LangSmith vs Datadog LLM Observability

Detailed side-by-side comparison to help you choose the right tool

LangSmith

🔴Developer

AI Observability

LangSmith is LangChain's commercial observability, evaluation and prompt management platform for LLM apps and agents in production.

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Starting Price

Free
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Datadog LLM Observability

Data Analysis

Enterprise-grade monitoring for AI agents and LLM applications built on Datadog's infrastructure platform. Tracks prompts, responses, costs, and performance across multi-agent workflows. Pricing scales with LLM span volume.

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Starting Price

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Feature Comparison

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FeatureLangSmithDatadog LLM Observability
CategoryAI ObservabilityData Analysis
Pricing Plans59 tiers40 tiers
Starting PriceFreeContact for pricing
Key Features
  • • Tracing for any LLM stack via Python/TypeScript SDKs or OpenTelemetry
  • • LLM-as-judge, code-based and pairwise evaluations
  • • Versioned prompts with production A/B traffic splits
  • • End-to-end LLM tracing
  • • Infrastructure correlation
  • • Cost tracking

LangSmith - Pros & Cons

Pros

  • ✓Best-in-class integration if you already use LangChain or LangGraph.
  • ✓Eval suites are practical enough to actually gate releases on, not just dashboards.
  • ✓Self-hosted Enterprise tier covers SOC 2 and regulated environments.

Cons

  • ✗Per-trace pricing on Plus surprises teams that scale production traffic quickly.
  • ✗Non-LangChain stacks work but trade ergonomic polish for SDK overhead.
  • ✗Some eval features require additional LLM spend on top of the platform fee.

Datadog LLM Observability - Pros & Cons

Pros

  • ✓Seamless integration with existing Datadog infrastructure and APM monitoring creates unified observability
  • ✓Automatic LLM span detection and instrumentation requires minimal setup for popular frameworks
  • ✓Production-based experiment generation uses real data for more accurate A/B testing results
  • ✓Enterprise-grade security, compliance, and governance features meet strict organizational requirements
  • ✓Correlation between LLM performance and infrastructure metrics helps identify root causes quickly

Cons

  • ✗Span-based billing can result in unexpectedly high costs for high-volume LLM applications
  • ✗Requires Datadog platform knowledge and often additional Datadog products for full value
  • ✗More expensive than specialized AI monitoring tools for teams only tracking LLM applications
  • ✗No transparent pricing makes cost planning difficult for budget-conscious teams

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🔒 Security & Compliance Comparison

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Security FeatureLangSmithDatadog LLM Observability
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA—✅ Yes
SSO✅ Yes✅ Yes
Self-Hosted🔀 Hybrid❌ No
On-Prem✅ Yes❌ No
RBAC✅ Yes✅ Yes
Audit Log✅ Yes✅ Yes
Open Source❌ No❌ No
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyUS, EUMultiple regions available
Data RetentionconfigurableConfigurable
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Review LangSmithReview Datadog LLM Observability