Software · head to head
Milvus vs Comet ML
Milvus
Software
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Milvus vector dimensions are capped at 32,768; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: Milvus covers Billion-scale vectors, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which Milvus and Comet ML actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Unknown), founded (2017).
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 Milvus
- Billion-scale vectors
- Multiple index types
- GPU acceleration
- Hybrid search
- Data partitioning
- LangChain
- LlamaIndex
Only in Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- Keras
- scikit-learn
Both cover
- PyTorch
- TensorFlow
- Hugging Face
- Linux support
- Mac support
- Windows support
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Milvus
- Self hosting a vector database for semantic searchnot Comet ML
- Storing and querying embeddings for retrieval augmented generationnot Comet ML
- Similarity search over images, audio or text at scalenot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Milvus
- Monitoring and evaluating LLM applications with tracingnot Milvus
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Pricing, plan by plan
Milvus
Free- Open SourceFree
- Full features
- Self-hosted
- Community support
- Zilliz CloudFree
- Managed service
- Free tier available
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Which should you pick?
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.
Choose Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Questions people ask
- Is Milvus or Comet ML better?
- Neither clearly leads. Milvus starts at Free and Comet ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Milvus or Comet ML?
- Milvus starts at Free and Comet ML at Free.
- Does Milvus or Comet ML run on more platforms?
- Milvus runs on Linux, Mac, Windows, Web. Comet ML runs on Web, Linux, Mac, Windows.
- Can I use Milvus for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Milvus best used for?
- Milvus is most often used for self hosting a vector database for semantic search, storing and querying embeddings for retrieval augmented generation, similarity search over images, audio or text at scale. Of those, self hosting a vector database for semantic search and storing and querying embeddings for retrieval augmented generation are not what Comet ML is typically brought in for.
- What can Milvus do that Comet ML cannot?
- Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle PyTorch, TensorFlow, Hugging Face, Linux support.
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