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Machine Learning & Data Science · head to head

AWS SageMaker vs Milvus

AWS SageMaker logo

AWS SageMaker

Machine Learning & Data Science

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
Milvus logo

Milvus

Machine Learning & Data Science

Open-source vector database for scalable similarity search

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Milvus vector dimensions are capped at 32,768
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, Milvus covers Billion-scale vectors.

Where they differ

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

Attributes where AWS SageMaker and Milvus differ
AttributeAWS SageMakerMilvus
Pricing modelUnknownfreemium
PlatformsWebLinux, Mac, Windows, Web
Founded20062017

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 AWS SageMaker

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

Only in Milvus

  • Billion-scale vectors
  • Multiple index types
  • GPU acceleration
  • Hybrid search
  • Data partitioning
  • PyTorch
  • TensorFlow
  • Hugging Face

Both cover

  • Web support

What people use each for

The jobs each tool is most often brought in to do.

AWS SageMaker

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

Milvus

  • Self hosting a vector database for semantic searchnot AWS SageMaker
  • Storing and querying embeddings for retrieval augmented generationnot AWS SageMaker
  • Similarity search over images, audio or text at scalenot AWS SageMaker

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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

Milvus

  • Vector dimensions are capped at 32,768
  • A collection is limited to 64 fields, 1,024 partitions and 16 shards
  • Only 1 index is allowed per field
  • Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
  • Input and output per RPC is capped at 64 MB for insert, search and query
  • VARCHAR values are limited to 65,535 characters
  • Data loaded into query nodes cannot exceed 90% of available memory
  • An instance supports at most 65,536 collections

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Milvus

Free
  • Open SourceFree
    • Full features
    • Self-hosted
    • Community support
  • Zilliz CloudFree
    • Managed service
    • Free tier available

Which should you pick?

Choose AWS SageMaker if

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

Choose Milvus if

  • You need billion-scale vectors.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want multiple index types.
Milvus review

Questions people ask

Is AWS SageMaker or Milvus better?
Neither clearly leads. AWS SageMaker starts at Free and Milvus at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or Milvus?
AWS SageMaker starts at Free and Milvus at Free.
Does AWS SageMaker or Milvus run on more platforms?
AWS SageMaker runs on Web. Milvus runs on Linux, Mac, Windows, Web.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Milvus is typically brought in for.
What can AWS SageMaker do that Milvus cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. 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.

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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