Machine Learning · head to head
LangChain vs Weaviate

LangChain
Machine Learning
Build applications with LLMs through composability
- From
- Free
- Rated
- -
The short version
- Each has a real cost: LangChain the free Developer plan of LangSmith is limited to 1 seat; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
- They diverge on capability: LangChain covers Chains and agents, Weaviate covers Vector and keyword search.
Where they differ
Only the attributes on which LangChain and Weaviate actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in LangChain
- Chains and agents
- Retrieval-augmented generation
- Memory management
- Tool integration
- Prompt templates
- Anthropic
- Pinecone
- Chroma
Only in Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- Cohere
- LangChain
- LlamaIndex
Both cover
- OpenAI
- Hugging Face
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
LangChain
- Building LLM applications and agents in Python or JavaScriptnot Weaviate
- Tracing and debugging LLM chains and agent runsnot Weaviate
- Evaluating prompt and model changes against datasetsnot Weaviate
Weaviate
- Running a vector database for semantic and hybrid searchnot LangChain
- Generating and storing embeddings alongside the objects they describenot LangChain
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
LangChain
- The free Developer plan of LangSmith is limited to 1 seat
- Base traces are retained for 14 days only; 400 day retention costs extra
- Included traces are capped at 5,000 per month on Developer and 10,000 per month on Plus, with everything beyond billed pay as you go
- Self hosted and hybrid deployment of LangSmith is Enterprise only
- Custom SSO, RBAC and ABAC are Enterprise only
- A support SLA is Enterprise only
- Enterprise pricing is by quote with no published rate
Weaviate
- The free tier caps at 100,000 objects, 1 GB of memory and a single collection
- Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
- Premium is a prepaid contract starting at $400 a month rather than pay as you go
- Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
- The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond
Pricing, plan by plan
LangChain
Free- Open SourceFree
- Full framework
- Community support
- LangSmith$39/month
- Debugging
- Monitoring
- Testing
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
Which should you pick?
Choose LangChain if
- You need chains and agents.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want retrieval-augmented generation.
Choose Weaviate if
- You need vector and keyword search.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want built-in vectorizers.
Questions people ask
- Is LangChain or Weaviate better?
- Neither clearly leads. LangChain starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, LangChain or Weaviate?
- LangChain starts at Free and Weaviate at Free.
- Does LangChain or Weaviate run on more platforms?
- LangChain runs on Linux, Mac, Windows. Weaviate runs on Linux, Mac, Windows, Web.
- Can I use LangChain for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is LangChain best used for?
- LangChain is most often used for building llm applications and agents in python or javascript, tracing and debugging llm chains and agent runs, evaluating prompt and model changes against datasets. Of those, building llm applications and agents in python or javascript and tracing and debugging llm chains and agent runs are not what Weaviate is typically brought in for.
- What can LangChain do that Weaviate cannot?
- LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Both handle OpenAI, Hugging Face, Linux support, Mac support.
Answered from the vendors’ own pages
LangChain: Does LangChain charge for its services?
LangChain's main website does not display pricing. However, LangSmith (a related platform) offers both free and paid plans. Visit the dedicated pricing page or contact LangChain for details.
SourceWeaviate: What pricing options does Weaviate offer?
Weaviate provides a free tier with usage-based pricing, plus enterprise options. Visit the pricing page for detailed information on plans.
SourceLangChain: How can I learn about LangChain pricing?
Click on the Pricing link in navigation or use the Try LangSmith or Get a demo options to explore pricing for LangChain's commercial offerings.
SourceWeaviate: Does Weaviate offer customer support?
Yes, support is included with Weaviate's cloud offerings. Enterprise customers receive first-class support from their global team of experts.
SourceWeaviate: Can I deploy Weaviate on my own infrastructure?
Yes. Weaviate is open source and deployment-agnostic. You can run it in your own cloud environment or use their managed cloud service.
SourceWeaviate: What data security features does Weaviate provide?
Weaviate includes security & governance, RBAC, SOC 2 and HIPAA compliance, along with multi-tenancy and high availability for enterprise requirements.
SourceWeaviate: How do I get started with Weaviate?
Sign up for their cloud tier, create your first dataset, connect an LLM, and build your AI app. Documentation and quickstart guides are available for Python, Go, TypeScript, and JavaScript.
SourceRelated pages
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