LangSmith vs New Relic AI Monitoring
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
LangSmith
LangChain's proprietary observability and eval platform. Turnkey and deeply integrated with LangChain and LangGraph - but closed-source, and the trace bill explodes at production scale.
New Relic AI Monitoring
LLM monitoring layered onto New Relic's APM platform, billed on data ingested - which is close to the worst possible meter for verbose agent traces. One reported deployment went from $1,400 to $12,000 a month after turning it on.
| LangSmith | New Relic AI Monitoring | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 3/5 | 2/5 |
| Starting price | $39/seat/mo | $0.40/GB ingested |
| Billing meter | seat | gb-ingested |
| Free plan | Yes | Yes |
| Free self-hosting | No or paid tier only | No or paid tier only |
| Best for | Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill | Organisations already standardised on New Relic for APM, logs, Kubernetes and SRE workflows, running relatively low-volume LLM features, who need correlation more than evaluation. |
Our verdict on LangSmith
The most turnkey observability platform if you already live in LangChain and LangGraph - the tracing is zero-config and the eval tooling is genuinely good. But it's closed source, self-hosting is Enterprise-only, and the trace bill is roughly 25x Langfuse at scale. It's great until you scale or want out, and you can't self-host your way around either problem.
Full LangSmith review →Our verdict on New Relic AI Monitoring
New Relic AI Monitoring makes sense for exactly one buyer - the organisation already committed to New Relic - and is a poor choice for anyone else. The correlation story is real, and the applied intelligence and SRE agent tooling is more mature than anything the AI-native vendors offer. But two structural problems dominate. First, the billing meter is data ingested, and LLM traces are among the most verbose telemetry in existence - prompts, completions, tool payloads and retrieved context routinely make a single trace 5 to 50 KB. Charging per gigabyte for that is close to the worst possible fit, and one reported deployment saw its bill go from $1,400 to between $9,500 and $12,000 a month after AI Monitoring volumes were counted. Second, it does not evaluate anything. There is no faithfulness, relevance or hallucination scoring, and no annotation or dataset curation. It tells you what your LLM did and what it cost, not whether the output was any good. If you need evaluation, you are buying a second tool regardless.
Full New Relic AI Monitoring review →Frequently Asked Questions
What is the main difference between LangSmith and New Relic AI Monitoring?
LangSmith: Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill New Relic AI Monitoring: Organisations already standardised on New Relic for APM, logs, Kubernetes and SRE workflows, running relatively low-volume LLM features, who need correlation more than evaluation. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, LangSmith or New Relic AI Monitoring?
It depends entirely on your workload shape, because they meter differently - LangSmith bills on seat and New Relic AI Monitoring bills on gb-ingested. Published starting prices are $39/seat/mo and $0.40/GB ingested respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host LangSmith or New Relic AI Monitoring?
LangSmith: No or paid tier only. New Relic AI Monitoring: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.