Machine Learning & Data Science · head to head
Weaviate vs AWS SageMaker
AWS SageMaker
Machine Learning & Data Science
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- They diverge on capability: Weaviate covers Vector and keyword search, AWS SageMaker covers Jupyter notebooks.
Where they differ
Only the attributes on which Weaviate and AWS SageMaker actually diverge.
| Attribute | Weaviate | AWS SageMaker |
|---|---|---|
| Pricing model | freemium | Unknown |
| Platforms | Linux, Mac, Windows, Web | Web |
| Founded | 2019 | 2006 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Hugging Face
- Cohere
Only in AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Weaviate
- Running a vector database for semantic and hybrid searchnot AWS SageMaker
- Generating and storing embeddings alongside the objects they describenot AWS SageMaker
AWS SageMaker
- Machine learningnot Weaviate
- Data analysisnot Weaviate
- Model trainingnot Weaviate
- Predictive analyticsnot Weaviate
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Which should you pick?
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.
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Questions people ask
- Is Weaviate or AWS SageMaker better?
- Neither clearly leads. Weaviate 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, Weaviate or AWS SageMaker?
- Weaviate starts at Free and AWS SageMaker at Free.
- Does Weaviate or AWS SageMaker run on more platforms?
- Weaviate runs on Linux, Mac, Windows, Web. AWS SageMaker runs on Web.
- Can I use Weaviate for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Weaviate best used for?
- Weaviate is most often used for running a vector database for semantic and hybrid search, generating and storing embeddings alongside the objects they describe. Of those, running a vector database for semantic and hybrid search and generating and storing embeddings alongside the objects they describe are not what AWS SageMaker is typically brought in for.
- What can Weaviate do that AWS SageMaker cannot?
- Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Both handle Web support.
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.
SourceAWS 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.
SourceAWS 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