LLM monitoring tools compared
A short, honest map of the LLM monitoring landscape — organized around the question most comparisons skip: does the tool send your prompts and completions to a server, and what is it actually built for? Most of these do different jobs; the goal here is to help you pick the right category, not to crown a winner.
| Tool | What it's for | Sends prompts + completions by default | Setup | Entry paid price |
|---|---|---|---|---|
| ChirpPal | Alarm when a live call breaks | No — metadata only | One line around a call | $19/mo |
| Langfuse | Tracing + prompt mgmt + evals | Yes — captured for tracing | SDK instrumentation | $29/mo (self-host free) |
| Helicone | Request logging / observability | Yes — logged via proxy | Proxy or async logging | Free tier + paid (self-host free) |
| LangSmith | Tracing + datasets + evals | Yes — captured for tracing | SDK instrumentation | Free tier + paid |
What each one is best at
ChirpPal — a one-line, fail-open wrapper that runs deterministic checks (valid JSON, refusals, keywords, latency, errors) in your process and sends only pass/fail metadata; it emails you when a live call starts failing, and includes a free model-deprecation radar. It is not a trace store — there's nothing to replay. Best when you want an alarm, not a platform, and you don't want to send user content. How it works · the privacy detail.
Langfuse — a full, open-source LLM engineering platform: tracing, prompt versioning, datasets, experiments, LLM-as-a-judge evals. Self-hostable. Best when a team is doing hands-on LLM development and wants to replay and evaluate calls. ChirpPal vs Langfuse.
Helicone — observability via a proxy (or async logging): logs your requests and responses, with dashboards, caching, and cost tracking. Open-source / self-hostable. Best when you want a fast way to log and inspect traffic with a one-line base-URL change.
LangSmith — LangChain's tracing and evaluation platform: captures prompts and completions for debugging, datasets, and evals. Tightly integrated with LangChain. Best when you're already in that ecosystem and want first-class tracing and evals.
The one axis that actually splits them
Three of the four are observability tools: their value comes from capturing your prompts and completions so you can inspect them later, which is exactly why they store that content (on their cloud, or your self-hosted instance). ChirpPal is the odd one out on purpose — it's an alarm, so it evaluates the response in your process and never needs to send the content at all. If "inspect everything later" is what you want, pick one of the tracing tools. If "tell me the moment it breaks, and don't take my prompts" is what you want, that's ChirpPal.
Want the alarm side — one line, metadata only by default, plus a free radar for model retirements?
See how ChirpPal works →Related: vs Langfuse · do you even need a tool? · monitoring without sending prompts · Model Deprecation Radar.