Vellum vs. Langchain

Forget worrying about abstractions or learning a new framework. Build, evaluate, deploy and monitor your AI applications end-to-end using Vellum.

Access expert AI support from day 1
Build RAG and Agents with visual builder and/or type-safe SDK
Set up safeguards, business logic and evaluate at scale
Monitor in production, manage releases, and gather feedback

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LLM Development doesn’t have to be this painful

Vellum
Langchain
End-to-end Solution
One central place to control your AI features from prototype to production
Everything in one platform: Merge prompts with your business logic, add custom code, out-of-box Vector DB, evaluate, deploy and measure.
No central place to manage the entire AI lifecycle. You'll need to use LangChain + LangGraph + LangSmith + LangChain Cloud... LongList of tools.
For both Product and Engineering Teams
Build AI systems, editable via Code
Build AI systems with a type-safe Python SDK.
Build with the LangChain (LCEL) language.
Build AI systems, editable via UI
Involve product teams and subject matter experts to collaborate with engineers on prompts & workflows using a visual builder.
No support for building AI systems using a UI or visual builder that allows non-technical teams to participate in the AI development process.
Support
Hands-on support in addition to having the right resources handy
Hands-on, live support + actionable industry reports.
No hands-on support, only access to documentation + tutorials.
Out of box RAG
Fully-managed vector search that can be used on day 1
Just upload your documents, set up your chunking strategy and build your RAG.
You will need to configure your own vector database.
Working in production
Working with AI in production can be scary  — but with the right tooling it can get a bit easier
Everything you need to manage your system in production — control releases, capture end-user feedback and continuously improve.
Support for managing your system in production with Langchain Cloud. This option is still in closed beta and with limited access.
End-to-end Solution
Vellum
Everything in one platform: Merge prompts with your business logic, add custom code, out-of-box Vector DB, evaluate, deploy and measure.
Langchain
You’ll need to use LangChain + LangGraph + LangSmith + LangChain Cloud.. LongList of tools.
For Product and Engineering teams
Vellum
Build any AI system (RAG, chains, agents) using arbitrary Python/Typescript code.
Engineers, product teams and subject matter experts can collaborate on prompts & workflows by using the visual builder.
Langchain
Build with the LangChain (LCEL) expression language.
No support for building AI systems using a UI or visual builder that allows non-technical teams to participate in the AI development process.
Support
Vellum
Hands-on, live support + actionable industry reports.
Langchain
No hands-on support, only access to documentation + tutorials.
Out of box RAG
Vellum
Just upload your documents, set up your chunking strategy and build your RAG.
Langchain
You will need to configure your own vector database.
Working in production
Vellum
Everything you need to manage your system in production — control releases, capture end-user feedback and continuously improve.
Langchain
Support for managing your system in production with Langchain Cloud. However, this option is still in closed beta and with limited access.
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