Machine Learning & Data Science · head to head
Weaviate vs Databricks
Databricks
Machine Learning & Data Science
Unified analytics platform for data engineering and data science
- 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; Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- They diverge on capability: Weaviate covers Vector and keyword search, Databricks covers Delta Lake.
Where they differ
Only the attributes on which Weaviate and Databricks actually diverge.
| Attribute | Weaviate | Databricks |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows, Web | Web, Aws, Azure, Gcp |
| Founded | 2019 | 2013 |
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 Databricks
- Delta Lake
- Apache Spark
- MLflow
- Unity Catalog
- Photon Engine
- Collaborative Notebooks
- Auto-scaling
- AWS
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 Databricks
- Generating and storing embeddings alongside the objects they describenot Databricks
Databricks
- Running Spark data engineering pipelines on managed clustersnot Weaviate
- Building a lakehouse over data in cloud object storagenot Weaviate
- Training and serving machine learning models alongside the datanot 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
Databricks
- Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- The free trial lasts 14 days
- Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
- Azure Databricks pricing is set by Microsoft rather than by Databricks
- Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate
Pricing, plan by plan
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
Databricks
Free- Community EditionFree
- Limited cluster
- Notebook environment
- Community support
- Standard$0.07/DBU
- Jobs compute
- SQL compute
- Standard 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 Databricks if
- You need delta lake.
- You want to start without paying.
- You work on Web, Aws, Azure, Gcp.
- You also want apache spark.
Questions people ask
- Is Weaviate or Databricks better?
- Neither clearly leads. Weaviate starts at Free and Databricks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Weaviate or Databricks?
- Weaviate starts at Free and Databricks at Free.
- Does Weaviate or Databricks run on more platforms?
- Weaviate runs on Linux, Mac, Windows, Web. Databricks runs on Web, Aws, Azure, Gcp.
- 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 Databricks is typically brought in for.
- What can Weaviate do that Databricks cannot?
- Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. Both handle Web support.