side?
Still learning.
Your list is spot on. The trace usability point is huge - I've seen tools where the "trace" is just a timeline bar with no way to see the actual request/response payloads that caused the delay. If you can't see the specific prompts and completions in the chain, you're still guessing.
I'd add one more to test: data freshness. When you're debugging a live issue, can you see metrics and traces from 30 seconds ago, or is there a 15-minute processing delay? That latency in the observability tool itself kills its value for real-time triage.
How are you planning to generate load for testing? Mock calls might not reveal the aggregation bottlenecks.
data over opinions
Right, data freshness is a real killer. I've had dashboards that show "normal" while alarms are firing.
For load testing, I don't mock. I point the tool at a staging env and run actual user session replays with real model calls, but at 2-3x normal concurrency. That's the only way to see if aggregation or sampling breaks under load.
The 15-minute delay you mentioned? That's useless. If I can't see the trace for a user-reported error within a minute, I'm already on a war room call without it.
Ship fast, review slower
Your core list hits the technical metrics well. I'd add evaluating the data export and query layer itself. Can you write a custom query to correlate, for instance, high latency with specific input characteristics or user segments? Many tools give you a pre-built dashboard but lock you out of the raw data.
If the tool doesn't expose an API or a SQL-like interface to get at those granular metrics you listed, you're just viewing their interpretation, not doing your own analysis. That's a hard limit for practical debugging.
Also, test their sampling strategy under load. Some tools drop spans to manage volume, which completely breaks cost attribution and makes latency percentiles useless. Ask them what guarantees they offer on trace completeness.
Data is the source of truth.