Lunary vs LangSmith

LangSmith is a complicated and expensive tool changing every week, weighed down by legacy features you won’t use.

LangSmith and Lunary are both building tools for AI teams. Compare pricing, hosting options, and product depth below.

LangSmith

https://smith.langchain.com

LangSmith is a solution for bigger companies, and they’ve got the rooster to prove it. Because of the complexity, they’re not a good fit for smaller businesses and startups.

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Pro plan: $39/mo

Lunary

https://lunary.ai/

Pro plan: $20/mo

Feature comparison

FeatureLangSmithLunary
Self-hostableOnly for very large companies on 6-figure plans.Yes
Prompt managementYesYes
Prompt playgroundYesYes
LLM analyticsYesYes
Feedback trackingYesYes
Agent tracingYesYes
Score conversationsYesYes
Real-time evaluationsYesYes
ExportsYesYes
User trackingNoYes
PII maskingRequires custom code and complicated configuration.Yes
Custom dashboardsYesYes
Pro plan$39/mo$20/mo

LangSmith

Pros
  • Open-source
  • Beautiful & modern UI
  • Stable product
  • Centered around chatbot usecases
  • Stellar support for everyone
  • Bootstrapped company
Cons
  • Super confusing dashboard
  • Requires a lot of configuration
  • Complicated integration outside of LangChain
  • Enterprise plans start at $75k
  • No support for startups and individuals

Lunary

Pros
  • Open-source and self-hostable in your own cloud.
  • Unified monitoring, prompt management, and evaluations.
  • SOC 2 Type II & ISO 27001 certified.

Powering the world's best AI teams.

From next-gen startups to established enterprises.

IslandsbankiZurichNetomiCloseDHL

Building an AI chatbot?

Open-source GenAI monitoring, prompt management, and magic.

Open Source

Self Hostable

1-line Integration

Prompt Templates

Chat Replays

Analytics

Topic Classification

Agent Tracing

Custom Dashboards

Score LLM responses

PII Masking

Feedback Tracking

Open Source

Self Hostable

1-line Integration

Prompt Templates

Chat Replays

Analytics

Topic Classification

Agent Tracing

Custom Dashboards

Score LLM responses

PII Masking

Feedback Tracking