Skip to content
effecthub

Langbase vs LangChain

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors. Langbase publishes a starting price ($100); LangChain does not. Both have a free tier. Only Langbase ships a public API.

LangbaseA serverless platform for building, deploying, and monitoring AI agents across 600-plus LLMs.LangChainMIT-licensed Python/JavaScript framework for building LLM agents, with 1,000+ model and tool integrations

Side by side

FactLangbaseLangChain
CategoryLLM Orchestration FrameworksLLM Orchestration Frameworks
Pricing modelFreemiumFree
Starts at$100 month—
Public APIYesNo
PlatformsWeb, APImacOS, Windows, Linux
CompanyLangbase, Inc.LangChain, Inc.
Launched—2022
Last verifiedSep 1, 2026Sep 6, 2026

What our research says

Langbase

Langbase bundles model routing, RAG memory, and multi-step workflows into one serverless platform, positioning itself as infrastructure teams don't have to assemble themselves from separate vector-database, routing, and orchestration tools. The free tier is real but modest (500 credits, no private pipes), so serious usage quickly pushes into the $100+/month range.

Pros

  • routes across 600+ LLMs through one unified API
  • built-in RAG memory with vector storage, no separate database needed
  • workflow layer adds retries, durability, and tracing to multi-step agents
  • clear company identity: Langbase, Inc., San Francisco

Cons

  • free tier has zero private pipes, limiting real project use
  • jump from Free to Individual is a full $100/month with no middle tier
  • full evaluation and observability tooling is described at a high level without deep public detail

LangChain

The default starting point for agent development: no other framework matches its integration catalog, documentation volume or hiring-market familiarity. The catch is churn and depth — major versions have repeatedly rewritten core APIs, and once you need observability or managed deployment you are steered toward the paid LangSmith platform.

Pros

  • Integrations for 1,000+ models, tools, vector stores and databases
  • MIT license with a large contributor community
  • LangGraph runtime adds persistence, rewind and human-in-the-loop control
  • Python and JavaScript keep parity for most features

Cons

  • History of breaking API changes between major versions
  • Layers of abstraction complicate debugging versus direct SDK calls
  • Production observability and deployment funnel into commercial LangSmith

Which one fits

Choose Langbase if you need

  • developers who want model routing, RAG, and workflows in one platform
  • teams wanting to avoid running their own vector database for agent memory
  • companies needing SSO/compliance for agent infrastructure at the Enterprise tier

Choose LangChain if you need

  • Teams prototyping agents quickly against many models and tools
  • Projects that expect to swap LLM providers without rewrites
  • Developers who want abundant examples, courses and community answers

More comparisons

Browse all LLM Orchestration Frameworks →