Datadog LLM Observability vs LangSmith
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
Datadog LLM Observability
LLM tracing bolted onto the Datadog APM platform. Bills per LLM span rather than per trace, which is the single most misunderstood thing about it - agentic workloads can burn the 40,000-span free tier in a day.
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.
| Datadog LLM Observability | LangSmith | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 3/5 | 3/5 |
| Starting price | $160/mo | $39/seat/mo |
| Billing meter | span | seat |
| Free plan | Yes | Yes |
| Free self-hosting | No or paid tier only | No or paid tier only |
| Best for | Teams already standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. | Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill |
Our verdict on Datadog LLM Observability
Datadog LLM Observability is the obvious pick if Datadog is already your monitoring platform, and a poor one otherwise. The correlation story is real - being able to trace an LLM latency spike down through the service, host and database in one product is something no AI-native competitor matches. The eval tooling is competent but not the reason you would buy it. The thing that decides this product is billing. Datadog charges per LLM span, not per trace, and that distinction is where teams get hurt. A simple completion is 3 to 5 spans, but an agentic workflow with tool calls, retrieval and reasoning chains is commonly 20 to 50. Estimate your bill from requests and you will be wrong by more than an order of magnitude. Model span volume first, then decide.
Full Datadog LLM Observability review →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 →Frequently Asked Questions
What is the main difference between Datadog LLM Observability and LangSmith?
Datadog LLM Observability: Teams already standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. LangSmith: Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Datadog LLM Observability or LangSmith?
It depends entirely on your workload shape, because they meter differently - Datadog LLM Observability bills on span and LangSmith bills on seat. Published starting prices are $160/mo and $39/seat/mo 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 Datadog LLM Observability or LangSmith?
Datadog LLM Observability: No or paid tier only. LangSmith: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.