Why this comparison matters
Both Datadog and LangSmith claim to monitor LLM applications, which makes them seem interchangeable. They are not. Datadog is an infrastructure observability platform that bolted on LLM metrics; LangSmith was purpose-built for LLM debugging and tracing. They excel at different problems, miss different problems, and the choice between them hinges on whether you need full-stack visibility or LLM-specific depth.
The confusion runs deeper than marketing. Both tools are post-hoc observability—they log what happened after the call fires. Neither prevents overspend. That distinction matters if your production LLM app has ever burned through a budget in 40 minutes because of a runaway loop, a hallucinating agent, or a malicious user crafting expensive prompts.
Datadog
Datadog's LLM observability module tracks token usage, latency, error rates, and cost across LLM API calls. It integrates with OpenAI, Anthropic, Bedrock, and other providers through instrumentation of the underlying libraries (langchain-python, litellm, vercel ai). You can monitor costs per model, per endpoint, per user, and correlate LLM metrics with your application's broader traces.
Pricing depends on your overall Datadog spend. LLM observability is not separately metered; it consumes your existing ingestion quota. A typical setup costs $50–150/month for moderate LLM traffic if you already have Datadog APM, or $200+/month if you're starting fresh. Datadog bills per GB of data ingested, so high-volume tracing can escalate quickly.
The main limitation is context. Datadog treats LLM calls as one kind of span among many. You get latency, token count, and cost, but not the prompt or response content (unless you configure that explicitly, which adds volume and cost). You also don't get LLM-specific debugging tools like replay, A/B testing of prompts, or evaluation frameworks. Datadog excels at "is my LLM app slow or expensive right now," not "why did this call produce the wrong output."
Datadog is owned by Datadog, Inc. The company is public and well-funded. The product is stable and integrates deeply with AWS, GCP, and Azure infrastructure.
LangSmith
LangSmith is a tracing and debugging platform built specifically for LLM applications. It captures traces (the full execution tree of an agent or chain), logs prompts and responses in full, and provides visualization, filtering, and search across your trace history. You can replay traces, compare variants (A/B test prompts or model parameters), and run evaluations against a test dataset.
LangSmith pricing (as of February 2025) is $39/month for the Pro plan, which includes up to 1 million traces per month, or you can pay per trace at $0.01–0.10/trace depending on volume. For teams with high trace volume, the cost scales linearly.
The core limitation is scope. LangSmith is purpose-built for LangChain, LangGraph, and a narrower set of frameworks. If your stack uses raw OpenAI/Anthropic SDK calls or a non-LangChain orchestrator, you can instrument it via LangSmith's REST API, but the experience is less polished. LangSmith also doesn't integrate with your application infrastructure—no database query tracing, no cache metrics, no network latency breakdowns. For teams that need "what went wrong in my LLM workflow," LangSmith is precise. For teams that need "where is my stack slow and expensive," Datadog is the better fit.
LangSmith is owned by LangChain, which is VC-backed and closely tied to the LangChain open-source project. It is stable but tightly coupled to that ecosystem.
Comparison table
| Capability | Datadog | LangSmith | noburn.dev |
|---|---|---|---|
| Trace capture | Yes | Yes (full prompt/response) | No (cost estimates only) |
| Full-stack correlation | Yes (APM + LLM) | No (LLM-focused) | No |
| Token counting | Yes | Yes | Yes (estimated client-side) |
| Cost tracking | Yes | Yes | Yes (pre-execution) |
| Prompt replay | No | Yes | No |
| A/B testing | No | Yes | No |
| Budget enforcement | No | No | Yes (blocks over-budget calls) |
| Per-user metering | Limited | No | Yes (Stripe passthrough) |
| Pre-flight blocking | No | No | Yes (before API call) |
| OpenAI / Anthropic | Yes | Yes | Yes |
| LangChain / LangGraph | Yes | Yes (native) | Yes |
| Pricing | $50–200+/mo | $39–500+/mo | $0–49/mo |
| Self-hosting | Enterprise only | No | No |
The enforcement gap
Both tools answer the question "how much did this cost?" after the call fires. Neither answers "should this call fire at all?" This is not a minor difference.
In production, you hit one of four scenarios:
- Your model hallucinates and loops, re-prompting the same expensive call 50 times.
- A customer crafts a pathological input (deeply nested context, adversarial prompt injection) that costs 50x more than expected.
- A new feature rolls out with an accidentally expensive model or longer context window.
- Your monitoring alert fires, but your team is in a meeting for 20 minutes—and by then, the damage costs $2,000.
Datadog and LangSmith will log every single one of these calls. They will tell you after 40 minutes that something went wrong. They will not stop the call before it fires. That is why teams building multi-tenant SaaS or usage-sensitive products need an enforcement layer in addition to observability.
FAQ
Can I use Datadog and LangSmith together? Yes, many teams do. Datadog provides full-stack visibility, and LangSmith provides LLM-specific debugging. The trade-off is dual ingestion costs and overlapping trace data. If your main need is "find slow LLM calls and debug why," LangSmith alone is cheaper. If you need "correlate LLM costs with database load," Datadog is necessary.
Does LangSmith work outside LangChain? Yes, via REST API instrumentation. The experience is less smooth than LangChain-native tracing, but you can send traces from any Python or Node.js application. Datadog has broader library support out of the box.
Can either tool prevent cost overruns? No. Both are observability, not enforcement. They report after the fact. If you need to stop a call before it charges your account, you need a different tool.
Which is cheaper? LangSmith starts at $39/month and scales with trace volume. Datadog starts higher ($100–200/month for a minimal LLM setup) and scales with data ingestion. For small, LLM-focused apps, LangSmith. For large, multi-service systems, Datadog often becomes cheaper because you already pay for their APM.
Is one owned by a big company? Datadog is public. LangSmith is owned by LangChain (VC-backed, not public). Both are stable. Datadog is less likely to shut down. LangSmith is tightly coupled to LangChain's roadmap.
Where noburn fits in this stack
Datadog and LangSmith are observability tools—they measure what happened. noburn.dev is an enforcement layer that runs before the API call fires. It estimates token cost client-side using LLM tokenizers, compares the estimate against a user or project budget, and blocks the request if the call would exceed the limit. This prevents the runaway loop, the expensive customer input, and the accidental model change from ever charging your account.
noburn.dev integrates with OpenAI, Anthropic, LiteLLM, LangChain, LangGraph, and Vercel AI SDK, so it fits into your existing stack regardless of whether you instrument through Datadog or LangSmith. It handles per-user metering for multi-tenant SaaS and integrates with Stripe for usage-based billing, so you can charge customers for their LLM usage and enforce spending limits at the same time.
You still need Datadog or LangSmith for debugging and performance analysis. noburn.dev prevents the budget from being destroyed before you have a chance to debug. The free tier covers 50,000 requests per month. Documentation and SDKs are at noburn.dev/docs.