What actually works for LLM tracing in a K8s production cluster?

Hey folks, been wrestling with this for a few months across a couple of projects. We've got a Python FastAPI app running on Kubernetes, making calls to various LLMs (OpenAI, Anthropic, some OSS models), and we needed tracing that's actually production-ready. LangSmith is great, but the "just set these environment variables" approach falls apart at scale in K8s.

Here's what we ended up with after a lot of trial and error. The core principles:

* **No synchronous logging in the request path.** The LangSmith SDK's default calls are blocking. In a high-volume API, that's a non-starter.
* **Batch and ship asynchronously.** We use a background thread via the `langsmith.run_helpers` module.
* **Respect pod lifecycle.** Traces need to be flushed on shutdown, or you lose data during deployments/scaling in.

Our setup:

**1. The Wrapper/Client Singleton**
We initialize the LangSmith client once, configured for async flushing. We also patch the OpenAI client to auto-trace.

```python
# llm_tracing.py
import os
from contextlib import asynccontextmanager
from langsmith import RunTree, run_helpers
from openai import AsyncOpenAI

# LangSmith config - uses standard env vars (LANGSMITH_API_KEY, LANGSMITH_TRACING, etc.)
_client = None

def get_langsmith_client():
global _client
if _client is None:
from langsmith import Client
# Critical: Use the background handler for async logging
_client = Client(
run_helpers=_run_helpers
)
return _client

# Use the background scheduler
_run_helpers = run_helpers.AsyncRunHandler(
max_batch_size=50,
flush_interval_seconds=2,
)
```

**2. Lifespan Management in FastAPI**
We start and stop the async handler with the app's lifespan.

```python
from fastapi import FastAPI
from llm_tracing import _run_helpers

@asynccontextmanager
async def lifespan(app: FastAPI):
# Start the background thread for LangSmith
_run_helpers.start()
yield
# Flush all remaining traces on shutdown
_run_helpers.shutdown()

app = FastAPI(lifespan=lifespan)
```

**3. Kubernetes: Graceful Shutdown & Readiness**
In your deployment, ensure the readiness probe gives enough time for flush on SIGTERM.

```yaml
# snippet from deployment.yaml
spec:
containers:
- name: api
lifecycle:
preStop:
exec:
command: ["/bin/sh", "-c", "sleep 15"] # Give time for trace flushing
readinessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 5
periodSeconds: 5
```

**The Result:**
- Traces are batched and sent in the background. Near-zero latency impact on our endpoints.
- We haven't lost a trace during a rolling update in weeks.
- We can crank the sampling rate up (we run at 100% for now) without worrying.

**Alternatives we tested & why we stuck with this:**
- **Manual instrumentation:** Too much boilerplate.
- **OpenTelemetry + LangSmith exporter:** Still a bit rough around the edges, and the data model mapping wasn't perfect for our use case.
- **Just using the sync client:** Caused sporadic latency spikes and was dropping traces under load.

Has anyone else found a different sweet spot? Especially curious about handling multi-tenant tracing or if you've integrated this with your existing OTEL pipelines.

-- Weave