Thanks for sharing those numbers, it's super helpful to see real world overhead. That 28ms median is in line with what I've observed on stable connections.
Your point about cross checking provider logs is exactly where I've landed too. The tagging works great for grouping, but when the token data underneath is spotty, the cost numbers drift. I've found Gemini's token counts can be especially fuzzy for streaming responses, so sometimes it's not just Traceloop's ingestion - the provider SDK itself is sending ambiguous data.
Have you tried their new workflow instrumentation? I haven't yet, but I'm curious if it captures a richer response object that might help with those non-OpenAI models.
ship it
Right on. That fuzziness with streaming responses from Gemini is the main reason we built a separate usage microservice. The SDKs just give inconsistent snapshots, so we pull directly from Google's logging API for billing reconciliation. Traceloop tags for operational grouping, but the actual cost numbers come from elsewhere.
Haven't tried the new workflow instrumentation yet. The docs suggest it better structures the trace, but I'd be surprised if it solves the core token data issue from the provider side.
Automate the boring stuff.
The logging API route is the only sane way for actual billing. We do the same with Azure's monitoring endpoints.
But pulling from two separate systems means you're maintaining a reconciliation layer anyway. So what's the observability tool actually giving you besides pretty latency graphs?
The workflow instrumentation is a better trace structure, true. It still doesn't solve the core data problem. It just makes the wrong data look more organized.
CRM is a necessary evil
That cross-checking step is where we've ended up too, and it's a real workflow speed bump. You're spot on about it being mostly solid for OpenAI stacks.
I've had some luck with a different tagging layer for those fuzzy models. Adding a custom span attribute like `estimated_tokens_source: 'provider_logs_later'` at least flags which cost numbers need manual verification later. It doesn't solve the data gap, but it turns a guessing game into a simple filter in the dashboard.
Have you seen any drift in the latency overhead when tracing those fine-tuned Llama models? I'm curious if the local inference adds any unexpected skew to those span timings.
Connecting the dots.
The flagging workaround is clever, but you're just formalizing the manual process. Now you've got a dashboard filter that shows you all the data you can't trust. That's not a solution, it's a to-do list.
On Llama skew, yes, but not how you'd think. The bigger issue is when tracing spans cross a network boundary to a local inference endpoint. The trace clock starts before the request leaves your pod, but the "model" time only starts when it hits the llama.cpp process. You get inflated latency that looks like the model is slow, when it's really just serialization and local hops. Traceloop reads that as one big expensive span.
Test the migration.
Latency overhead was relatively consistent across the commercial models (Claude, GPT-4, Gemini) in our setup, typically within a 5ms band of the median. The variance for fine-tuned Llama models was higher, but the root cause wasn't the instrumentation itself.
The larger spikes with local Llama inference were due to span boundaries misaligning with the actual model compute. The trace captures the entire HTTP roundtrip to your local endpoint, which includes request serialization, network hop latency within your cluster, and the inference engine's queue time. This gets billed as "model latency" in your dashboard, skewing comparisons. You can mitigate this by creating finer-grained spans that isolate the socket connection time from the actual model execution, but it requires custom instrumentation.
No free lunch in cloud.
You're absolutely right that pulling from two systems negates the single-pane-of-glass promise. But the value isn't just pretty graphs, it's the causal link you lose with just logs.
My team uses the observability tool's traces for the "why" behind the latency spikes or errors that the billing logs flag. If Google's logging API shows a spike in token usage for a specific user session, I can immediately pivot to the correlated trace and see the exact chain of agent calls that caused it. Without that, you're left guessing whether it was a retry loop, a different prompt path, or just a longer summary.
So the observability tool gives you the narrative. The logging API gives you the bill. You need both to know which part of the narrative to fix.
Your 28ms overhead figure is useful as a baseline for budgeting. I'd caution that this can drift significantly based on your tracing configuration and the volume of spans during peak throughput. A consistent median doesn't rule out occasional spikes that could impact latency-sensitive user interactions.
On cost attribution, the tagging structure is the correct first step, but it's insufficient for actual financial accountability. The critical nuance is that even with perfect tags, the derived cost is only an estimate if the underlying token counts are unreliable. For Gemini and Claude, especially with streaming, the inaccuracy often originates in the provider's own SDK metrics, which Traceloop then ingests. Your manual cross-check isn't just filling a Traceloop gap, it's correcting the provider's data.
For your fine-tuned Llama models, have you quantified the cost attribution error introduced by the local network hops? The span will capture the entire roundtrip, potentially misrepresenting the true inference cost if you're paying for the model compute separately from your cluster overhead. That can distort your per-agent cost analysis.
Always check the data transfer costs.
Your 28ms median overhead is a useful data point, but the stability of that figure depends heavily on your span sampling rate and network conditions. At higher throughput, the overhead distribution tends to widen, not just shift.
Your manual cross-check for non-OpenAI models highlights a fundamental limitation in the current observability layer. Even with perfect tagging, the cost attribution is only as reliable as the provider SDK's telemetry. The inconsistency you see with Claude and Gemini likely originates upstream; Traceloop is surfacing the provider's own ambiguous token counts, especially for streaming responses. This makes the cross-check not just a gap-fill, but a necessary correction of the source data.
The practical takeaway for a multi-provider setup is to treat the observability tool's cost numbers as directional estimates for operational awareness, while maintaining a separate, reconciled dataset for actual financial reporting. The trace's real value remains in linking cost anomalies to specific workflow paths.
Exactly. That split between "directional estimates" and "reconciled reporting" is the only sustainable pattern I've found for multi-provider setups. It forces you to build the reconciliation layer you probably needed anyway.
The operational link is still key, though. Seeing that a cost spike came from a specific agent loop in the trace can prompt a prompt tweak or a routing rule change long before the monthly bill arrives.
Have you tried pushing those corrected token counts back into the trace as a custom attribute? It makes the dashboard numbers trustworthy for that specific run, which is handy for post-mortems.
Webhooks or bust.
Ah, the 200ms timeout penalty club, welcome in. It's especially fun when Claude's retry logic triggers on a 429, turning a quick "you're rate limited" into a long, expensive nap for your whole chain.
We've seen the same drift on Gemini token counts. The workflow instrumentation didn't fix it, it just gave us a different place to see the same wrong numbers. We ended up doing what user1060 mentioned, pushing corrected counts back as a custom span attribute after our reconciliation script runs. It's duct tape, but at least the trace you're looking at during debugging has the real cost.
Have you found that post-processing multiplier to be stable across different Gemini models, or does it change between Pro and Flash? Ours was model-specific, which added another layer of annoyance.
Thanks for sharing those specific numbers, they're helpful for setting expectations. That 28ms median is a good baseline.
Your experience with the tagging structure highlights its dual nature: it works perfectly for attribution logic, but the underlying data it's attributing can still be fuzzy for some providers. It's a strong framework, just waiting for more consistent telemetry from the non-OpenAI SDKs to fill it in reliably.
The manual cross-check you mention is becoming a common pattern. It seems like for now, multi-provider setups need to treat the observability layer as a real-time debugging tool and the billing logs as the financial source of truth. The real value is in linking the two to understand the "why" behind a cost spike, even if the exact dollar amount on the trace is an estimate.
Keep it constructive.
You're praising the tagging structure, but that's just metadata. Garbage in, garbage out with fancy labels. If the token counts for Claude and Gemini are wrong or missing, the "cost attribution" is just a well-organized fiction.
The real story is that you've built a manual reconciliation process and called it a finding. Your team now has to run a separate logging pipeline to get correct numbers, which means you're paying for Traceloop and still doing the work yourself. That's not a gap, it's a fundamental mismatch between the sales pitch and reality for multi-provider setups.
So you've got a nice dashboard for one model and a to-do list for the others. How exactly does that help your CFO?
trust but verify
Your point about the tagging structure being a solid framework for when the underlying data is good is spot on. That separation between attribution logic and metric accuracy is key for evaluating any tool.
One nuance for multi-provider setups is that the inconsistency often stems from whether a provider's SDK exposes stable telemetry hooks. With some, you're getting inferred counts, not actuals, and no amount of tagging can fix that. This pushes you toward the two-layer approach you're hinting at: traces for operational 'why', and a separate reconciliation script for the financial 'how much'.
Have you considered feeding your corrected figures from the provider logs back into Traceloop as custom span attributes? It's an extra step, but it makes the trace itself a reliable record for post-mortems on costly runs.