Machine Learning & Data Science · head to head
Weaviate vs Google Vertex AI
Google Vertex AI
Machine Learning & Data Science
Unified ML platform to build, deploy, and scale AI models
- From
- On request
- Rated
- -
The short version
- Only Weaviate has a free tier, so it costs nothing to try first.
- Each has a real cost: Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- They diverge on capability: Weaviate covers Vector and keyword search, Google Vertex AI covers AutoML.
Where they differ
Only the attributes on which Weaviate and Google Vertex AI actually diverge.
| Attribute | Weaviate | Google Vertex AI |
|---|---|---|
| Starting price | Free | On request |
| Pricing model | freemium | Unknown |
| Free tier | Yes | No |
| Platforms | Linux, Mac, Windows, Web | Cloud, Web |
| Founded | 2019 | 2008 |
Identical on both: 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 Google Vertex AI
- AutoML
- Custom training
- Feature Store
- Model monitoring
- Prediction serving
- BigQuery
- Cloud Storage
- TensorFlow
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 Google Vertex AI
- Generating and storing embeddings alongside the objects they describenot Google Vertex AI
Google Vertex AI
- Machine learningnot Weaviate
- Data analysisnot Weaviate
- Model trainingnot Weaviate
- Predictive analyticsnot 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
Google Vertex AI
- Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- Requires familiarity with Google Cloud Platform infrastructure and concepts
- Cost can escalate quickly with large training and inference workloads
Pricing, plan by plan
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
Google Vertex AI
On requestNo published plan breakdown. See the Google Vertex AI review.
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 Google Vertex AI if
- You need automl.
- You work on Cloud, Web.
- You also want custom training.
Questions people ask
- Is Weaviate or Google Vertex AI better?
- Neither clearly leads. Weaviate starts at Free and Google Vertex AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Weaviate or Google Vertex AI?
- Weaviate has a free tier; the other does not. Paid plans start at Free for Weaviate and On request for Google Vertex AI.
- Does Weaviate or Google Vertex AI run on more platforms?
- Weaviate runs on Linux, Mac, Windows, Web. Google Vertex AI runs on Cloud, Web.
- Can I use Weaviate for free?
- Yes. Weaviate has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
- 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 Google Vertex AI is typically brought in for.
- What can Weaviate do that Google Vertex AI cannot?
- Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Both handle Web support.
Answered from the vendors’ own pages
Google Vertex AI: What is the pricing model for Google Vertex AI?
Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.
SourceGoogle Vertex AI: What types of data can Vertex AI handle?
Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.
SourceGoogle Vertex AI: Does Vertex AI support custom model training?
Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.
SourceGoogle Vertex AI: What deployment options are available in Vertex AI?
Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.
Source