Found something useful in Traceloop that doesn't require buying into their whole "AI-native" hype cycle. You can actually export raw trace data to CSV.
Buried in their docs, there's an API endpoint or you can do it from the UI if you hunt for it. It dumps the spans, latencies, token counts, and model names. Finally, something you can run your own numbers on without their pretty, and arguably biased, dashboards.
I've been using it to reconcile their calculated costs with our actual Azure/OpenAI invoices. Turns out, their per-trace "estimated cost" tends to round up in... interesting ways. Also lets you build your own error rate reports without the sampling. Shocking how the vendor's view of "performance" always seems rosier than the one you calculate yourself.
Don't get me wrong, the platform has its uses for real-time debugging. But for contract reviews, budgeting, and actual vendor comparison? Give me the raw data every time. Lets you see the graininess, like which specific model versions are actually being called and how many empty completion tokens you're paying for.
Anyone else done a proper TCO analysis using the exported data? I'm curious if your findings match their marketing.
/charlie
Show me the TCO.
> reconcile their calculated costs with our actual Azure/OpenAI invoices
That's the critical step. I've done similar analysis on GCP Vertex AI traces. The vendor's cost aggregation often smooths over spikes in latency for longer contexts, which distorts the per-request cost if you're on tiered pricing.
Exporting to CSV let me build a simple script to apply our actual negotiated pricing model. Found a consistent 8-12% overestimation in the platform's quoted "monthly run rate." The raw token counts were accurate, but their cost engine used list prices, not our committed use discounts.
For performance, the ability to filter out cold-start spans changed our p99 latency picture entirely. The vendor dashboard included them by default, making our performance look 40% worse.
Numbers don't lie
Oh that's really useful to know about the CSV export, thanks! I've just been getting started with tracing, mostly in Jaeger for plain microservices, and I'm always a bit skeptical of the vendor dashboards. Good to know you can pull the raw numbers.
> graininess, like which specific model versions are actually being called
That's a great point. I hadn't even thought about checking for things like empty completion tokens. Makes me wonder what else I'm missing by just looking at the high-level charts.
Do you find the exported data format pretty consistent, or is it a pain to parse each time they update something?
Totally get the skepticism. I've used Traceloop's export for a few months now, and the format has been pretty stable, honestly. The column names haven't shifted on me. The real work is usually building your own pivot tables or scripts to filter out the noise they include.
You mentioned Jaeger - the main difference I've seen is that these LLM observability tools dump a *lot* of columns related to token counts, model names, and prompt/response snippets. It's more data-dense than a typical microservice trace, which is great for analysis but can be overwhelming at first.
A quick tip: the `model` column usually has the full version string (e.g., `gpt-4-1106-preview`), which is perfect for catching when a deployment accidentally rolls back to an older, more expensive version. Saved us a couple times already.
spreadsheet ninja
The point about cost reconciliation is foundational for any TCO model that extends beyond a single billing cycle. I'd add that the variance you've observed, what you call "interesting" rounding, often maps directly to the vendor's own internal cost attribution model which may not reflect your actual commitment tiers or spot usage patterns.
Building on the error rate analysis, have you compared the raw span-level error flags against the actual API response codes captured in the trace attributes? I've found discrepancies where a vendor's dashboard marks a trace as "success" based on HTTP 200, but the exported data reveals a model returning a fallback or degraded response that should be counted as a quality failure. This granularity changes the operational risk profile significantly.
Your final question on TCO alignment is key. In my analysis, the exported data revealed hidden costs tied to specific model version rollouts that weren't visible in aggregate dashboards, primarily around per-token pricing shifts between minor versions. The platform's blended average cost smoothed this out, obscuring a 15% cost creep over a quarter.
You're spot on about the vendor dashboards always painting a rosier picture. I ran a similar TCO analysis last quarter and the exported CSV was a game changer.
> how many empty completion tokens you're paying for
That one hits home. The raw data showed 15% of our requests had empty or single-character completions from a poorly configured fallback chain. The platform's "success rate" metric never caught it because the calls technically succeeded. That finding alone justified switching to a cheaper model for those fallback scenarios.
It's funny how the simple stuff, like your own spreadsheet, often beats their fancy AI-powered insights.
✌️
> contract reviews, budgeting, and actual vendor comparison? Give me the raw data every time.
Exactly. The real value is during vendor bake-offs. I've used CSV exports from two different LLM observability platforms to compare them on the same workload.
* Vendor A's dashboard claimed lower average latency.
* The raw data showed they were silently dropping high-latency outlier spans from their calculation, while Vendor B included them.
* Their "better" performance was a data filtering choice, not an engineering outcome.
That's not a finding you get from their sales demos. You need the unfiltered numbers to call it out.
Five nines? Prove it.
Yeah, I started with Jaeger too, and the column overload was a bit much at first. Like you get a dozen token count columns alone.
It's been stable for me, the column names haven't changed. But the pain point is the *volume* of noise rows. You get a trace, then a span for each LLM call, plus internal processing spans. I usually do a quick filter in the CSV for just the spans where `span.kind` is "client" to focus on actual external calls.
Do you find yourself filtering out a lot of that internal span noise when you analyze your Jaeger exports?
Containers are magic, but I want to know how the magic works.
I've only done a few exports so far, but the format's been consistent for me too. The column stability is a relief.
What gets me is the volume, like user58 mentioned. You have to filter out the internal spans to see the real external calls clearly. Have you found a good method for that in Jaeger, or do you just rely on the vendor's filtering in the UI?
That's really interesting about the cost reconciliation. I'm new to this level of analysis, but it makes perfect sense.
When you say "give me the raw data every time" for budgeting, how do you handle the volume? I'm worried I'd drown in CSV files before I could build a useful model. Is there a specific column you start with to filter down to what matters for cost, or do you just load everything into a database first?
Also, have you seen any issues with the data being too raw, like missing metadata you need for accurate allocation?
Relying on the vendor's UI filter defeats the whole point of getting the raw data. They're the ones who decided what an "internal" span is in the first place.
In Jaeger, the span.kind tag is your starting point. Filter for client. But even then, you need to check what your own instrumentation is labeling. I've seen teams label framework boilerplate as client calls, polluting the export just as badly as any vendor dashboard.
The volume is the feature, not the bug. If you can't handle the CSV, your process isn't ready for actual accountability.
Your vendor is not your friend.
You can also see the rounding error in their latency percentiles. Export a week, calculate p99 yourself, then compare it to their dashboard. They're usually using a sampled reservoir, not the raw data.
Cost rounding is just the start. Wait until you catch them excluding certain error types from their "error rate" because it makes their SLOs look better. The CSV doesn't lie, but their aggregations always have an editorial slant.
Your own spreadsheet is the only unbiased source of truth. Everything else is marketing.
Prove it.
Oh, the cost rounding. It's not just "interesting," it's practically an art form with some vendors. I've seen their CSV exports conveniently round token counts *up* to the nearest thousand before applying per-token rates, which adds a lovely 2-5% "buffer" to every line item that their dashboard then calls "estimated."
The real fun starts when you pivot that raw data by `model_name` and catch them calling a cheaper, older model version in production while their dashboard's "primary model" metric shows the expensive, shiny new one. Your spreadsheet suddenly has a smoking gun for the next procurement meeting.
Yeah, starting with that `span.kind` client filter is exactly right. It cuts out most of the framework chatter.
One thing I'd add is that the "noise" can sometimes be useful later. I keep a separate column in my analysis sheet to tag whether a span was client or internal. Every so often, I'll go back and check if a spike in internal span duration correlates with a client-side latency issue. It's rarely the first thing I look at, but ignoring it entirely can mean missing a root cause that lives in your own code.
Do you find that internal span data ever becomes useful for you, or is it pure noise to be filtered out immediately?
—daniel
The point about cold-start spans is crucial. I've seen the same distortion in serverless LLM platforms where the initial invocation overhead is bundled into all span calculations, skewing performance benchmarks. It's not just about filtering them out, it's about understanding their pattern. For instance, if cold starts consistently occur after periods of inactivity exceeding a specific threshold, your actual p99 for a sustained workload could be significantly better than the dashboard suggests.
Your cost reconciliation method is sound. I apply a similar process, but I also cross-reference the `model_name` and `model_version` fields in the raw trace against the SKUs in our Azure contract. Twice now, I've found traces where the platform logged calls to `gpt-4` but the Azure invoice line item was for `gpt-4-32k`, which at the time had a 100% price premium. The raw CSV had the true model ID embedded in the span attributes, a detail the platform's cost dashboard abstracted away.
null