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Software · head to head

LangChain vs AWS SageMaker

LangChain logo

LangChain

Software

Build applications with LLMs through composability

From
Free
Rated
-
Full reviewWebsite
AWS SageMaker logo

AWS SageMaker

Software

Build, train, and deploy machine learning models at scale

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; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • They diverge on capability: LangChain covers Chains and agents, AWS SageMaker covers Jupyter notebooks.

Where they differ

Only the attributes on which LangChain and AWS SageMaker actually diverge.

Attributes where LangChain and AWS SageMaker differ
AttributeLangChainAWS SageMaker
Pricing modelfreemiumUnknown
PlatformsLinux, Mac, WindowsWeb
Founded20222006

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

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 AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

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

AWS SageMaker

  • Machine learningnot LangChain
  • Data analysisnot LangChain
  • Model trainingnot LangChain
  • Predictive analyticsnot 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

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

Pricing, plan by plan

LangChain

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

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker 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 AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker review

Questions people ask

Is LangChain or AWS SageMaker better?
Neither clearly leads. LangChain starts at Free and AWS SageMaker at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, LangChain or AWS SageMaker?
LangChain starts at Free and AWS SageMaker at Free.
Does LangChain or AWS SageMaker run on more platforms?
LangChain runs on Linux, Mac, Windows. AWS SageMaker runs on 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 AWS SageMaker is typically brought in for.
What can LangChain do that AWS SageMaker cannot?
LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment.

Answered from the vendors’ own pages

AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

Source
AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

Source
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