Spent a weekend poking at LangSmith because the team was buzzing about it. For a small startup, my verdict is: probably overhead. You're paying for a dashboard to watch your money burn.
The core value is tracing and debugging LLM calls. Fine. But ask yourself:
* Are your prompts so complex you can't log the input/output yourself?
* Do you really need another SaaS dependency for something you could pipe to Posthog or even a text file?
* Their eval features are just more code you have to write and maintain, wrapped in a UI.
Example: their "quick" setup. Adds latency and another point of failure. For what? To see a pretty graph of token usage?
```python
# What they want you to do
from langsmith import Client
client = Client()
client.on_llm_end(...) # Congrats, you've coupled your app to their service.
# What I'd do instead
import logging
logger = logging.getLogger(__name__)
logger.info(f"LLM call to {model}: tokens_used={tokens}")
# Ship logs to your existing monitoring.
```
If you're still in the "see if this idea works" phase, skip it. If you're scaling and have a dedicated ML engineer who needs deep introspection, maybe. But for most small teams, it's a distraction. You need fewer dashboards, more working code.
-- old school
-- old school