Software · head to head
Weaviate vs Comet ML
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- 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; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: Weaviate covers Vector and keyword search, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which Weaviate and Comet ML actually diverge.
Identical on both: starting price (Free), pricing model (freemium), 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 Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Cohere
- LangChain
Only in Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Both cover
- 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.
Weaviate
- Running a vector database for semantic and hybrid searchnot Comet ML
- Generating and storing embeddings alongside the objects they describenot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Weaviate
- Monitoring and evaluating LLM applications with tracingnot 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
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
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
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 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 Weaviate or Comet ML better?
- Neither clearly leads. Weaviate 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, Weaviate or Comet ML?
- Weaviate starts at Free and Comet ML at Free.
- Does Weaviate or Comet ML run on more platforms?
- Weaviate runs on Linux, Mac, Windows, Web. Comet ML runs on Web, Linux, Mac, Windows.
- 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 Comet ML is typically brought in for.
- What can Weaviate do that Comet ML cannot?
- Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle Hugging Face, Linux support, Mac support, Windows support.