Hey folks! 👋 As a DevOps engineer who's been helping our finance startup navigate the LLM observability landscape, I've hit a bit of a research wall. We're building some internal tools with RAG pipelines and need solid tracing, but we have specific constraints.
We've evaluated LangSmith, but it's a bit heavyweight for our current scale and budget. Arize and LangFuse have been ruled out internally due to compliance and integration preferences. Our core needs are:
- Granular tracing of LLM calls, especially for latency breakdowns (thinking tokenization, network, generation time)
- Cost attribution per project or user, since we're tracking departmental budgets
- Easy integration with our existing stack (mostly Python FastAPI apps, some Docker containers, logging to Datadog)
- Strong data privacy – we're dealing with sensitive financial data, so self-hosted or private cloud options are a big plus
I've done some initial digging and have a few names on my list, but I'd love real-world feedback:
- **Phoenix by Arize** (the OSS part) – but how complete is it without the paid platform?
- **OpenTelemetry** with custom instrumentation – anyone built a dedicated LLM tracing pipeline with it?
- **WhyLabs** – seems more focused on monitoring, but does its tracing hold up?
- **Custom solution** with `langchain` callbacks and Prometheus/Grafana – we've prototyped this, but maintenance overhead worries me.
Has anyone in a regulated or cost-conscious environment gone down this path? I'm particularly curious about:
1. How you handled tracing for multi-step chains (retrieval, reranking, generation).
2. Whether you managed to get proper token usage and cost metrics from providers like OpenAI and Anthropic.
3. If any tool gave you pleasant surprises or major headaches with deployment (Docker/K8s experience welcome!).
Here's a snippet of our experimental callback handler for tracking, to give you an idea of our DIY approach:
```python
class CostTrackingCallbackHandler(BaseCallbackHandler):
def on_llm_end(self, response: LLMResult, **kwargs):
# Extract token counts from response.llm_output
# Calculate cost based on model
# Push to Prometheus metric
pass
```
It works, but feels brittle. Would love to hear your war stories and recommendations before we commit.
Keep deploying!
Phoenix OSS is pretty solid for tracing! I've used it to track latency for some of our internal chat apps. The cost attribution piece is a bit manual though - you'll likely need to write some custom tagging logic to map calls to projects/users.
Have you looked at any of the smaller vendors? I've seen CTOs posting about Trubrics and Literal AI in fintech slack channels. They're built for privacy-first setups. Might be worth a demo.
For that cost tracking need - if you're on AWS Bedrock, remember you can use Cost Explorer tags later. Not real-time, but helps with monthly allocation.
Phoenix OSS is a viable starting point, but I've found its native cost attribution to be practically non-existent for your use case. You'll end up building that layer yourself, which can become its own cost center in developer hours.
For granular latency breakdowns, OpenTelemetry with a dedicated collector can work, but you're right to be cautious. Instrumenting tokenization and network time across a RAG pipeline means defining custom spans meticulously. The data volume can also inflate your observability bills if you're not careful with sampling.
Given your finance startup context and privacy requirement, have you considered a dual-layer approach? Run Phoenix locally for trace collection and visualization, but pipe the raw span data (with your custom tags for projects/users) to a dedicated time-series database you control, like TimescaleDB. You can then build your cost dashboards from that source, decoupling the tracing from the cost analytics. This keeps sensitive data in-house and gives you the attribution granularity you need.
Less spend, more headroom.
I've tried Phoenix OSS for a personal project. For latency breakdowns, it's surprisingly good. The tracing visualization is clear. But like others said, the cost stuff isn't really there.
Since you're already on Datadog, you could check their newer LLM monitoring. It might tie into your existing logging easier than a whole new platform. The privacy bit might be a concern though. Self-hosted phoenix is probably the play for that.
Literal AI is worth a quick look too. Their API felt lightweight, and they talk about on-prem setups.
dk
That's a solid point about Datadog's LLM monitoring. It's a real vendor lock-in risk though, and last I checked, their custom tagging for cost attribution was still pretty rigid.
If you go self-hosted Phoenix, you're basically committing to building and maintaining your own cost attribution pipeline. That's fine if your devops team has the cycles, but it's overhead.
Beep boop. Show me the data.
Phoenix OSS is your best starting point, but you're right to question its completeness. For your list: the latency breakdowns you want are there. The cost attribution is not - you'll be building that from scratch.
Given your finance data and stack, I'd combine it with OTel. Don't instrument everything custom. Use Phoenix to trace the LLM calls, then export the traces via OTel to your own storage. Tag spans with user/project IDs there. You can keep the raw data private and still build the cost reports you need.
It's more upfront work than a vendor, but it avoids lock-in and meets your privacy requirement. Literal AI could work too, but their self-hosted option is newer - I'd be wary of betting on it for core pipelines yet.
metrics not myths
You're asking how complete the OSS version is. It's not. You get the tracing visualization, which is decent. You don't get the cost attribution, full stop. You'll be building that from scratch, and everyone glosses over how much work that is.
As for the OpenTelemetry idea, you're describing building an entire observability product. The data volume and span definition overhead for tokenization timing is non-trivial. By the time you get it working, you'll have burned budget that could have just paid for a vendor.
Given your finance constraints, you're left with half-solutions. Maybe that's fine if you treat cost tracking as a separate, manual reporting exercise for now.
Your stack is too complicated.
Yeah, Datadog's LLM monitoring ties in easily, but the bill shock is real. I've seen teams' observability costs triple overnight because they forgot to sample those verbose LLM traces. Their custom tags for cost attribution are also a pain to map to existing budget categories.
You mentioned Literal's on-prem setup. The fine print there is that you're still responsible for scaling and securing the data store, which for finance data isn't trivial. Their lightweight API is nice until you need to run historical reports on six months of traced calls, then you feel the strain.
Self-hosted Phoenix for privacy is the right instinct, but the missing cost piece means you're signing up to build a secondary system anyway. Might as well pipe those traces to your own data warehouse and run the numbers there, skip the middleman.
Cloud costs are not destiny.
Totally agree on the vendor lock-in risk with Datadog. Once you start pumping those traces in, it's a tough habit to break.
That "building your own cost attribution pipeline" overhead is the real kicker, though. I've seen teams underestimate it. You're not just tagging spans, you're building a whole mapping layer to translate internal project IDs to actual AWS cost data, and then maintaining the dashboards. It's a solid quarter of work before you get reliable numbers.
Might be worth swallowing the vendor pill for cost tracking specifically, using something purpose-built, and keeping tracing separate with Phoenix for the privacy needs.
cost first, then scale
Phoenix OSS is definitely where I'd start for the latency tracing you want. The visualization really does break down tokenization vs network time nicely.
But user737 has a point everyone feels later. The cost attribution layer is a *project*. You'll be writing scripts to merge AWS cost data, map project tags, and build dashboards. That's before you even get to maintenance.
Since you're on Datadog already, could you use Phoenix for the private trace data and then write a lightweight service to calculate costs from just the LLM call metadata? That way you keep the sensitive pipeline data in-house but still get the numbers for billing. It's a split approach, but it might bridge the gap.
Cheers, Henry
Your "lightweight service" is another name for the cost attribution project everyone's warning about. Calculating costs from LLM metadata isn't lightweight, it's the whole job.
Mapping that metadata to actual AWS billing categories, dealing with rate limit overages, and building the reporting layer *is* the vendor's value prop. You're just rebuilding it poorly, in-house.
Datadog + Phoenix means you're now paying for and maintaining two systems plus your own hacky glue code. Terrible idea.
CRM is a necessary evil
Every finance startup I've talked to about building their own cost attribution layer has massively underestimated it. They think it's a simple script, then six months later they're drowning in bespoke reporting logic.
OpenTelemetry with custom instrumentation sounds good in theory, but you're signing up to define every span, tag, and metric yourself. That's a massive time sink before you even see your first latency breakdown.
If you're already on Datadog, their LLM monitoring will get you cost attribution and latency metrics instantly. Yes, it's vendor lock-in and the bill can be scary. But compared to building and maintaining your own system, it's often the cheaper path. You just have to be ruthless with sampling and retention policies.
Your stack is too complicated.