Machine Learning · head to head
Keras vs Milvus

Milvus
Machine Learning
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; Milvus vector dimensions are capped at 32,768
- They diverge on capability: Keras covers Sequential and Functional API, Milvus covers Billion-scale vectors.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Keras and Milvus actually diverge.
Identical on both: starting price (Free), 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 Keras
- Sequential and Functional API
- Pre-built neural network layers
- Model training and evaluation
- Transfer learning
- Model serialization
- JAX
Only in Milvus
- Billion-scale vectors
- Multiple index types
- GPU acceleration
- Hybrid search
- Data partitioning
- Hugging Face
- LangChain
- LlamaIndex
Both cover
- TensorFlow
- PyTorch
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Keras
- Machine learningnot Milvus
- Data analysisnot Milvus
- Model trainingnot Milvus
- Predictive analyticsnot Milvus
Milvus
- Self hosting a vector database for semantic searchnot Keras
- Storing and querying embeddings for retrieval augmented generationnot Keras
- Similarity search over images, audio or text at scalenot Keras
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Keras
- Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- Error messages can be vague and unhelpful, making debugging challenging
- Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch
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
Keras
Free- Open SourceFree
- High-level API
- Pre-built layers
- Model serialization
Milvus
Free- Open SourceFree
- Full features
- Self-hosted
- Community support
- Zilliz CloudFree
- Managed service
- Free tier available
Which should you pick?
Choose Keras if
- You need sequential and functional api.
- You want to start without paying.
- You work on Python, Google Colab, Jupyter.
- You also want pre-built neural network layers.
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.
Questions people ask
- Is Keras or Milvus better?
- Neither clearly leads. Keras 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, Keras or Milvus?
- Keras starts at Free and Milvus at Free.
- Does Keras or Milvus run on more platforms?
- Keras runs on Python, Google Colab, Jupyter. Milvus runs on Linux, Mac, Windows, Web.
- Can I use Keras for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Keras best used for?
- Keras 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 Keras do that Milvus cannot?
- Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. Both handle TensorFlow, PyTorch, Linux support, Mac support.
Answered from the vendors’ own pages
Keras: What is Keras?
Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.
SourceMilvus: How much does Milvus cost?
Milvus is open-source and free to use and modify. The self-hosted version has no licensing cost. Zilliz Cloud (the managed SaaS version) does not publish pricing on the website.
SourceKeras: What model architectures does Keras support?
Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.
SourceMilvus: Is there a free or open-source version of Milvus?
Yes, Milvus is fully open-source and available for free. Milvus Lite is a lightweight option for learning and prototyping that can be installed via pip.
SourceKeras: Can Keras models run on TPUs and GPUs?
Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.
SourceMilvus: Does Milvus offer a managed cloud service?
Yes, Zilliz Cloud is a fully managed Milvus cloud offering with serverless and dedicated cluster options. Pricing must be requested from the company as it is not listed on the public website.
SourceKeras: Does Keras offer pre-trained models?
Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.
SourceKeras: Who should use Keras?
Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.
SourceRelated pages
Other head to heads
- Keras vs PyTorch
- Keras vs scikit-learn
- Keras vs Python
- Keras vs Anaconda
- Keras vs AWS SageMaker
- Keras vs Azure Machine Learning
- Keras vs DataRobot
- Keras vs Jupyter
- Keras vs H2O.ai
- Keras vs Dataiku
- Keras vs Pinecone
- Keras vs Groq
- Keras vs Weka
- Keras vs BentoML
- Keras vs ClearML
- Keras vs Cohere
- Keras vs Dask
- Keras vs Fal AI
- Keras vs Google Vertex AI
- Keras vs Weaviate
- Keras vs Ray
- Keras vs LangChain
- Keras vs Weights & Biases
- Keras vs Alteryx
- Keras vs Domino Data Lab
- Keras vs DVC
- Keras vs Semantic Kernel
- Milvus vs PyTorch
- Milvus vs scikit-learn
- Milvus vs Python
- Milvus vs Anaconda
- Milvus vs AWS SageMaker
- Milvus vs Azure Machine Learning
- Milvus vs DataRobot
- Milvus vs Jupyter
- Milvus vs H2O.ai
- Milvus vs Dataiku
- Milvus vs Pinecone
- Milvus vs Groq
- Milvus vs Weka
- Milvus vs BentoML
- Milvus vs ClearML
- Milvus vs Cohere
- Milvus vs Dask
- Milvus vs Fal AI
- Milvus vs Google Vertex AI
- Milvus vs Weaviate
- Milvus vs Ray
- Milvus vs LangChain
- Milvus vs Weights & Biases
- Milvus vs Alteryx
- Milvus vs Domino Data Lab
- Milvus vs DVC
- Milvus vs Semantic Kernel

