THE COMPARISON / A CLOSER LOOK

LangSmith vs Vellum

Compare the listed plans, useful capabilities, and practical tradeoffs.

Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.

LangSmith

🔴Developer

LLM Observability

LangSmith helps developers trace, evaluate, test, and monitor applications built with language models and agent workflows.

SOURCE-CHECK COVERAGE

No scoped source-check record shown. Check current pricing and vendor terms before buying.

Starting Price

Free

Vellum

🔴Developer

Testing & Quality

LLM development platform for prompt engineering, evaluation, workflow orchestration, and deployment of production AI applications.

SOURCE-CHECK COVERAGE

No scoped source-check record shown. Check current pricing and vendor terms before buying.

Starting Price

Free

Feature Comparison

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FeatureLangSmithVellum
CategoryLLM ObservabilityTesting & Quality
Pricing Plans1 listed plan3 listed plans
Starting PriceFreeFree
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
  • • Prompt engineering playground with multi-model comparison
  • • Automated evaluation and regression testing pipelines
  • • Visual workflow builder for multi-step AI pipelines

The editorial take

Choose Vellum if you want an integrated prompt-to-deployment platform with visual workflow building and managed infrastructure. Choose LangSmith if your stack is built on LangChain and you need deep tracing and observability for LangChain-specific constructs. Vellum offers a broader development lifecycle; LangSmith offers tighter LangChain integration.

LangSmith - Pros & Cons

Pros

  • ✓Makes multi-step LLM failures easier to localize than application logs alone
  • ✓Connects production traces, datasets, experiments, and evaluations in one workflow
  • ✓Particularly natural for teams already building with LangChain or LangGraph
  • ✓Supports repeatable release checks instead of subjective prompt spot-testing

Cons

  • ✗Useful evaluations require ongoing dataset curation and carefully designed scoring criteria
  • ✗LLM-as-judge results can be noisy and should not replace expert review for high-risk outputs
  • ✗Captured traces may contain sensitive prompts, documents, and tool results
  • ✗Current pricing and usage limits could not be verified from vendor pages in this environment

Vellum - Pros & Cons

Pros

  • ✓Complete LLM development lifecycle in one platform — from prompt engineering through production monitoring
  • ✓Automated evaluation pipelines catch prompt regressions before they reach users
  • ✓Visual workflow builder enables complex AI pipelines without orchestration code
  • ✓Model-agnostic approach supports OpenAI, Anthropic, Google, and other providers side by side
  • ✓SOC 2 Type II certified with HIPAA compliance available for regulated industries
  • ✓Strong API and SDK support (Python, TypeScript) for CI/CD integration

Cons

  • ✗Learning curve for teams new to structured LLM development practices
  • ✗Pro tier at $89/seat/month is higher than some competitors, and Enterprise requires custom sales engagement
  • ✗Adds a dependency layer between your application and LLM providers
  • ✗Workflow builder may be less flexible than code-first orchestration for very complex pipelines
  • ✗Evaluation framework effectiveness depends on teams defining good test criteria

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🔒 Security & Compliance Comparison

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Security FeatureLangSmithVellum
SOC2✅ Yes—
GDPR✅ Yes—
HIPAA——
SSO✅ Yes—
Self-Hosted🔀 Hybrid—
On-Prem✅ Yes—
RBAC✅ Yes—
Audit Log✅ Yes—
Open Source❌ No—
API Key Auth✅ Yes—
Encryption at Rest✅ Yes—
Encryption in Transit✅ Yes—
Data ResidencyUS, EU—
Data Retentionconfigurable—
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