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Machine Learning · head to head

Google Vertex AI vs Zilliz

Google Vertex AI logo

Google Vertex AI

Machine Learning

Unified ML platform to build, deploy, and scale AI models

From
On request
Rated
-
Zilliz logo

Zilliz

Databases

Managed vector database and vector lakebase for AI applications

From
Free
Rated
-

The short version

  • Only Zilliz has a free tier, so it costs nothing to try first.
  • Each has a real cost: Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult; Zilliz pricing structure not publicly disclosed, requires sales contact
  • They diverge on capability: Google Vertex AI covers AutoML, Zilliz covers Vector indexing.

Where they differ

Only the attributes on which Google Vertex AI and Zilliz actually diverge.

Attributes where Google Vertex AI and Zilliz differ
AttributeGoogle Vertex AIZilliz
Starting priceOn requestFree
Pricing modelUnknowncontact-sales
Free tierNoYes
PlatformsCloud, WebCloud, Self-hosted
CategoryMachine LearningDatabases
Founded20082017

Identical on both: user rating (Not yet rated).

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 Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Model monitoring
  • Prediction serving
  • BigQuery
  • Cloud Storage
  • TensorFlow

Only in Zilliz

  • Vector indexing
  • Distributed architecture
  • SQL interface
  • Tensor support
  • Real-time search
  • Cloud-native
  • Open-source compatible

What people use each for

The jobs each tool is most often brought in to do.

Google Vertex AI

  • Machine learningnot Zilliz
  • Data analysisnot Zilliz
  • Model trainingnot Zilliz
  • Predictive analyticsnot Zilliz

Zilliz

  • Build retrieval-augmented generation (RAG) systemsnot Google Vertex AI
  • Implement semantic search over documentsnot Google Vertex AI
  • Create multimodal search with text and imagesnot Google Vertex AI
  • Power recommendation engines with vector similaritynot Google Vertex AI
  • Enable similarity search on user embeddingsnot Google Vertex AI

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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

Zilliz

  • Pricing structure not publicly disclosed, requires sales contact
  • Operational complexity for self-hosted Milvus deployments
  • Learning curve for those unfamiliar with vector databases
  • Limited built-in analytics compared to some alternatives

Pricing, plan by plan

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI review.

Zilliz

Free

No published plan breakdown. See the Zilliz review.

Which should you pick?

Choose Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.

Choose Zilliz if

  • You need vector indexing.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want distributed architecture.

Questions people ask

Is Google Vertex AI or Zilliz better?
Neither clearly leads. Google Vertex AI starts at On request and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Google Vertex AI or Zilliz?
Zilliz has a free tier; the other does not. Paid plans start at On request for Google Vertex AI and Free for Zilliz.
Does Google Vertex AI or Zilliz run on more platforms?
Google Vertex AI runs on Cloud, Web. Zilliz runs on Cloud, Self-hosted.
Can I use Zilliz for free?
Yes. Zilliz has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is Google Vertex AI best used for?
Google Vertex AI is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Zilliz is typically brought in for.
What can Google Vertex AI do that Zilliz cannot?
Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor 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.

Source
Zilliz: What is the difference between Milvus and Zilliz Cloud?

Milvus is the open-source vector database that you can self-host. Zilliz Cloud is the fully managed service built on Milvus that removes operational overhead and handles scaling automatically.

Source
Google 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.

Source
Zilliz: How many vectors can Zilliz handle?

Milvus and Zilliz Cloud can store and search billions of vectors through their distributed architecture that separates storage and compute layers.

Source
Google 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.

Source
Zilliz: Is Milvus open-source?

Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.

Source
Google 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
Zilliz: What pricing does Zilliz Cloud offer?

Zilliz Cloud pricing is not publicly listed and requires contacting their team to discuss your specific scale and use case requirements.

Source
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