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Machine Learning · head to head

LangChain vs Zilliz

LangChain logo

LangChain

Machine Learning

Build applications with LLMs through composability

From
Free
Rated
-
Full reviewWebsite
Zilliz logo

Zilliz

Databases

Managed vector database and vector lakebase for AI applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: LangChain the free Developer plan of LangSmith is limited to 1 seat; Zilliz pricing structure not publicly disclosed, requires sales contact
  • They diverge on capability: LangChain covers Chains and agents, Zilliz covers Vector indexing.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which LangChain and Zilliz actually diverge.

Attributes where LangChain and Zilliz differ
AttributeLangChainZilliz
Pricing modelfreemiumcontact-sales
PlatformsLinux, Mac, WindowsCloud, Self-hosted
CategoryMachine LearningDatabases
Founded20222017

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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
  • OpenAI
  • Anthropic
  • Hugging Face

Only in Zilliz

  • Vector indexing
  • Distributed architecture
  • SQL interface
  • Tensor support
  • Real-time search
  • Cloud-native
  • Open-source compatible

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 Zilliz
  • Tracing and debugging LLM chains and agent runsnot Zilliz
  • Evaluating prompt and model changes against datasetsnot Zilliz

Zilliz

  • Build retrieval-augmented generation (RAG) systemsnot LangChain
  • Implement semantic search over documentsnot LangChain
  • Create multimodal search with text and imagesnot LangChain
  • Power recommendation engines with vector similaritynot LangChain
  • Enable similarity search on user embeddingsnot 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

Zilliz

  • Pricing structure not publicly disclosed, requires sales contact
  • Operational complexity for self-hosted Milvus deployments
  • Learning curve for those unfamiliar with vector databases
  • Limited built-in analytics compared to some alternatives

Pricing, plan by plan

LangChain

Free
  • Open SourceFree
    • Full framework
    • Community support
  • LangSmith$39/month
    • Debugging
    • Monitoring
    • Testing

Zilliz

Free

No published plan breakdown. See the Zilliz review.

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.
LangChain review

Choose Zilliz if

  • You need vector indexing.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want distributed architecture.
Zilliz review

Questions people ask

Is LangChain or Zilliz better?
Neither clearly leads. LangChain starts at Free and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, LangChain or Zilliz?
LangChain starts at Free and Zilliz at Free.
Does LangChain or Zilliz run on more platforms?
LangChain runs on Linux, Mac, Windows. Zilliz runs on Cloud, Self-hosted.
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 Zilliz is typically brought in for.
What can LangChain do that Zilliz cannot?
LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor 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.

Source
Zilliz: What is the difference between Milvus and Zilliz Cloud?

Milvus is the open-source vector database that you can self-host. Zilliz Cloud is the fully managed service built on Milvus that removes operational overhead and handles scaling automatically.

Source
LangChain: 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.

Source
Zilliz: How many vectors can Zilliz handle?

Milvus and Zilliz Cloud can store and search billions of vectors through their distributed architecture that separates storage and compute layers.

Source
Zilliz: Is Milvus open-source?

Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.

Source
Zilliz: What pricing does Zilliz Cloud offer?

Zilliz Cloud pricing is not publicly listed and requires contacting their team to discuss your specific scale and use case requirements.

Source
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