Hey everyone, I've been tasked with setting up a monitoring layer for our new internal RAG pipeline that surfaces financial research summaries. We're dealing with earnings call transcripts, SEC filings, that sort of thing. Accuracy and hallucination detection are non-negotiable.
We've narrowed it down to Traceloop and Arize AI, as both seem purpose-built for LLM observability. I'm trying to map features to our actual needs and would love input from anyone who's been down this road.
Here’s what I’m weighing:
**Our Must-Haves:**
* Tracking for both retrieval quality (are we fetching the right chunks?) and generation quality.
* Custom metrics for financial-specific checks (e.g., hallucination on numerical data like EBITDA figures).
* Easy integration with our existing stack (OpenAI, Pinecone, FastAPI).
* Clear alerting when confidence scores dip or anomalies are detected.
**Initial Impressions:**
* **Traceloop:** Seems very developer-first, with the Python SDK and focus on tracing. I like the idea of the `llm_guard` integration for real-time checks. The pricing model based on "sessions" is interesting.
* **Arize AI:** Comes with a strong reputation in traditional ML monitoring. Their Phoenix project is open-source, which is appealing. The UI looks more polished for product managers to view dashboards.
For those in regulated or data-sensitive fields like finance, did you run into any issues with data handling or residency? Also, how easy was it to set up custom scoring for your domain-specific logic?
Any gotchas or "wish I knew" moments comparing these two would be super helpful. I'll likely run a pilot with both, but real-world experience beats any feature matrix.
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