Databases · head to head
QuestDB vs Zilliz

QuestDB
Databases
Fast open source time-series database for high throughput ingestion
- From
- Free
- Rated
- -

Zilliz
Databases
Managed vector database and vector lakebase for AI applications
- From
- Free
- Rated
- -
The short version
- Each has a real cost: QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features; Zilliz pricing structure not publicly disclosed, requires sales contact
- They diverge on capability: QuestDB covers High Throughput Ingestion, Zilliz covers Vector indexing.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which QuestDB and Zilliz actually diverge.
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 QuestDB
- High Throughput Ingestion
- SQL Support
- Time-series Optimization
- SIMD Vectorization
- Column-oriented Storage
- Built-in Web Console
- InfluxDB Line Protocol
- PostgreSQL
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.
QuestDB
- Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot Zilliz
- Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot Zilliz
- Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not Zilliz
Zilliz
- Build retrieval-augmented generation (RAG) systemsnot QuestDB
- Implement semantic search over documentsnot QuestDB
- Create multimodal search with text and imagesnot QuestDB
- Power recommendation engines with vector similaritynot QuestDB
- Enable similarity search on user embeddingsnot QuestDB
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
QuestDB
- Open-source edition lacks high-availability, distributed architecture, and enterprise security features
- Enterprise edition pricing not published; requires contacting sales for custom quote
- Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements
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
QuestDB
FreeNo published plan breakdown. See the QuestDB review.
Zilliz
FreeNo published plan breakdown. See the Zilliz review.
Which should you pick?
Choose QuestDB if
- You need high throughput ingestion.
- You want to start without paying.
- You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- You also want sql support.
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 QuestDB or Zilliz better?
- Neither clearly leads. QuestDB 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, QuestDB or Zilliz?
- QuestDB starts at Free and Zilliz at Free.
- Does QuestDB or Zilliz run on more platforms?
- QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP). Zilliz runs on Cloud, Self-hosted.
- Can I use QuestDB for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is QuestDB best used for?
- QuestDB is most often used for time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds, real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics, applications requiring multi-tier storage (hot ingest, real-time sql, cold parquet archive). Of those, time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds and real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics are not what Zilliz is typically brought in for.
- What can QuestDB do that Zilliz cannot?
- QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.
Answered from the vendors’ own pages
QuestDB: How much does QuestDB Enterprise cost?
QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.
SourceZilliz: 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.
SourceQuestDB: Does QuestDB offer a free version?
Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.
SourceZilliz: 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.
SourceQuestDB: What deployment options does QuestDB offer?
QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.
SourceZilliz: Is Milvus open-source?
Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.
SourceZilliz: 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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