LangSmith vs Baseten for real-time LLM monitoring on serverless GPUs

Hey folks, been deep in the weeds on this one and wanted to share my findings. I'm running a few high-throughput, real-time inference endpoints using serverless GPUs (think Lambda with large memory configs and some SageMaker Serverless for bigger models). The core need is granular, real-time monitoring of latency, token usage, and cost per invocation, especially with unpredictable burst traffic.

I've been testing both LangSmith and Baseten for this specific monitoring layer. LangSmith's tracing is fantastic for the development and debugging cycle—seeing the exact chain of calls, tool usage, and intermediate results is a game-changer. But for a pure, production-scale *monitoring* dashboard on serverless, I found myself wrestling a bit. The pricing model can get fuzzy with high volume, and I wanted something that felt more integrated with my existing cloud cost alerts.

Baseten, on the other hand, is built directly around the model deployment. The real-time metrics (p50, p99 latency, GPU utilization) pop up almost automatically when you deploy there. Their cost tracking is very transparent because it's tied to your infra on their platform. However, I'm not fully deploying my stack there; I'm mainly using it as a monitoring facade for my existing serverless endpoints, which feels slightly against the grain.

Here's a snippet of the wrapper I used to send data to both for comparison:

```python
import baseten
import langsmith

def log_inference(model_input, model_output, latency_ms, tokens_used):
# Log to Baseten for infra-centric metrics
baseten.log_metric("inference_latency", latency_ms)
baseten.log_metric("tokens_used", tokens_used)

# Log as a LangSmith trace
with langsmith.trace("serverless_inference") as trace:
trace.outputs = {"response": model_output}
trace.metadata = {
"latency_ms": latency_ms,
"tokens": tokens_used,
"cost_estimate": calculate_cost(tokens_used, latency_ms)
}
```

The big question for the community: Has anyone else run a similar setup? I'm trying to nail down the most cost-effective and operationally simple way to get observability that bridges LLM-specific traces (like token usage per call) and classic SRE metrics (latency, error rates, cost). Is there a clear winner, or are we in the "glue things together" phase? Especially interested if you've tied this into a FinOps dashboard.