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Databases · head to head

Redpanda vs Zilliz

Redpanda logo

Redpanda

Databases

Kafka-compatible streaming platform with no ZooKeeper or JVM

From
Free
Rated
-
Full reviewWebsite
Zilliz logo

Zilliz

Databases

Managed vector database and vector lakebase for AI applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Redpanda the community edition is source-available rather than OSI open source, which matters for some procurement; Zilliz pricing structure not publicly disclosed, requires sales contact
  • They diverge on capability: Redpanda covers Kafka API compatible, Zilliz covers Vector indexing.
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Redpanda and Zilliz actually diverge.

Attributes where Redpanda and Zilliz differ
AttributeRedpandaZilliz
Pricing modelSource-available community edition with paid enterprise and cloud tierscontact-sales
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, Self-hosted
FoundedUnknown2017

Identical on both: starting price (Free), free tier (Yes), 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 Redpanda

  • Kafka API compatible
  • No JVM or ZooKeeper
  • Thread-per-core
  • Built-in HTTP proxy and schema registry

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.

Redpanda

  • Kafka workloads where the operational cost of running Kafka is the blockernot Zilliz
  • Latency-sensitive streaming where tail latency mattersnot Zilliz
  • Smaller teams wanting streaming without a dedicated platform groupnot Zilliz

Zilliz

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

Where each one falls short

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

Redpanda

  • The community edition is source-available rather than OSI open source, which matters for some procurement
  • Kafka API compatibility is high but not total, and deep ecosystem tools can hit gaps
  • Smaller community than Kafka, so fewer people have solved your problem before
  • Some operational and tiered-storage features are enterprise-only

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

Redpanda

Free
  • CommunityFree
    • Kafka-compatible broker
    • Single binary
    • Community support

Zilliz

Free

No published plan breakdown. See the Zilliz review.

Which should you pick?

Choose Redpanda if

  • You need kafka api compatible.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want no jvm or zookeeper.
Redpanda review

Choose Zilliz if

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

Questions people ask

Is Redpanda or Zilliz better?
Neither clearly leads. Redpanda starts at Free and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Redpanda or Zilliz?
Redpanda starts at Free and Zilliz at Free.
Does Redpanda or Zilliz run on more platforms?
Redpanda runs on Linux, Docker, Kubernetes, Self-hosted. Zilliz runs on Cloud, Self-hosted.
Can I use Redpanda for free?
Both have a free tier, so you can try either at no cost before committing.
What is Redpanda best used for?
Redpanda is most often used for kafka workloads where the operational cost of running kafka is the blocker, latency-sensitive streaming where tail latency matters, smaller teams wanting streaming without a dedicated platform group. Of those, kafka workloads where the operational cost of running kafka is the blocker and latency-sensitive streaming where tail latency matters are not what Zilliz is typically brought in for.
What can Redpanda do that Zilliz cannot?
Redpanda covers Kafka API compatible, No JVM or ZooKeeper, Thread-per-core, Built-in HTTP proxy and schema registry. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.

Answered from the vendors’ own pages

Redpanda: Is Redpanda free?

A community edition is free and source-available. Enterprise features and Redpanda Cloud are paid, and the licence is not OSI open 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
Redpanda: Can I use my Kafka clients?

Yes. Redpanda implements the Kafka API, so existing clients and most tooling connect without changes.

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
Redpanda: Why remove ZooKeeper and the JVM?

Both are significant sources of Kafka’s operational burden — tuning, coordination and failure modes. Removing them is the core of Redpanda’s pitch.

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
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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