LangChain / LangSmith Pricing: What the Platform Costs

LangChain the framework is free and open source. LangSmith, the observability and evaluation platform, is where the pricing kicks in. If you're building production LLM apps with LangChain, you'll eventually want LangSmith for debugging and monitoring. Here's what that costs.

Developer (Free)

$0 Free forever
  • 5,000 traces per month
  • 1 user
  • 14-day data retention
  • Basic tracing and debugging
  • LangGraph Cloud playground
Most Popular

Plus

$39 per user/month
  • 100,000 traces per month included
  • Additional traces at $0.50 per 1,000
  • Up to 10 users
  • 400-day data retention
  • Evaluation datasets and experiments
  • Prompt versioning and management

Enterprise

Custom annual contract
  • Unlimited traces
  • Unlimited users
  • Custom data retention
  • SSO and RBAC
  • Self-hosted deployment option
  • Dedicated support and SLA
  • SOC2 compliance

Hidden Costs & Gotchas

  • Overage charges on Plus hit fast. If you're running 500K traces/month, you'll pay $39 base plus $200 in overage ($0.50/1K traces). That's $239/user/month.
  • The framework itself is free, but LangChain apps tend to use more LLM calls than simpler architectures. Chain-of-thought, retrieval, and agent loops multiply your API costs.
  • Data retention on the free tier is only 14 days. If you need to debug an issue from last month, that data is gone.
  • LangGraph Cloud (their agent deployment platform) has separate compute costs on top of LangSmith subscriptions.

Which Plan Do You Need?

Solo developer prototyping

Free tier with 5K traces is enough for development and testing. You won't need more until you're handling real production traffic.

Small team shipping LLM features

Plus at $39/user/mo with 100K traces works well for teams of 2-5. Watch overage charges once you're past the initial 100K. If you don't need the observability, you can skip LangSmith entirely and just use the open-source framework.

Enterprise with multiple LLM apps

Enterprise is worth it once you have 10+ developers or need SSO and compliance. The unlimited traces alone can save money compared to Plus overage charges at high volumes.

The Bottom Line

LangChain is free. LangSmith Plus at $39/user/mo is worth it if you're debugging production LLM apps and need trace visibility. But watch the overage charges on traces. If you're building simple LLM integrations, you might not need LangSmith at all. Alternatives like Langfuse (open source) give you similar observability for free if you self-host.

Disclosure: Pricing information is sourced from official websites and may change. We update this page regularly but always verify current pricing on the vendor's site before purchasing.

Related Resources

Best LLM Frameworks → LangChain review → LangChain vs LlamaIndex → OpenAI API pricing → RAG architecture guide →

Frequently Asked Questions

Is LangChain free?

LangChain the framework is completely free and open source. LangSmith, the observability and evaluation platform, has a free tier (5K traces/month) and paid plans starting at $39/user/month. You can use LangChain without LangSmith.

Do I need LangSmith to use LangChain?

No. LangChain works fine without LangSmith. LangSmith adds tracing, debugging, and evaluation tools that help in production. For prototyping and simple apps, the framework alone is sufficient.

How does LangSmith pricing compare to Langfuse?

Langfuse is open source and free if you self-host. Their cloud version has a free tier and paid plans that are generally cheaper than LangSmith. LangSmith has tighter integration with LangChain, but Langfuse works with any LLM framework.

What counts as a trace in LangSmith?

A trace represents one complete execution of your LangChain pipeline, including all the steps (LLM calls, retrieval, tool use). A single user request that triggers 5 LLM calls still counts as 1 trace. Complex agent loops with many steps are still 1 trace per top-level invocation.

Is LangChain worth using in production?

It depends on your use case. LangChain is great for complex chains, agents, and retrieval pipelines where you need the orchestration abstractions. For simple API calls to OpenAI or Anthropic, it adds unnecessary complexity. Many production teams start with LangChain and either stick with it or simplify to direct API calls.