Datadog LLM Observability vs LangSmith
Detailed side-by-side comparison to help you choose the right tool
Datadog LLM Observability
🟡Low CodeBusiness Analytics
Enterprise-grade monitoring for AI agents and LLM applications built on Datadog's infrastructure platform. Provides end-to-end tracing, cost tracking, quality evaluations, and security detection across multi-agent workflows.
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Starting Price
$2.50 per 1M indexed LLM spans (plus Datadog platform subscription from $15/host/month)LangSmith
🔴DeveloperAI Observability
LangChain’s platform for tracing, debugging, evaluating, monitoring, and operating LLM applications and agent workflows.
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Datadog LLM Observability - Pros & Cons
Pros
- ✓Unifies LLM traces with APM, infrastructure, and log telemetry so a single distributed trace covers the full request path including model calls, tool use, and downstream services
- ✓Built-in evaluations cover quality, faithfulness, toxicity, and topic relevance without requiring teams to wire up a separate evaluation framework
- ✓Security detection for prompt injection and sensitive data leakage reuses Datadog's existing detection rules engine, which is unusual among LLM-specific observability vendors
- ✓Cost and token tracking can be sliced by model, environment, user, or arbitrary custom tags and alerted on through the standard monitor system
- ✓Enterprise foundations are already in place: SOC 2, HIPAA, FedRAMP, granular RBAC, audit logs, and SSO are inherited from the core platform
- ✓Native support for multi-agent and agentic workflow tracing, including frameworks like LangChain, LlamaIndex, OpenAI Assistants, and custom orchestration
Cons
- ✗Pricing is opaque and usage-based, with separate charges for ingested spans and evaluations that can become expensive for high-volume LLM applications
- ✗The product is most valuable when paired with the rest of Datadog; teams not already on the platform inherit a heavy onboarding and contract footprint
- ✗Open-source LLM observability tools like Langfuse and Arize Phoenix offer self-hosting options that Datadog does not, which can be a blocker for regulated or air-gapped environments
- ✗The interface assumes familiarity with Datadog conventions (facets, tags, monitors), which has a steeper learning curve than purpose-built LLM-only tools
- ✗Custom evaluators and prompt experimentation features are less mature than dedicated LLM platforms like LangSmith, with fewer prompt management and dataset workflows
LangSmith - Pros & Cons
Pros
- ✓Excellent fit for LangChain and LangGraph teams because traces, evals, prompts, and agent workflows live in one connected platform.
- ✓Free Developer plan is useful for solo builders and small production workloads with 5,000 included base traces per month.
- ✓Pricing page is unusually explicit about trace retention: 14 days for base traces and 400 days for extended traces.
- ✓Enterprise plan addresses real procurement needs with custom SSO, RBAC, SLA, training, architectural guidance, and self-hosted or hybrid options.
- ✓Async SDK design is intended not to add application latency; the vendor says the app keeps running normally even if LangSmith has an incident.
Cons
- ✗Trace-based pricing can become hard to forecast for high-volume agents, especially when many agent steps produce large traces.
- ✗The strongest product experience is tied to the LangChain ecosystem, so teams with a custom stack should run a proof of concept before standardizing.
- ✗Base trace retention is only 14 days; teams that need longer debugging history must pay for extended traces or export data.
- ✗Enterprise features such as custom SSO, RBAC, support SLA, and self-hosting require custom pricing rather than a transparent public rate.
- ✗It may be more platform than needed if you only want simple LLM request logs or cost dashboards.
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