Databases · head to head
Privacera vs Zilliz
Privacera
Databases
Centralised data access governance from the creators of Apache Ranger, now rebranding as Trust3 AI
- From
- On request
- Rated
- -
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: Privacera the company is mid-rebrand to Trust3 AI as of March 2026, so documentation, contracts and support channels are in transition and buyers should confirm which entity and which product name their agreement actually names.; Zilliz pricing structure not publicly disclosed, requires sales contact
- They diverge on capability: Privacera covers Centralised policy authoring, Zilliz covers Vector indexing.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Privacera and Zilliz actually diverge.
Identical on both: user rating (Not yet rated), category (Databases).
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 Privacera
- Centralised policy authoring
- Native enforcement
- Attribute-based access control
- Dynamic masking and row filtering
- Sensitive data discovery
- Encryption and de-identification
- Audit reporting
- AI governance agent
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.
Privacera
- An enterprise running both Databricks and Snowflake that needs one masking policy honoured identically in both rather than two sets of grants to reconcilenot Zilliz
- A bank that must produce a single access audit across its analytics estate for a regulator without stitching together per-engine logsnot Zilliz
- A Hadoop shop with years of Apache Ranger policies migrating to cloud analytics and wanting to carry the policy model across rather than rewrite itnot Zilliz
- A team exposing governed data to LLM applications that needs the same row and column restrictions to apply when an agent queries on a user behalfnot Zilliz
Zilliz
- Build retrieval-augmented generation (RAG) systemsnot Privacera
- Implement semantic search over documentsnot Privacera
- Create multimodal search with text and imagesnot Privacera
- Power recommendation engines with vector similaritynot Privacera
- Enable similarity search on user embeddingsnot Privacera
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Privacera
- The company is mid-rebrand to Trust3 AI as of March 2026, so documentation, contracts and support channels are in transition and buyers should confirm which entity and which product name their agreement actually names.
- Investment is visibly shifting towards agentic AI governance, which raises a fair question about how much engineering continues to go into the classic data access governance modules that most existing customers actually bought.
- Native enforcement depends on each engine supporting the policy constructs you need, so what you can express on Databricks may not be enforceable identically on a less capable source, and coverage must be verified source by source.
- It sits between the data platforms and their own governance features, and as Databricks Unity Catalog and Snowflake native governance mature, single-platform customers find the case for a separate layer weakening.
- Pricing is unpublished and scales with connected sources, so an organisation that keeps adding data platforms discovers the governance layer cost grows alongside the platform costs it was meant to rationalise.
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
Privacera
On request- Privacera Platform$undefined/year
- Quoted by connected data sources, users and deployment model
- Self-managed and Privacera Cloud SaaS options
- Free trial available for Privacera Cloud and Trust3 AI
Zilliz
FreeNo published plan breakdown. See the Zilliz review.
Which should you pick?
Choose Privacera if
- You need centralised policy authoring.
- You work on Web, Linux.
- You also want native enforcement.
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 Privacera or Zilliz better?
- Neither clearly leads. Privacera 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, Privacera or Zilliz?
- Zilliz has a free tier; the other does not. Paid plans start at On request for Privacera and Free for Zilliz.
- Does Privacera or Zilliz run on more platforms?
- Privacera runs on Web, Linux. 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. Privacera starts at On request.
- What is Privacera best used for?
- Privacera is most often used for an enterprise running both databricks and snowflake that needs one masking policy honoured identically in both rather than two sets of grants to reconcile, a bank that must produce a single access audit across its analytics estate for a regulator without stitching together per-engine logs, a hadoop shop with years of apache ranger policies migrating to cloud analytics and wanting to carry the policy model across rather than rewrite it, a team exposing governed data to llm applications that needs the same row and column restrictions to apply when an agent queries on a user behalf. Of those, an enterprise running both databricks and snowflake that needs one masking policy honoured identically in both rather than two sets of grants to reconcile and a bank that must produce a single access audit across its analytics estate for a regulator without stitching together per-engine logs are not what Zilliz is typically brought in for.
- What can Privacera do that Zilliz cannot?
- Privacera covers Centralised policy authoring, Native enforcement, Attribute-based access control, Dynamic masking and row filtering. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.
Answered from the vendors’ own pages
Privacera: Did Privacera merge with Immuta?
No. They remain independent competitors, and Privacera still publishes comparison material against Immuta. What did happen is a rebrand to Trust3 AI announced in March 2026.
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.
SourcePrivacera: Is it the same as Apache Ranger?
It is built on Ranger by Ranger creators, but it adds multi-engine enforcement, discovery, a managed cloud option and support. Ranger alone does not cover Snowflake or cloud storage in the same way.
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.
SourcePrivacera: Does it slow down queries?
It pushes policy into the underlying engine rather than proxying, so query execution stays native. Policy synchronisation, not query latency, is the usual operational concern.
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.
SourcePrivacera: What does it cost?
Not published. Quoted by connected sources, user count and whether you self-manage or use Privacera Cloud. A free trial of the cloud product is available.
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.
SourceRelated pages
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